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Ecological Economics 104 (2014) 145–151

Contents lists available at ScienceDirect

Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Commentary

Determining the willingness to pay for ecosystem service restoration in a degraded coastal watershed: A ninth grade investigation Kristina Nicosia a,⁎, Suhrudh Daaram a, Ben Edelman a, Lev Gedrich a, Eric He a, Sarah McNeilly a, Vishnu Shenoy a, Akhil Velagapudi a, Walter Wu a, Luna Zhang a, Aneri Barvalia a, Veena Bokka a, Brian Chan a, Jennifer Chiu a, Sai Dhulipalla a, Victoria Hernandez a, Jenny Jeon a, Pranav Kanukollu a, Pearl Kravets a, Amrita Mantha a, Colin Miranda a, Vishan Nigam a, Meghnee Patel a, Sam Praveen a, Thomas Sang a, Shruti Upadhyay a, Tanvee Varma a, Camilla Xu a, Bhavish Yalamanchi a, Masha Zharova a, Allen Zheng a, Rashika Verma a, James Vasslides b, John Manderson d, Rebecca Jordan c, Steven Gray e,⁎ a

West-Windsor Plainsboro North High School, 90 Grovers Mill Road, Plainsboro, NJ 08536, United States Barnegat Bay Partnership, Ocean County College, PO Box 2001, Toms River, NJ 08754, United States Rutgers University, Department of Ecology, Evolution and Natural Resources, Program in Science Learning, 14 College Farm Road, New Brunswick, NJ 08901, United States d NOAA/NMFS/NEFSC, Ecosystems Processes Division, James J. Howard Marine Station, Highlands, NJ 07732, United States e University of Massachusetts, School for the Environment, 100 Morrissey Blvd., Boston, MA 02125, United States b c

a r t i c l e

i n f o

Article history: Received 22 September 2011 Received in revised form 13 December 2013 Accepted 11 February 2014 Available online 13 March 2014 Keywords: Ecosystem services Contingent valuation Coastal watersheds Environmental education Citizen science K–12

a b s t r a c t Over the course of a school year, a high school biology class and a local watershed partnership worked together to design a study to determine the willingness to pay for ecosystem service restoration in a local degraded watershed. With research control given to the teacher and her classroom as part of their in-class honors biology curriculum, the result was a student designed/written, and professionally structured, research manuscript. The aim of this collaboration was to: (1) integrate quantitative social science into the K–12 science curriculum to foster learning about the nature of social science investigation in a real world context; (2) create a community-based science partnership; and (3) generate social science data useful for decision-making that could withstand scientific peer review. In this commentary, we present the written product of the classrooms' work to illustrate the type of information that can be generated by a participatory science education program, along with a reflection from the students and project researchers about opportunities and barriers to conducting authentic social science research in K–12 classrooms. © 2014 Elsevier B.V. All rights reserved.

1. Foreword from Ms. Nicosia's 9th grade biology class Under the guidance of our biology teacher, a professor, and local environmental management organization our class conducted a scientific study. It was a first-hand exposure into what the world of science is like – something very different than what we had originally imagined. Soon after our class learned that we would be conducting a scientific study, we brainstormed a variety of questions about the environment that we thought we might like to address. Eventually, we narrowed it down to questions about the relationship between demographics, economics, and ecological services provided by the Barnegat Bay, which is a coastal area located near our school. As we put together our survey, we were faced with a series of problems: how to design the survey, how to word the questions, how to ⁎ Corresponding authors. E-mail addresses: [email protected] (K. Nicosia), [email protected] (S. Gray).

http://dx.doi.org/10.1016/j.ecolecon.2014.02.010 0921-8009/© 2014 Elsevier B.V. All rights reserved.

word the preface of the survey, and how to ensure that the recipients of our survey were a truly random sample of the population. Then, when we received the returned surveys, we faced another set of issues: how to deal with surveys that had only been partially answered, how to organize and analyze the data, and how to deal with the sample size which was smaller than we had expected. Designing and conducting our study was very difficult, but undoubtedly the hardest step of the process was writing the paper. For example, before we began writing we had to review and synthesize the existing literature about ecosystem valuation. The papers were sometimes too advanced for us to fully understand, but after reading so many, we were able to gain a general understanding. We assigned different students to write different sections of the paper, but the amount of effort each individual was willing to provide varied, essentially leading to a jumble of sections filled with pretentious scientific jargon that masked a lack of understanding on the part of many students. This may have been because of a lack of effort on the part of some, or the misguided efforts of students to make the paper conform to what they

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thought science (and scientific writing) was. Also, we had to learn how to tailor our paper to this journal, Ecological Economics, working to format it so that it fit the requirements. The problems we faced may also have stemmed from having such a large class working on one paper or just from struggling to understand the data. In addition, the statistical data analysis and models we used were beyond the grasp of most of our class, thus making discussions about the results more difficult. This also complicated our ability to draw conclusions based on our findings. It was not a simple process. Overall, the opportunity provided us with our first exposure to ‘real science’. Before this project, all the 'science' we had conducted was predetermined, with a correct outcome. To discover new scientific knowledge was far more challenging but also rewarding because we learned that science is a process, and not just results. Of course, we could only test problems in the scope of our budget, people were not as willing to participate in our survey as initially thought, and the effort put into creating the paper required significant time and resources beyond our normal class time. Ultimately, we learned how to format a paper, worked out the technical details, and learned about how science and scientific inquiry is applicable to the real world, all from a hands-on experience. We were given a lot of freedom, with just a little bit of guidance to help us through the process, so this study is truly something that our class created. The experience as a whole was eye-opening and intriguing, revealing to us all how science can be used to address problems, make policy decisions and better understand the interactions between the environment and society. Ben Edelman, Luna Zhang and Rashika Verma on behalf of Mrs. Nicosia's 9th Grade Biology Class (2010–2011), West-Windsor Plainsboro High School. 2. Introduction In this study, we evaluate a community's willingness to pay (WTP) for ecosystem service restoration in a highly urbanized and degraded watershed located in coastal New Jersey, the Barnegat Bay Watershed (BBW) (Kennish et al., 2007). To understand whether ecological restoration activities in the watershed are feasible, we wanted to measure the current level of public support for proposed restoration activities in terms of hypothetical monetary payments added to a monthly water bill and compare this amount to the cost of restoration currently estimated by the local watershed management organization, the Barnegat Bay Partnership (BBP). To address our research question, we used information collected through randomized surveys mailed to watershed residents to develop a Contingent Valuation (CV) model of WTP. We begin by briefly discussing our study site and reviewing the ecosystem services we chose to evaluate in the watershed then outline our method of ecosystem valuation. 2.1. Urbanized Coastal Watersheds and Ecosystem Services Barnegat Bay is a highly urbanized watershed located in Ocean County, New Jersey (BBP, 2002). Its habitat boundary is composed of three bays: Barnegat Bay, Manahawkin Bay, and Little Egg Harbor. The watershed includes wetlands (which takes up a quarter of the Barnegat Bay), barrier island–coastal dune scrubs and shrub complexes, submerged aquatic vegetation, and upland forests (BBP, 2002) which have all seen significant changes in land-cover and in land-use over the last 40 years (Fig. 1). Although the watershed provides many services, we had to narrow our field down to a few ecosystem benefits important to our study area. In consultation with the BBP the four ecosystem services we decided to evaluate were (1) water quality, (2) soil retention, (3) habitat provisioning, and (4) recreational use. We decided that these four were the most likely to affect those living in the Barnegat Bay and the Barnegat Bay's immediate future and we also found information about these services included in the Barnegat Bay's

management plan (see BBP, 2002). First, we reasoned that water quality was an important ecosystem service and decreased water quality affects not only the potable water supply for the human population but also reduces the ability of local fish populations to survive and reproduce (Carson and Mitchell, 1993). Second, soil retention was important because with increases in shoreline and riparian zones, the rate of natural toxin filtration increases (DeGroot et al., 2002) while chances of flooding and damage to infrastructure may decrease (Gregory et al., 1991). Third, habitat provisioning was seen as important since the conversion of unaltered lands to agriculture, urban, and suburban areas reduces the amount of suitable habitat available to native populations of plants and wildlife which leads to population fragmentation (BBP, 2002; Gregory et al., 1991; Heike et al., 2006). Finally, recreational use was considered given its importance to many coastal communities and since it is a major economic driver in the watershed community (BBP, 2002). The total amount of money needed for restoration of these services is estimated by the BBP at around 6.63 million dollars (BBP, 2002). 2.2. Ecosystem Valuation Models There are several methods used to value ecosystems and the public's WTP for restoration. WTP is a general construct associated with several methods that determine the amount of money people have spent or would theoretically spend in order to use, restore or improve an ecosystem service or natural resource. WTP can be determined by analyzing revealed preferences, such as measuring the amount paid by individuals to use a resource (e.g. Carr and Mendelsohn, 2003) or through stated preferences, which use direct or indirect surveys (e.g. Loomis et al., 2000). We used a stated preference survey method called the Contingent Valuation Method (CVM) which determines the value of an ecosystem by asking people how much they would be willing to pay in a hypothetical situation to restore or maintain specific ecosystem services (Follain and Emmanuel, 1985; Malpezzi, 2008). Demographic and other characteristics are used as independent variables to create a formula to predict how WTP varies by individual characteristics. CVM uses a simulated market where consumers are exposed to staged information and make decisions to observe their response to a new idea (Chee, 2004). 3. Materials and Methods 3.1. Survey Design and Distribution We designed a survey to test the hypothesis that a correlation exists between a person's WTP to restore ecosystem services and their personal and demographic characteristics. We included several questions that would help determine the reasons watershed residents might be more or less willing to pay money to support future restoration including age, marital status, distance of residence from the bay, frequency of use of the bay's resources, education, environmental awareness/attitudes and political affiliation. In total, the survey included 19 questions and was based on previous surveys applied in other WTP and ecosystem restoration studies (Loomis et al., 2000). To ensure all respondents had an equal opportunity to understand issues facing the bay, a short paragraph provided respondents with background information about the Barnegat Bay, a list of ecosystem services, the proposed policies, and the expected future state of the ecosystem services after restoration activities. This information was included on the first page of the mail-out survey and individuals were encouraged to read this information before answering any questions (Table 1). In order to provide an incentive to respond to the questionnaire, a photo of our class was included, along with an informational letter detailing the objectives of the study. A pilot survey was given to a group of 10 area residents prior to the survey being mailed. The pilot group was used to proofread the surveys for errors and provide feedback on the effectiveness and clarity of each question. After the focus group revisions were made to the survey, the survey was sent to 1000 randomly selected watershed residents. Only 19.7% of the recipients

K. Nicosia et al. / Ecological Economics 104 (2014) 145–151

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Fig. 1. Map of the study area: Barnegat Bay Watershed, New Jersey.

responded and only 15% of the 1000 surveys could be used as our net sample since the remaining 4.7% included incomplete surveys. The response rates are specified in Table 2. 3.2. Formatting of WTP Question Our study uses WTP as the dependent variable intended to estimate restoration support. The WTP question, as written on the survey, was stated in the following way: If the restoration fund was on the ballot in the next election and it would cost your household $“X” each month extra added to your water bill, would you vote in favor or against? I would vote Yes ____ I would vote No ____

Based on the pilot groups and literature review, the dollar amounts selected were as follows: $1, $2, $3, $5, $8, $10, $12, $20, $30, $40, $50, and $100. Each survey contained one randomly selected dollar amount which was evenly distributed over the 1000 surveys. Participants responded if they would be willing to pay the selected amount or not for the restoration projects listed (Table 3). 3.3. Statistical Model of WTP 3.3.1. Probability of WTP A logistic regression model best estimates the probability that a household will pay a given dollar amount because they respond with only a ‘yes’ or ‘no’ answer (Hanemann, 1984; Loomis et al., 2000). The model is: ProbðYesÞ ¼ 1=f1 þ exp½B0 þ B1ð$X Þ þ B2ðY Þ þ B3…g

in which $“X” represents the bid amount (in USD). This question was been designed using a method that enables a calculation of the average WTP by asking if one would be willing to pay for dollar amount “X”.

ð1Þ

in which each B is a coefficient estimated using a logistic regression. $X is the dollar amount the household was asked to pay. ‘Other variables’

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Table 1 Information provided in the survey on current state, proposed restoration for and restored state of ecosystem services. Ecosystem service

Current state

Proposed restoration

Restored statea

Water quality

Urbanization of the watershed has drastically decreased the supply and quality of water of the bay. These issues are related to storm water runoff, impairment of groundwater retention, leaking septic systems, and increasing amounts of fertilizer and other toxins entering the water systems.

Water quality is expected to improve significantly. Management of toxins entering the water will allow for maintenance and monitoring of these toxins at key points. Plant, vertebrate, and invertebrate populations should also improve significantly through adaptive management.

Soil retention

Increasing erosion brought on by rising development and shore-use has created an unstable shoreline in many coastal areas in the bay. Due to lack of vegetation, plants are unable to grow which, in turn, has created more erosion. Due to poor water quality and erosion, many ecosystems have been disturbed. Because of human development and urbanization in the bay, habitats in forests have been destroyed with human disturbance. Populations have decreased and there is loss of wildlife.

Watershed planners suggest the retrofitting of retention/detention storm water basins. These are expected to increase infiltration and recharge groundwater. Additionally, it is recommended that a comprehensive water supply plan is developed for the entire watershed that guides water supply development, use, and reuse, through the year 2040. To reduce erosion, planners suggest restoring native plants along public spaces, riparian zones and other waterways.

To maintain intact large blocks of pinelands habitat within state parks and forests and other publicly owned lands. Save fire-prone regions (in the uplands of the watershed) to maintain the natural fire cycle and allow for improved habitat. Coordinate and integrate management of federal lands for natural habitat values and watch over federal owned lands to ensure continued ecological integrity of the watershed. Protect and improve vegetated buffer zones adjacent to coastal wetlands to maintain continuous riparian corridors for low-impact recreational pursuits. Establishing riparian corridors allows for canoeing and other low-impact recreation.

Establishing habitat corridors, reserves, and fire management of pinelands will decrease the current system of fragmentation and maintain and restore ecological conditions to help support terrestrial and aquatic life.

Habitat provisioning

Recreational use

a

Over the last 20 years, recreational activities in the bay have increased at the cost of the bay's natural ecological structure. Some recreational fisheries have declined and there have been significant declines in water quality which have resulted in beach closings.

By increasing the amount of vegetation along the shoreline and riparian zones, the rate of soil erosion will decrease while habitat and the rate of natural toxin filtration will increase.

Promoting low-impact recreational use is expected to better maintain coastal wetlands which better protect against resource decline.

After 15–30 years.

may include responses to attitude questions and the respondent's demographic information (Loomis et al., 2000).

3.3.2. Calculating the Mean WTP The formula to calculate expected WTP based on given coefficients is: Mean WTP ¼ ð1=B1Þ  ln ½1 þ expðB0Þ

ð2Þ

in which B1 is the estimated coefficient of the bid amount and B0 is the grand constant, the sum of the estimated constant plus the product of the other independent values times their means (Hanemann, 1984).

3.4. Survey Administration A total of 1000 surveys were mailed out to residents of the Barnegat Bay watershed area using a random number generator created using Python, a free software program, attached to all addresses in the watershed. Each survey, including a cover page, basic background on each service to be restored, and the attached questionnaire, was three pages long. The survey was estimated to be completed in 10–15 min, so people would be more likely to send back their completed surveys.

Table 2 Survey response rates. Category

Number

Percentage

Surveys mailed Undeliverable No answer Net sample Complete sample Incomplete sample (not used to generate regression)

1000 128 675 197 150 47

100 12.8 67.5 19.7 15 4.7

Percentage of deliverable surveys

4. Results A statistical model including all environmental awareness/attitude and demographic variables was generated, and was shortened to only include independent variables significant at the .05 level. No awareness/attitude variables were relevant and the demographic variables such as education, marital status, distance from the Bay, and household size were found to be not significant and were not included in the final model. This stands in contrast to Loomis et al. (2000), which found attitude values to be a significant predictor of WTP for restoration. The final logistic model was: Log½ProbðYesÞ=Probð1−YesÞ ¼ ½B0 þ B1ðbidÞ þ B2ðageÞ þ B3ðsexÞ ð3Þ in which ‘yes’ is the dependent variable, defined as whether or not the respondent was willing to pay the bid amount. A 1 indicates a “yes” vote, while a 0 indicates a “no” vote. The bid amount is the amount of money the person was asked to pay in the form of an increased water bill. Age is specified in the age groups 18–25, 26–35, 36–45, 46–55, 56–65, and 65+ years. Sex is 0 if the respondent is female and 1 if the respondent is male (see Table 4). A negative coefficient shows that a higher bid amount correlates to a lower chance of the respondent agreeing to pay the bid amount for restoration of ecosystem services. The independent variable of age also has a negative coefficient. Younger respondents were significantly more willing to pay the bid amount except for the 36–45 years age group, which, along with older groups, were less willing to pay. For sex, the coefficient was negative, indicating that women were significantly more likely to pay to restore ecosystem services than men. Table 3 Responses at each bid amount.

77.4 22.6 17.2 5.4

$1

$2

$3

$5

$8

$10

$12

$20

$30

$40 $50

Yes 15 13 9 16 7 6 7 4 3 1 No 1 6 1 10 7 4 16 9 12 14 Percent Yes 94% 62% 90% 62% 50% 60% 30% 31% 20% 7%

$100

2 3 14 8 13% 27%

K. Nicosia et al. / Ecological Economics 104 (2014) 145–151 Table 4 Final statistical model. Variable

Coefficient

Constant Bid amount Age Sex

−.160 −1.430⁎⁎ −1.390⁎ −2.390⁎

⁎ Significant at the .05 level. ⁎⁎ Significant at the .01 level.

Survey results indicated that respondents were willing to pay, on average, $11.06 per month for restoration efforts. According to the BBP proposed policies (BBP, 2002), the complete cost for the restoration of the bay is estimated at $6.63 million in 2002, which is approximately $7.96 million in 2011 dollars, adjusted for inflation using the GDP deflator (NASA, 2011). Two estimates of total annual WTP for the Barnegat Bay were calculated based on averaged survey responses — an upper bound, using the mean WTP derived from Eq. (2) and assuming every household in Ocean County is willing to pay the $11.06 monthly fee estimated on survey responses, and a lower bound, assuming that houses that did not respond have a WTP of $0. The upper bound estimate totaled $29.29 million, which greatly exceeds the necessary funds for the restoration project. However, the lower bound of $6.62 million per year is $1.34 million less than the estimated cost of ecosystem restoration. Since the average WTP of the non-respondents is almost certainly greater than $0, the total WTP likely exceeds the estimated costs of restoration. Using this information, the BBP and the local decision-makers can promote the increase in water bill as well as implement the specified restoration plans (Table 5).

5. Discussion The monthly WTP of $11.06 is significantly lower than the WTP determined by other similar studies involving CVM surveys. For example, Loomis et al. (2000) found that the public was willing to pay $22 per month for the restoration of an impaired river basin. This may be because those surveys were conducted in person, putting pressure on interviewees to respond with higher WTP instead of a mail-out survey. Another possible reason for the lower WTP might be the current state of the economy, which was in recovery from a recession when the study took place. Additionally, the area where the Loomis et al. study took place may be more ecologically sensitive or experiencing more significant ecological degradation. The combination of the economic climate, differences in data collection, and study site could possibly cause recipients to provide different levels of willingness to spend extra money, especially for services which may not, in their opinion, directly affect them. Also, the Barnegat Bay has a large senior population, which might skew WTP downward because the logistic model showed a negative correlation between age and WTP. One of our hypotheses was that married couples would be less willing to pay compared to single residents because they feel that it is more important to save money for their family; therefore, they have a lower WTP. However, the relationship status of survey respondents has little impact on WTP, as both single and married people were similarly

Table 5 Annual WTP/households, which included those for upper and lower bounded estimates. Scenario

Monthly WTPa

Annual WTPa

Number of households

Annual (millions)

Apply mean to all households Apply mean to 22.6% of households

$11.06

$132.72

220,656

$29.29

$11.06

$132.72

49,868

$6.62

a

Per household.

149

willing to pay at different bid amounts. Another one of the initial hypotheses, people who affiliated themselves with the Democratic Party were more willing to pay for restoration than those who affiliated themselves with the Republican Party, was also not supported as our data indicated that political affiliations had little impact on WTP. Further, another hypothesis was that people living closer to the coast would be more aware of the ecosystems' damaged conditions and thus would be willing to pay more. However, the data collected show that people living off the coast were willing to pay as much as people living near the bay given similar bid amounts. Since distance from the coast showed no correlation to a given household's WTP, residents outside of Ocean County and even across New Jersey could potentially be willing to pay to restore the Barnegat Bay and other highly urbanized coastal watersheds. Environmental managers may extend restoration campaigns to other stakeholders beyond just local residents of coastal areas. The level of education of the people (options ranging from a high school degree to a PhD) showed no effect on WTP, and thus was not factored into the overall WTP. It is intriguing that this variable has no correlation to WTP since we assumed that different levels of education would manifest different beliefs in people and ultimately lead to differences in WTP. Also, the hypothesis which stated that ‘environmentally aware citizens would pay more than those who are not as environmentally involved’ was also rejected by the data because there was no correlation between environmental awareness and WTP. 6. Conclusion The mean monthly WTP of Ocean County residents to restore four ecosystem services of the Barnegat Bay Watershed (soil retention, water quality, habitat provisioning, and recreational services) was calculated to be $11.06 per household per month. This total WTP of Ocean County for these services is almost certainly higher than the estimated costs of the restoration. This justifies the restoration based on economic efficiency criteria and provides a possible source of funding for the initiative. Age was negatively correlated to WTP, while females had a higher WTP than males. Additional areas of study include conducting CVM studies in spatial areas beyond the ecosystem scheduled for restoration, since distance from Barnegat Bay was found to be unrelated with WTP. 7. Afterword from Program Researchers The goals of this collaboration were to: (1) integrate quantitative social science into the K–12 science curriculum to foster learning about the nature of social science investigation in a real world context; (2) create a community-based science partnership; and (3) generate social science data useful for decision-making that could withstand scientific peer review. By using a real-world problem, driven by the economic data needs of a local environmental NGO, this collaboration contributes to contemporary discussions about the integration of interdisciplinary environmental science in K–12 classrooms (e.g. sustainability science) as well as the benefits and limitations of participatory forms of science to support environmental decision-making (see Shirk et al., 2012). The research paper and the references included are entirely the product of the students under the guidance of their teacher and no changes to meaning were made by project scientists. The original paper written by the students was ultimately shortened to better reflect professional norms of scientific communication following double-blind reviews received from researchers who were unaware that the manuscript was authored by 9th grade (i.e. 14 and 15 year old) students. The original paper submitted for peer review by the teacher and her students can be found in its original form online as a report to the watershed partnership (see: http://bbp.ocean.edu/Reports/Willingness %20to%20Pay%20for%20Ecosystem%20Restoration.pdf). In terms of the educational impacts, learning assessments administered to students before and after their involvement in the project

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Table 6 ANOVA comparison of mean responses collected from students pre and post involvement in the program. Knowledge items (N = 24)

Science Ecology Visiting the outdoors Carbon cycling Human population growth Ecosystem function Water pollution Conducting scientific investigation Collecting and analyzing data Asking scientific questions Participating in an ecology project Sharing scientific information with others

Interest items (N = 26)

Pre mean (SD)

Post mean (SD)

df

F

p

Pre mean (SD)

Post mean (SD)

df

F

p

2.9 (.68) 2.0 (.55) 2.8 (.88) 1.5 (.73) 2.4 (.73) 2.5 (.80) 2.7 (.60) 2.6 (.76) 3.3 (.62) 2.8 (.74) 2.0 (8.6) 2.5 (.70)

3.3 (.60) 2.8 (.76) 2.6 (.93) 2.7 (.76) 2.7 (.85) 3.5 (.64) 3.1 (.53) 3.2 (.47) 3.2 (.66) 3.2 (.66) 3.6 (.63) 3.3 (.73)

1 1 1 1 1 1 1 1 1 1 1 1

4.85 21.32 1.87 43.29 4.02 30.50 10.18 12.69 .085 7.21 70.96 18.50

.031* .000* .175 .000* .049* .000* .002* .000* .772 .009* .000* .000*

3.5 (.59) 2.7 (.78) 3.0 (1.0) 2.3 (.95) 2.7 (75) 2.6 (1.0) 2.8 (.73) 3.0 (.78) 2.8 (.76) 2.8 (.80) 2.8 (.84) 2.8 (.73)

3.6 (.50) 2.5 (.90) 2.7 (.91) 2.3 (.74) 3.0 (.96) 3.0 (.84) 2.7 (1.0) 3.1 (.82) 2.9 (.80) 2.6 (80) 3.0 (.90) 3.0 (1.0)

1 1 1 1 1 1 1 1 1 1 1 1

.237 1.01 1.92 .002 1.69 1.70 .389 .697 .493 .215 .937 .992

.628 .317 .170 .960 .197 .535 .223 .485 .644 .337 .323 .544

Items were adapted from the Student Assessment of Learning Gains (SALG) questionnaire (Seymour et al., 2000). Responses measured on 5 point Likert scale for Knowledge (1 not at all knowledgeable–5 very knowledgeable) and Interest (1 not at all interested–5 very interested).

indicated that participation in the study significantly increased student knowledge (Table 6). Specifically students rated their knowledge higher at the conclusion of the project in general subject areas (e.g. science, ecology, human population growth, ecosystem functioning, water pollution) and in terms of scientific investigation skills (e.g. asking scientific questions, participating in an ecology project, and sharing information with others). Equally as important as self-reported learning were reports from the classroom teacher that students, while conducting the year-long study, did as well on standardized assessments as students from previous years. The notion that teachers should teach beyond their curriculum and embrace the uncertainties that are part of all real world scientific investigation is somewhat controversial because it is often presumed that students might suffer on standardized tests. Our data, however, do not support the argument that research collaboration and experimental scientific activities with uncertain outcomes have a negative impact on standardized student assessments. Although our program indicated that students increased their understanding of scientific content and scientific investigation, we did not find significant changes in terms of student interest in these same areas after their involvement in the program (Table 6). In fact, in some cases the mean scores for student responses regarding interest in ‘ecology’ and interest in ‘asking scientific questions’ actually decreased; however these changes were not significant. Additionally, students did not report significant learning increases in the area of ‘collecting and analyzing data’. When considered together, these findings indicate that engagement in real-world scientific investigations may have no, or even negative, impacts on student motivation to engage with scientific content and investigation possibly due to the considerable amount of work, focus, and prolonged engagement required when conducting scientific research. In addition to classroom-based impacts, this project also contributes to a growing literature about how science classrooms, and more generally citizens at large, might contribute to scientific research and environmental management. The term Public Participation in Scientific Research (PPSR) or citizen science is often used to describe collaborations like ours (Bonney et al., 2009). Citizen science has been heralded as a mechanism by which science can be pushed forward in an era of dwindling budgets (Shirk et al., 2012), democratized (Mueller et al., 2012), and made more relevant to local communities (Bonney et al., 2009). For our program, in addition to introducing environmental social science as a curricular tool, we also designed it as a means of generating locally relevant datasets intended to be useful for management institutions. Indeed, we feel that overall this goal was successful since a subset of students were asked to present their work at both local and federal environmental management meetings. Additionally, the classroom teacher has been asked to continue her collaboration with the local

watershed organization with new research questions that support management goals being selected by the class each year. It is important to note that several practical issues presented themselves during the project. These issues included barriers such as a lack of classroom access to the primary literature, and a general lack in some students' ability to communicate and critique scientific information (for a complete review of ‘lessons learned’ see Gray et al., 2012). Further, double-blind peer reviews of the original classroom manuscript were mixed and included criticisms regarding student writing, an absence of citations from the recent literature, problems with scientific novelty and one reviewer highlighted weaknesses in the study's design that could not be easily overcome (see comments from the first round of double-blind reviews in Attachment 1). These issues associated with limitations in the classroom's ability to contribute to scientific research should not be discounted or overlooked. Further, questions about the degree to which classrooms, and indeed many other citizen groups, can be expected to meaningfully contribute to defining scientific questions, analyzing scientific data, and interpreting and communicating scientific findings is an open and interesting area of future research. The barriers notwithstanding, our collaboration resulted in the establishment of a diverse learning community (see institutional affiliation of authors), increased community engagement with local environmental issues (see Moore, 2011) and increased student understanding and appreciation of the importance of environmental valuation in decision-making (see Verna, 2012). It clear that new research frameworks and novel educational programs can lead to positive outcomes that are concurrently useful for scientific training while contributing to applied environmental management. In conclusion, we suggest that scientist/teacher collaborations are not only an opportunity for students to engage in authentic scientific practice and contribute to local environmental issues, but also an opportunity to provide educators with tools to understand and teach about socio-scientific research which provides broader frameworks by which to tackle emerging environmental problems. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ecolecon.2014.02.010.

Acknowledgements This research was supported by teachers from West WindsorPlainsboro High School North who served as the focus group during the survey development, and by a panel of professors from Rutgers University, NOAA and the Barnegat Bay Watershed Partnership who provided feedback and reviews on our paper. Additional acknowledgments to the National Science Foundation for funding (Award 918589).

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