Ecological Economics 70 (2011) 1672–1679
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Ecological Economics
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n
Analysis
Factors determining awareness and knowledge of aquatic invasive species
Mark E. Eiswerth a,⁎, Steven T. Yen b, G. Cornelis van Kooten c
a
b
c
Department of Economics, University of Northern Colorado, Greeley, CO 80639-0046, USA
Department of Agricultural and Resource Economics, The University of Tennessee, Knoxville, TN 37996-4518, USA
Department of Economics, University of Victoria, Victoria, Canada BC V8W 2Y2
a r t i c l e
i n f o
Article history:
Received 11 June 2010
Received in revised form 15 April 2011
Accepted 16 April 2011
Keywords:
Invasive species
Nonnative species
Awareness
Perception
Trivariate ordered probability model
a b s t r a c t
Public perceptions of invasive species may influence policies and programs initiated by public and private
stakeholders. We investigate the determinants of the public's awareness and knowledge of invasive species as
few studies have examined this relationship. We focus on aquatic invasive species (AIS) and employ survey
data from property owners in a lake district. A major contribution is that we estimate a mixed trivariate
binary-ordered probit regression model that accommodates correlations among unobserved characteristics,
produces statistically more efficient estimates, and allows a more proper investigation of the probability of
knowledge conditional on awareness. Our results provide insights for invasive species education and
management programs. We find that individuals are more likely to be aware of AIS if they participate in
water-based recreation, visit lakes outside their area, have a boat, belong to a lake association, or are college
educated. This has a policy implication: Given high levels of AIS awareness by those most involved in activities
around lakes and those with a higher education, it may be beneficial to target informational campaigns at
those who do not display these characteristics, so that they can better make informed decisions about whether
to support and expend money on invasive species management programs.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Environmental damages and losses caused by approximately
50,000 non-indigenous species in the United States are estimated to
exceed more than $136 billion per year (Pimentel et al., 2000). While
the true damage figure can never be known with precision and
remains a source of debate, there is broad consensus that aggregate
damage impacts across species are substantial. In targeted studies,
researchers have estimated, for example, the economic losses from
invasive species in forests (Krcmar, 2007), leafy spurge (Euphorbia
esula) (Leitch et al., 1996), various species of knapweed (Centaurea
diffusa Lam., C. maculosa Lam., and Acroptilon repens L.) (Hirsch and
Leitch, 1996), and yellow starthistle (Centaurea solstitialis) (Eagle et al.,
2007). Others have developed models to identify economically
optimal approaches for the prevention and control of invasive species
(e.g., Eiswerth and Johnson, 2002; Eiswerth and van Kooten, 2007;
Jones and Medd, 2000; Shogren, 2000; Wilkerson et al., 2002).
In some states, public agencies and Cooperative Extension staff
spend time and money to promote public awareness and knowledge
of invasive species. Presumably, policy makers think that better public
awareness helps prevent or slow the spread of such species.
Awareness and knowledge are believed to affect the characteristics
of multiple anthropogenic vectors of invasion including the move-
⁎ Corresponding author. Tel.: + 1 970 351 2094.
E-mail address: [email protected] (M.E. Eiswerth).
0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolecon.2011.04.012
ments of anglers, hikers and cars, and, of particular importance for
aquatic invasive species (AIS), transport of boats (Howard-Williams,
1993). For example, if boat owners are more aware of AIS and
knowledgeable regarding their adverse impacts, then they are more
likely to use caution in inter-lake transport and expend greater effort
to wash their boots, boats, propellers and trailers. In addition, public
awareness and knowledge are key factors determining the level of
public support for actual or potential programs to control/manage
nonnative invasive species. However, there currently is a gap in our
understanding of human behavior as almost no research to date has
investigated what kinds of factors determine individuals' levels of
knowledge and awareness of nonnative invasive species. Findings on
awareness and understanding, and how these are related to individual
characteristics, can inform deliberations about invasive species
education and management programs.
To fill the gap in our understanding of human behavior regarding
invasive species, we econometrically determine factors that contribute to public awareness and knowledge of nonnative invasive species.
Our data come from a survey administered to approximately 1500
property owners in a lake-rich region of Bayfield County in northern
Wisconsin.1 The survey questions address a range of lakes-related
topics, including AIS. We use econometric analyses to explore
whether property owners in the study region recognize the existence
of specific invasive species (e.g., whether they can even name an
1
We target property owners in the lakes region because they are most likely to be
directly impacted by the adverse effects of aquatic invasive species.
M.E. Eiswerth et al. / Ecological Economics 70 (2011) 1672–1679
invasive species). We then examine people's knowledge regarding the
behavior and impacts of AIS. For example, do individuals know that
AIS can interfere with water-based recreational activities such as
swimming, fishing and boating? Do they know that AIS can easily be
transported from one lake to the next? To examine these issues, we
use a mixed trivariate binary-ordered probability (probit) model that
is conducive to this sort of inquiry.
In the next section, we provide some background on AIS and a brief
summary of previous literature on public awareness and knowledge
regarding invasive species. This is followed by a description of the
econometric model and our data. Then, in Section 4, we present and
discuss our empirical results. The final section concludes.
2. Background
2.1. Aquatic Invasive Weeds
There is an extensive literature describing the environmental
impacts of aquatic invasive weeds, including Eurasian watermilfoil
(Myriophyllum spicatum L.), which is the AIS of greatest prevalence in
northern Wisconsin. Eurasian watermilfoil (hereafter milfoil) and
other aquatic invasive weeds can lead to a substantial reduction in the
numbers and cover of native plant species. Because of its higher
efficiency in nutrient uptake and photosynthesis (Grace and Wetzel,
1978), milfoil's large canopy reduces the sunlight available to native
plants (Madsen et al., 1991). Aquatic invasive weeds have been
associated with declines in native plant species richness (which
relates to the numbers of species in a particular area) and abundance
(which relates to population size of species) at several locations
around the United States. Examples cited in the literature include the
apparent displacement of native aquatic plant species in Wisconsin
and Michigan (Nichols, 1994), Vermont and Massachusetts (Sheldon,
1994), the Mobile River Delta of Alabama (Bates and Smith, 1994),
and New York's Lake George (Madsen et al., 1991).
Aquatic invasive weeds also have adverse impacts on fish and
other aquatic animals that depend on the health of aquatic
ecosystems. Because aquatic invasive weeds alter the composition
of native aquatic plant communities, they can adversely impact
animals that depend on those communities (Madsen, 1997). Negative
impacts can be relatively large for sport fish species such as
largemouth bass (Micropterus salmoides) via decreased predation
success (Engel, 1987), and salmonids (Salmonidae spp.) via reduced
spawning success (Newroth, 1985). Additionally, aquatic invasive
weeds can increase the prevalence of other undesirable animal
species, such as mosquitoes (Ades spp., Anopheles spp., Culex spp.,
Culiseta spp.) that may spread diseases to humans (Bates et al., 1985;
Gallagher and Haller, 1990).
Under favorable conditions, aquatic invasive weeds can spread
rapidly following their introduction at a single site. For example, in
Washington State milfoil was first found in Lake Meridian near
Seattle in 1965; by the mid 1970s it had spread to Lake Washington
in Seattle. It then spread into British Columbia and central Washington, and clearly followed Interstate 5 to the south (Washington State
Department of Ecology, undated). Milfoil likely reached the eastern
United States in the 1940s, but was not discovered until 1965 in
Currituck Sound, North Carolina, when about 40 ha of the Sound
were found to be heavily infested and 200 to 400 ha lightly infested.
Within a year, 3200 ha had become heavily infested at this location,
while nine years later more than 32,000 ha were infested (Davis and
Brinson, 1983). The rapid spread of milfoil has also been documented at various other locations (Madsen et al., 1991; McKay et al.,
1997).
Boat and trailer traffic have clearly been significant vectors in the
spread of aquatic weeds. For example, Howard-Williams (1993)
found that for New Zealand, where nearly 20% of the aquatic and
wetland flora constitute invasive species, the “interlake movement of
1673
boats has been implicated almost exclusively in the transfer of aquatic
weeds.” Indeed, as a result many jurisdictions now have rules
regarding the cleaning of weeds from boats and propellers before
inter-lake transit.
In lakes-rich northern Wisconsin (our study area), aquatic invasive
weed species (notably, milfoil) are present and pose the types of
threats to the environment described above. They also have
potentially important impacts on human activities in our study region
because of the large degree of water-based recreation that occurs
there. Milfoil was first discovered in Wisconsin in 1962 and currently
is detected in 540 lakes or rivers in the state, including eight lakes in
our case study county of Bayfield (Wisconsin Department of Natural
Resources, 2010a). For comparison, curly-leaf pondweed (Potamogeton crispus), another important AIS in Wisconsin, is confirmed in 382
lakes/rivers in 62 counties, but has not been detected to date in any
lakes in Bayfield County (Wisconsin Department of Natural Resources,
2010a).
The most common means of controlling aquatic invasive weeds
such as milfoil are mechanical cutting and harvesting. A disadvantage
of this approach is that it is not selective, as it also removes desirable
aquatic plants. For this and other reasons, biological control is also
being considered. For example, twelve lakes in Wisconsin are
currently included in a Department of Natural Resources (DNR)
project to assess a native weevil's (Eurhychiopsis lecontei) effectiveness in selectively controlling Eurasian watermilfoil (Wisconsin
Department of Natural Resources, 2010b).2
2.2. Public Awareness, Knowledge, and Values for Nonnative Invasive
Species
There is a paucity of research examining determinants of public
awareness and knowledge of nonnative invasive species. Bremner and
Park (2007) used a sample of 248 individuals in Scotland to assess
public support for invasive species control and eradication programs.
They found that citizens were more willing to support such programs
if they had familiarity with the programs or were a member of a
conservation organization. Bremner and Park's focus was on explaining determinants of public support for control and eradication
programs, rather than on factors that underlie the public's awareness
of invasive species and knowledge regarding their impacts.
Garcia-Llorente et al. (2008) employed a survey of ecosystem
users, tourists and conservation professionals in southwestern Spain
to assess differences in human perceptions and knowledge of invasive
alien species, and willingness to pay for eradication. They applied
hierarchical cluster and principal component analyses to identify and
characterize different stakeholder groups, finding that perceptions
and knowledge of invasive species impacts varied across the groups
(e.g., conservation professionals and nature tourists were more
knowledgeable about invasive alien species than were general
tourists and local users). Though Garcia-Llorente et al. examined
public knowledge of invasive species, the sample frame and data
analysis methods used in their study were quite different from those
of the present study. Specifically, Garcia-Llorente et al. used cluster
and principal component analyses to categorize the characteristics of
quite diverse groups (locals, generalist tourists, nature tourists, and
two groups of conservation professionals). In contrast, our study uses
a system of three regression equations to identify and measure the
relative importance of causal factors of AIS awareness and knowledge
of one group (local residents). Garcia-Llorente et al. (2008, p. 2969)
2
One reviewer suggested that the DNR is weighing its options, which is sometimes
seen as a technical or, in this case, biological question; but it is also a political one. The
reviewer argues that the use of biological control can be perceived as ‘playing God,’
which is viewed by some as unacceptable, except perhaps when a problem is
perceived to be acute. Unfortunately, we have no information on what the DNR is
taking into account in its policy choice. A discussion concerning the appropriateness of
human intervention in the environment can be found in Nelson (2010).
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noted that “relatively little attention has been focused on public
attitudes toward invasive alien species, probably because of the
difficulty in measuring impacts, and the conflicts between different
stakeholders.”
Jetter and Paine (2004) estimated the public's willingness to pay
(WTP) for biological control of an invasive pest, the eucalyptus snout
beetle (Gonipterus scutellatus Gyllenhall) in urban landscapes in
California. Estimates of annual WTP varied from $23 for a chemical
pesticide option to $485 for the introduction of a natural enemy of the
pest (biological control). The study also found that respondents with a
greater level of concern for the environment or those who had
donated to environmental organizations were less likely to support a
program involving the spraying of pesticides, and more likely to
support biological control. While Jetter and Paine (2004) did not
examine the determinants of public awareness of invasive species, it
may be reasonable to postulate a positive correlation across individuals between their awareness of invasive pest issues and their
willingness to pay for invasive pest management.
We are not aware of studies that examine determinants of the
public's awareness or knowledge of aquatic invasive species specifically (as compared to all nonnative invasive species). Gates et al.
(2009) quantified angler movement patterns in southwestern
Montana, examined aquatic nuisance species awareness and angler
equipment cleaning practices, and measured the amount of soil
transported on anglers' boots and waders. They found that about half
of anglers ‘occasionally,’ ‘rarely’ or ‘never’ cleaned their boots and
waders between uses. This is indicative of low levels of awareness or
lack of concern, but factors contributing to variance in awareness or
concern were not specifically examined in this study.
The present study differs from previous research in three ways:
(i) we identify factors that influence the public's awareness and
knowledge of invasive species, using regression analysis to indicate
the relative impacts of different factors; (ii) we model public
awareness and knowledge jointly in a multi-equation regression
model; and (iii) we concentrate specifically on the awareness and
knowledge levels of residents who own property near the lake
ecosystem under consideration, because these individuals are likely
impacted the most by policies pertaining to the AIS.
3. Methods
Our empirical model consists of a system of three probit equations.
The first equation has a binary dependent variable that represents
awareness of AIS (denoted a), and is modeled as a binary probit:
ð1Þ
The second and third equations have ordinal dependent variables,
reflecting knowledge about the impacts of AIS on water-based
recreation (r) and on the belief about transferability of AIS from
lake to lake (t). These are modeled as ordered (rather than binary)
probit equations (j = r, t):
yj = 0 if xβj + εj ≤ 0
= 1 if 0 b xβj + εj ≤ μj
= 2 if xβj + εj N μj :
Prðya ¼ 1Þ ¼ Φ1 ðxβa Þ;
ð2Þ
In the above, x is a vector of explanatory variables; βa, βr and βt are
parameter vectors; μr and μt are threshold parameters defining the
ordinal categories; and the random error terms (εi, i = a, r, t) are
distributed as a trivariate normal distribution with zero means,
unitary variances and a correlation matrix containing unique
elements (ρar, ρat, ρrt). The three error variances are standardized at
ð3Þ
and the probabilities of being on the top knowledge categories
Prðyj ¼ 2Þ ¼ 1Φ1 μj −xβj ; j = r; t:
ð4Þ
Importantly, the dependent model also allows investigation of the
effects of explanatory variables on the probabilities of knowledge
(j = r, t), conditional on awareness:
Prðyj ¼ 2jya ¼ 1Þ ¼ Φ2 ðxβa ; −μ j + xβj ; ρaj Þ = Φ1 ðxβa Þ; j = r; t;
3.1. Econometric Specification
ya ¼ 1 if xβa + εa N 0
¼ 0 if xβa + εa ≤ 0:
unity because outcomes for the dependent variables are all categorical
(binary or ordinal) and, therefore, the parameter vectors βa, βr and βt
are identified only up to a scale. For purposes of interpreting results,
the parameter vectors βa, βr and βt reflect the marginal contributions
of the explanatory variables to the marginal propensities of awareness
(βa) and knowledge (βr and βt).
For the current application, specification of a dependent error
distribution is important for two reasons. First, there may be common
unobserved factors such as personal and household characteristics
that affect both awareness and knowledge.3 To take into consideration
correlations among the unobserved factors requires specification of an
error structure that accounts for such correlations. This results in
more efficient parameter estimates than procedures that do not
account for the correlations. Second, a dependent model allows a
proper investigation of the probability of any one outcome (e.g.,
knowledge about the impacts of AIS on water-based recreation)
conditional on another outcome (e.g., awareness about AIS), and vice
versa. Parameters βa, βr, βt, μr, μt, ρar, ρat and ρrt in the statistical
models (1) and (2) can be estimated by the method of maximum
likelihood. The likelihood function is a slight extension of that for the
trivariate probit model (Greene, 2008, pp. 817–822), and derivation is
available upon request.
The significance or importance of the error correlation can be
tested with usual statistical inference procedures such as the Wald
test, likelihood-ratio test, and Lagrange multiplier test for nested
hypotheses (Greene, 2008). Further, to explore the effects of
explanatory variables, we calculate the marginal effects of each
regressor on the probabilities characterizing the outcomes. Specifically, we examine the marginal probability for awareness
ð5Þ
where Φ1 and Φ2 are univariate and bivariate standard normal
cumulative distribution functions, respectively.
In this model the marginal effects of a continuous explanatory
variable can be obtained by differentiating the probabilities (3)
through (5) with respect to the non-discrete explanatory variables
(x). For discrete (binary) explanatory variables, we calculate the
discrete effects of each explanatory variable by simulating the effects
of a finite change (from 0 to 1), one variable at a time, on the above
probabilities, while using the observed values of all other explanatory
variables. The marginal and discrete effects of variables are calculated
for all sample observations and then averaged over the sample. For
statistical inference, standard errors of the discrete effects are
calculated by mathematical approximation, known as the delta
method (Greene, 2008).
The econometric specification provided above has not been
previously applied to the examination of awareness and knowledge
of invasive species. In the context of food safety concerns, Yen et al.
(2006) found that by including awareness in the regression equation,
3
One example of a set of unobserved factors would be a vector of attitudes
regarding the environment. Such attitudinal factors could be functions of a complex
mix of underlying characteristics such as the socio-cultural characteristics of the
community in which the respondent has lived; exposure to different media outlets
such as various newspapers and television channels; upbringing (e.g., the characteristics of the respondent's parents); and so on.
M.E. Eiswerth et al. / Ecological Economics 70 (2011) 1672–1679
estimation results were more robust. This was because they took
explicit account of ‘presupposition effects’ associated with those who
were and were not aware of pesticide and antibiotic residues in food
(Sterngold et al., 1994). Although the empirical issue is different from
that in the current study, Yen et al. (2006) point out the importance of
distinguishing between awareness and knowledge.
3.2. Data and Sample
Our data are from a mail-out survey of residential property owners in
the towns of Iron River and Delta located in Bayfield County, northern
Wisconsin. The survey constituted a detailed five-page, 22-question
instrument conducted by the University of Wisconsin-Extension
(UWEX) office in Bayfield County in cooperation with several other
extension collaborators from the university. The survey instrument was
developed by a five-person team of UWEX faculty from various
disciplines, including economics and the natural sciences.
In the first section of the survey, basic information on the property
(e.g., distance to nearest lake) and property owner (e.g., length of
ownership, days property is occupied each season) was collected. The
next section collected detailed information on the lake-based activities
in which respondents took part (fishing, boating, swimming, etc.), along
with frequencies of the activities. The survey then collected data on
lakes outside the respondents' own area that he or she might visit,
frequency of visitation and activities undertaken. A series of survey
sections then asked questions about attitudes and opinions regarding
various lake issues; awareness and knowledge of aquatic invasive
species; and questions regarding possible human reactions to potential
changes in the quality of the lakes. Finally, respondents were asked to
provide socioeconomic and demographic information, including annual
expenditures (by category) in the study area, household income,
education, age and preferences related to the study area.
As a precursor to the mail out and to increase response rates, a
short article previewing the survey was included in the local
newspaper. The survey was mailed to all 1451 residential property
owners in the two towns in June of 2007. Following the initial mail
out, the survey was re-mailed several weeks later along with a note to
potential respondents explaining the importance of their participation in the project. Of the 1451 surveys mailed out, 666 were
completed and returned for a total response rate of 48% after adjusting
for 49 undelivered surveys.
In designing the survey, ‘yes/no’ questions, such as “Do you know
what aquatic invasive species are?” or “Have you ever heard of
Eurasian watermilfoil?” were deemed to be undesirable as they could
introduce response bias and/or ‘yea-saying.’ Specifically, some respondents would have a tendency to answer ‘Yes’ even when the
truth is ‘No,’ so as not to appear ignorant or unaware. Therefore, since
invasive species are a real threat in the region, the survey asked the
following:
Can you think of the names of any aquatic invasive species that you
believe could be a problem for the lakes in the towns of Iron River and
Delta? ❑ Yes ❑ No
If yes, please list some: ________________________________________
In analyzing responses to the above, a respondent was classified as
‘aware’ of AIS only if he or she could list (name) at least one bona fide
aquatic invasive species (ya = 1); otherwise, the respondent was
classified as unaware (ya = 0). The primary advantages of this
measure are that it is not subject to response bias or yea-saying,
and, relative to other potential measures of awareness, it is
straightforward enough to determine in a survey format without
undue burden to the respondent.4
4
Clearly, awareness can be defined in other ways (e.g., ask to what extent the
person is aware on a 5-point Likert scale), but we feel that, given the current context,
this is a simple and unambiguous definition of awareness. If a person cannot identify
an AIS, it is highly unlikely they are aware of the problem.
1675
We are also interested in a respondent's knowledge of the impacts
and behavior of AIS, as quite distinct from simply possessing an
awareness that one or more such species exists. That is, we wished to
make a distinction between awareness and knowledge.5 Therefore,
questions were included in the survey to assess the level of knowledge
among respondents regarding the fact that AIS can adversely affect
water-based recreation and the fact that AIS are rather easily
transported across lakes. Specifically, respondents were confronted
with true statements and asked to express their agreement or
disagreement with them using a 5-point Likert scale that ran from
‘strongly disagree’ with the true statement to ‘strongly agree’ (i.e.,
strongly disagree, somewhat disagree, neutral, somewhat agree, and
strongly agree) plus a ‘don't know’ option. In the subsequent analysis,
however, we collapsed the 5-point scale to a 3-point scale, because
there were too few respondents who disagreed strongly with the
(true) knowledge statements.
The true statements “for lakes in general” were as follows:
a) “Aquatic invasive species can interfere with water-based recreation
like swimming, fishing, and boating”
b) “Aquatic invasive species are easily transferred from one lake to
another”
Responses to these questions were used to develop two variables:
(1) knowledge that AIS impacts recreation (yr) and (2) knowledge
that AIS are easily transferred across lakes (yt). Both of these
knowledge variables are ordinal.6
As described above, the survey also included questions that
provided information on the independent variables in the model
(Eqs. 1 and 2).7 The definitions and sample means of all the variables
used in the analysis are provided in Table 1. Income is a continuous
variable denoting annual household income for the respondent. Since
acquisition of information generally requires expenditures of time or
money, one would predict that higher levels of income would increase
the probability of awareness and better knowledge.
The binary variables ‘Keeps boat,’ ‘Use lakes,’ ‘Other lakes,’ and
‘Membership’ all indicate some aspect of a respondent's avidity for
water-based recreation and lake amenities. We hypothesize that an
affirmative response of each of these variables would increase
respondents' awareness and knowledge of AIS, other factors equal.
The binary variable ‘Year round’ indicates that a respondent is a 12month rather than seasonal resident at their property. We predict this
variable to have positive impacts on awareness and knowledge, since
year-round residents are more likely to be more familiar with local
and lakes issues. We include a suite of binary variables that indicate a
respondent's level of education. Our hypothesis is that, because
education allows an individual to process information more efficiently, higher levels of education would be associated with better
awareness and knowledge about AIS, ceteris paribus. Finally, we
include three binary variables to capture respondent age. While we
have no particular hypotheses regarding the impact of age on
awareness/knowledge, we include these variables to capture the
5
A respondent could well be aware that an aquatic invasive species exists, but still
disagree with, or respond with a ‘don't know’ to, these true statements that assess
knowledge regarding AIS impacts and transport. The respondent may simply not
consider the invasive species to be a problem, not know that it is a problem, or not
know that it is easily transferred across lakes.
6
It is possible that a respondent with concern for the environment (in general) or
negative perceptions of invasive species (specifically) may be more likely to answer in
agreement with the knowledge questions. Indeed, one would predict that people
concerned about the environment or invasive species would be more likely to have
better knowledge regarding their behavior and impacts. However, we are not able to
make fine distinctions regarding the precise causal factors behind respondents'
answers to these questions.
7
While Eqs. (1) and (2) were the basis for the empirical work, the choice of
independent variables (and survey design) was based on rather straightforward a
priori hypotheses (explained in the text) regarding factors predicted to influence
awareness and knowledge, rather than on a formal microtheory-based model.
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Table 1
Variable definitions and sample means.
Variable
Definition
Table 2
Frequency histograms of the knowledge variables.
Mean
Dependent variables
Awareness
Could think of the names of any AIS
0.48
(binary: no = 0, yes = 1)
1.65
Knowledge:
Ordinal variable indicating respondent's
AIS affect recreation
level of agreement with statement: “For
lakes in general: aquatic invasive species
can interfere with water-based recreation
like swimming, fishing, and boating.”
(0 = neutral/somewhat disagree/strongly
disagree, 1 = somewhat agree,
2 = strongly agree)
1.70
Knowledge:
Ordinal variable indicating respondent's
AIS easily transferrable level of agreement with statement: “For
lakes in general: aquatic invasive species
are easily transferred from one lake to
another.” (0 = neutral/somewhat disagree/
strongly disagree, 1 = somewhat agree,
2 = strongly agree)
Continuous explanatory variable
Income
Annual income: recoded as mid-point of
99,034.00
income categories
Binary explanatory variables (yes = 1, no = 0)
Keeps boat
Keeps a boat on a lake in town of Iron
0.64
River or Delta
Other lakes
Has gone to any lakes outside Iron River/
0.49
Delta area over the last three years
≤High school
Has only high school education or less
0.13
(reference)
0.28
Some college
Had some college or technical school,
including technical training in the armed
forces.
Completed college
Completed college
0.24
Post-graduate
Completed some graduate classes or higher
0.35
(has a post-graduate degree(s))
Age ≤ 44
Age is 44 or younger
0.13
Age 45–64
Age is 45–64 (reference)
0.57
Age ≥ 65
Age is 65 or older
0.30
0.88
Use lakes
Respondent or family members use lakes in
the towns of Iron River or Delta for fishing,
swimming/wading, or motor boating
Membership
Member of a lake association
0.38
Year round
Year-round resident at this property
0.35
Sample size
438
Knowledge
variable
Frequencies
Neutral/somewhat disagree/
strongly disagree
Somewhat
agree
Strongly
agree
Total
Knowledge 1:
AIS affect
recreation
Knowledge 2:
AIS easily
transferrable
38
(8.68%)
76
(17.35%)
324
(73.97%)
438
(100%)
34
(7.76%)
65
(14.84%)
339
(77.40%)
438
(100%)
4.1. Maximum-likelihood Estimates
Maximum-likelihood estimation is carried out by programming
the sample likelihood function in Gauss (Aptech, 1997). Our first
empirical task was to examine potential multicollinearity by calculating the variance inflation factors (VIFs) for all explanatory
variables. A value in excess of 20 is indicative of a multicollinearity
problem (Chatterjee and Hadi, 2006). All VIFs are very small, ranging
from 1.14 for Year round to 2.77 for Post-graduate. Since these are
much lower than the criterion for multicollinearity suggested in the
literature, we conclude that multicollinearity is not an issue for the
current analysis.
Table 3
Maximum-likelihood estimates of trivariate binary-ordered probit model of awareness
and knowledge.
Variable
Income/10000
Keeps boat
Other lakes
Some college
Completed college
Post-graduate
Age ≤ 44
potential nonlinear effects of age on the awareness and knowledge
probabilities. Further, age is typically used as a socio-demographic
variable and may reflect other individual characteristics that we do
not ascertain via our survey.
The final sample available for analysis comprised 438 observations,
which is lower than the 666 surveys that were returned. This is
because not all surveys exhibited complete responses for all of the
several dependent and independent variables used in the analysis.
Age ≥ 65
Use lakes
Membership
Year round
Constant
Threshold (μ)
4. Results
Simple descriptive statistics were performed to characterize the
respondents with regard to their values for the dependent variables.
The respondents were fairly evenly split between those who were
aware of an aquatic invasive species (about 48% of respondents) and
those who were not. In contrast, there was modest variation across
respondents for the two ordinal measures of knowledge. Specifically,
Table 2 shows that relatively few of the respondents disagreed with
the knowledge statements that were posed to them. We discuss the
implications of this feature of the data below as we turn to the
maximum-likelihood regression results and marginal effects of the
independent variables.
AIS
awareness
Knowledge: AIS Knowledge: AIS easily
affect recreation transferrable
0.005
(0.009)
0.335⁎⁎
(0.160)
0.459⁎⁎⁎
0.007
(0.010)
0.075
(0.163)
0.410⁎⁎⁎
(0.144)
(0.147)
0.228
− 0.252
(0.249)
(0.230)
0.746⁎⁎⁎ − 0.085
(0.256)
(0.252)
0.651⁎⁎⁎
0.028
(0.248)
(0.231)
− 0.096
− 0.321
(0.208)
(0.208)
− 0.290⁎
− 0.177
(0.168)
(0.175)
0.555⁎⁎
0.192
(0.252)
(0.212)
0.700⁎⁎⁎
0.433⁎⁎⁎
(0.160)
(0.163)
0.154
− 0.152
(0.155)
(0.148)
− 1.742⁎⁎⁎
1.048⁎⁎⁎
(0.313)
(0.300)
–
0.768⁎⁎⁎
(0.084)
Error correlations:
Knowledge: AIS affect
0.430⁎⁎⁎
recreation
(0.082)
Knowledge: AIS easily
0.409⁎⁎⁎
transferred
(0.087)
Log likelihood
− 746.137
Test for error
independence:
LR statistic (df=3)
179.601
Wald statistic (df=3)
481.187
LM statistic (df=3)
163.970
–
− 0.003
(0.010)
0.272
(0.168)
0.070
(0.148)
0.148
(0.230)
0.340
(0.243)
0.383⁎
(0.226)
− 0.095
(0.221)
− 0.121
(0.168)
− 0.127
(0.222)
0.323⁎⁎
(0.163)
− 0.044
(0.161)
1.106⁎⁎⁎
(0.301)
0.693⁎⁎⁎
(0.088)
–
0.804⁎⁎
(0.037)
–
–
–
–
–
–
–
–
–
Note: Asymptotic standard errors in parentheses.
⁎⁎⁎ Indicates level of significance at 1%.
⁎⁎ Indicates level of significance at 5%.
⁎ Indicates level of significance at 10%.
M.E. Eiswerth et al. / Ecological Economics 70 (2011) 1672–1679
The parameter estimates are provided in Table 3. Whereas the
parameter estimates in this table suggest qualitative effects (in terms
of signs and significance) of the explanatory variables on awareness
(or knowledge), these effects can be quantified by calculating the
marginal effects of these variables on the category probabilities,
discussion of which we defer to Section 4.2. Six of the eleven
explanatory variables (Keeps boat, Other lakes, Completed college,
Post-graduate, Use lakes, and Membership) are statistically significant
at the 5% level of significance or better in the awareness equation,
with an additional variable (Age ≥ 65) significant at the 10% level.
Fewer regressors are statistically significant in the two knowledge
equations, with two explanatory variables (Other lakes and Membership) significant at the 1% level in the ‘knowledge of recreation
impacts’ equation and two variables (Post-graduate and Membership)
significant at the 10% and 5% levels, respectively, in the ‘knowledge of
AIS transferability’ equation. The reason for the reduced numbers of
significant variables in these equations may be the result of the
relatively small proportions of ‘neutral’ and ‘disagree’ responses to the
dependent knowledge variables (see Table 2, which shows frequency
distributions for the regression sample). Modest variation across all
values of an independent ordinal variable typically may lead to sparse
statistical significance.
All three error correlations are positive and significant (two at the
1% level of significance and one at the 5% level). Joint significance of
these error correlations is confirmed by the Wald test (W = 481.19),
the likelihood-ratio test (LR = 179.60), and the Lagrange multiplier
test (LM = 163.97), all of which have three degrees of freedom and pvalue b 0.0001 (Greene, 2008). This joint significance justifies joint
estimation of the three equations to improve statistical efficiency.8
The estimates for the threshold parameter (μ) are significant at the 1%
level of significance for both knowledge equations, suggesting that
these parameters are successful in delineating categories in the
(ordinal) knowledge variables. A negative threshold parameter would
have suggested mis-specification of the model, and insignificant
threshold parameter estimates would have suggested consolidation of
the ordinal variable (into binary).
4.2. Marginal Effects of Explanatory Variables
Marginal effects of the explanatory variables on the marginal
probabilities of awareness and (unconditional) knowledge are
provided in Table 4.9 These marginal effects reflect the additional
contribution of each explanatory variable on (1) the probability of
awareness and (2) the unconditional (overall) probabilities of
knowledge at the ‘strongly agree’ level.
From the second column of the table, seven explanatory variables
(Keeps boat, Other lakes, Completed college, Post-graduate, Age ≥ 65,
Use lakes, and Membership) have statistically significant effects on
the marginal probability of AIS awareness. Four of these seven
statistically significant variables (Keeps boat, Other lakes, Use lakes,
Membership) reflect an individual's active use of lakes for recreation
and/or involvement in lake associations. Level of education and age
also influence awareness. The largest effects on the probability of
awareness are produced by Completed college and Membership, in
that order. For example, compared to individuals with a high school
8
The independent model results, not presented due to space consideration, indicate
relatively larger standard errors (and smaller t-values) for 38 of the 44 parameter
estimates. We also have estimated the models using completely binary specifications
for all of the dependent and independent variables and fewer regressors. While those
models yield somewhat different results, the final models presented are deemed
superior because they allow for more variation in several of the variables as suggested
by reviewers.
9
We have also computed the marginal effects of the explanatory variables on the
probabilities of knowledge conditional on awareness. Because most of these
conditional marginal effects are not statistically significant, we do not include them
in Table 4.
1677
Table 4
Marginal effects on marginal probabilities of awareness and knowledge.
Variable
Awareness = 1
Knowledge: AIS affect
recreation = 2
Knowledge: AIS easily
transferred = 2
Income/10000
0.002
(0.003)
0.108⁎⁎
(0.050)
0.147⁎⁎⁎
(0.044)
0.073
(0.080)
0.239⁎⁎⁎
(0.081)
0.209⁎⁎⁎
(0.078)
− 0.031
(0.067)
− 0.093⁎
0.002
(0.003)
0.022
(0.049)
0.122⁎⁎⁎
(0.043)
− 0.075
(0.068)
− 0.025
(0.075)
0.008
(0.069)
− 0.096
(0.062)
− 0.053
(0.052)
0.057
(0.063)
0.129⁎⁎⁎
(0.048)
− 0.045
(0.044)
− 0.001
(0.003)
0.078
(0.048)
0.020
(0.042)
0.042
(0.066)
0.098
(0.069)
0.110⁎
(0.064)
− 0.027
(0.063)
− 0.035
(0.048)
− 0.036
(0.064)
0.092⁎⁎
(0.047)
− 0.013
(0.046)
Keeps boat
Other lakes
Some college
Completed
college
Post-graduate
Age ≤ 44
Age ≥ 65
Use lakes
Membership
Year round
(0.053)
0.178⁎
(0.080)
0.225⁎⁎⁎
(0.047)
0.049
(0.049)
Note: Asymptotic standard errors in parentheses. Income is a continuous variable. All
other variables are binary.
⁎⁎⁎ Indicates level of significance at 1%.
⁎⁎ Indicates level of significance at 5%.
⁎ Indicates level of significance at 10%.
education or less, those who have completed college are 23.9% more
likely to be aware of AIS than other individuals. Similarly, compared to
non-members, members of a lake association are on average 22.5%
more likely to be aware of AIS.
For knowledge regarding AIS impacts on recreation (column 3,
Table 4), the marginal effects on unconditional knowledge (at the
‘strongly agree’ level) are statistically significant for Other lakes and
Membership.10 As examples of the correct interpretation of the
magnitudes of effects, note that members of a lake association are
12.9% more likely than non-members to know that AIS affects
recreation and respondents who use other lakes are 12.2% more
likely to be knowledgeable than those who do not. We do not show
the marginal effects on the probability of knowledge conditional
upon awareness for all explanatory variables in Table 4, but it is
worth noting that we do find a statistically significant effect of Other
lakes on conditional knowledge of recreation impacts. Interestingly,
however, the magnitude of the marginal effect on conditional
knowledge is somewhat smaller than the effect on unconditional
knowledge: respondents who use other lakes are only 6% more likely
to be knowledgeable conditional on their awareness of AIS. Such a
result indicates that, by conditioning knowledge to awareness – a
filtering process against presupposition bias in consumer recognition
(Sterngold et al., 1994) – the marginal effects on this conditional
probability provide more conservative (smaller) estimates of the
effects of this regressor on knowledge.
For knowledge regarding the ease of inter-lake transfer of AIS
(column 4, Table 4), the marginal effects on unconditional knowledge
(at the ‘strongly agree’ level) are statistically significant for Postgraduate and Membership. Members of a lake association are 9.2%
more likely than non-members to know that aquatic invasive species
are easily transferred from one lake to another, and respondents with
10
We focus on the probability of knowledge at the ‘strongly agree’ level because the
majority of the respondents (over 70% of the sample) are in this category for both
knowledge variables. One reviewer has noted that, given that there were relatively
few respondents who disagreed with the (true) knowledge statements, a priority for
future research in this area might be to develop measures of knowledge that yield
more detectable variations across individuals.
1678
M.E. Eiswerth et al. / Ecological Economics 70 (2011) 1672–1679
post-graduate education are 11% more likely to be knowledgeable of
this fact.
5. Discussion and Conclusions
In this study, we found that individuals were more likely to be
aware of aquatic invasive species if they were active participants in
water-based recreation, visited lakes outside their immediate area,
kept a boat on one of the area's lakes, were members of a lake
association, and/or were college-educated. The first three results are
indicative of what might be called recreational avidity; that is, those
most involved in lakes-based recreation are also the most likely to be
aware of the invasive species that threaten those ecosystems.
Membership in a lake association is important because it stands up
even after controlling for recreational avidity: lake association
members are more aware. We expect this to be the case because,
typically, such individuals are exposed to information (newsletters,
speakers, meetings, etc.) aimed at promoting AIS awareness, while
those individuals who do not belong to such organizations are missed
even though they might be a vector in spreading the invasive species.
Similarly, the finding that less-educated individuals tend to be less
aware suggests the importance of constructing public awareness
programs that are broad-based in nature. Once the level of awareness
is relatively high among sub-populations that are active in a relevant
environmental organization or are college educated, the comparative
benefit/cost metric of awareness campaigns targeted elsewhere may
increase.
Our results also point to conclusions regarding the public's
knowledge of the adverse recreational impacts and ease of interlake transfer of aquatic invasive species. Individuals are more likely to
possess good knowledge on AIS recreational impacts if they are
members of a lake association or visit other lakes outside the area.
Similarly, highly educated individuals and members of a lake
association tend to be more knowledgeable about the ease of transfer
of AIS from one lake to another. Again, it is not surprising that
association members possess better knowledge regarding AIS impacts
and ease of transport, since membership certainly increases the
probability of exposure of individuals to various forms of information
regarding the behavior and consequences of AIS.
Our results have implications for structuring invasive species
awareness, education and management efforts, and for analyzing the
factors determining knowledge and awareness. First, since the error
terms in our three equations are found to be correlated, it is important
to model public awareness and knowledge jointly. Failure to do so
may compromise statistical efficiency and lead to imprecise empirical
estimates of the impacts of individual characteristics on knowledge
and awareness.
Second, in constructing public awareness and education programs,
it may be necessary to consider the relative importance of maximizing
the market penetration of the desired message (i.e., casting a wide
marketing net to increase public support for AIS management) versus
focusing on specific groups involved in lake recreation or controlling
the rate of anthropogenic spread of the invasive species through
monitoring and management. This is because awareness and
knowledge tend to be lower among sub-populations that are less
active in outdoor recreation, suggesting a need to educate such
groups. At the same time, those same individuals also are less likely to
play a role spreading invasive species (e.g., via boat use and
transport). Therefore, it is important to consider carefully the
objective(s) of an education program when determining the primary
intended target(s) of the message, as well as the means and media to
be used. If slowing the rate of spread of the AIS by humans (e.g.,
through inter-lake boat transport) is the main objective, it may be
preferable to continue targeting groups that already have relatively
high rates of awareness.
If the primary objective of an awareness/education program is to
foster broad-based public support for government spending on
invasive species management options such as the application of
herbicides or biological control (rather than encourage public
behavior that will slow the rate of spread), it may be more important
to target sub-populations with currently low levels of knowledge and
awareness. Based on our results, this means educating individuals
who are not recreationally active, are not members of an environmental or natural resource stewardship organization, and are less
educated than other residents of the region. Identifying the relative
importance of alternative objectives may allow more efficient
allocations of limited public education funds.
Acknowledgements
We thank the University of Wisconsin Cooperative Extension for
their support of this research. Eiswerth was on a leave of absence from
the University of Wisconsin — Whitewater during the completion of a
portion of this work. We also thank Robert Korth, Tiffany Lyden, Julia
Solomon, and Tim Kane for their expert collaboration in survey
design, and are grateful to Candace Kolosso and Christie Kornhoff for
excellent assistance in survey implementation and data entry.
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