Embed Size (px)
Citation preview
Research Prioritization and the Potential Pitfall of PathDependencies in Coral Reef Science
Mark William Neff
Published online: 26 March 2014
� Springer Science+Business Media Dordrecht 2014
Abstract Studies of how scientists select research problems suggest the process
involves weighing a number of factors, including funding availability, likelihood of
success versus failure, and perceived publishability of likely results, among others.
In some fields, a strong personal interest in conducting science to bring about
particular social and environmental outcomes plays an important role. Conservation
biologists are frequently motivated by a desire that their research will contribute to
improved conservation outcomes, which introduces a pair of challenging questions
for managers of science and scholars of policies governing science: 1) How do
scientists integrate that goal into their processes of research priority evaluation, and
2) How can managers and funders of science utilize that knowledge in designing
and administering funding programs? I use Q method to uncover four distinct
schools of thought amongst researchers and knowledge-users about the merits of
possible research priorities for coral reefs; one of the axes along which these schools
of thought differ is in their interpretation of how science can and should interact
with policy. The results reveal that perceived severity of reef stressors plays a role
for some participants. Disciplinary training does not appear to be a major influence
on research priority evaluation, but individual participants indicated professional
expediency does prevent some researchers from pursuing or advocating that others
pursue topics outside of that disciplinary specialty. Influences on and processes in
research prioritization uncovered in this study have the potential to lead to counter-
productive disciplinary path dependencies. From these results and building on
outside literature, I conclude that better coordination and communication about
research priorities across disciplines and with broader stakeholders – including
Electronic supplementary material The online version of this article (doi:10.1007/s11024-014-9250-5)
contains supplementary material, which is available to authorized users.
M. W. Neff (&)
Department of Environmental Science, Allegheny College, 520 N Main St, Box E, Meadville,
PA 16335, USA
e-mail: [email protected]
123
Minerva (2014) 52:213–235
DOI 10.1007/s11024-014-9250-5
knowledge users – could improve the research enterprise’s ability to contribute to
meaningful societal and conservation goals. These findings are relevant to
researchers and research administrators across disciplines that seek to conduct or
fund science that is useful in addressing specific goals.
Keywords Research priorities � Problem choice � Research policy �Coral reefs � Conservation biology � Q method
Introduction
The question of how scientists select research problems, known as ‘problem choice’ in
the sociology of science literature, receives relatively little contemporary attention.
This is unfortunate, since problem choice within science is an under-recognized yet
critical step in environmental management and policy, as the research that is ultimately
conducted shapes the problems managers and politicians are aware of and affects the
policy and management interventions that those constituencies consider (cf. Hacking
1999; Neff and Corley 2009; Neff 2011). Once a scientist achieves initial success in a
novel area, subsequent researchers may be drawn into that research area (Zuckerman
1978; Ziman 1987; Kuhn 1996). Building additional inertia, once a given research
agenda is established it may feed into the concerns of and motivate a particular set of
stakeholders. These stakeholders then may influence science research agendas directly
by advocating further research or indirectly by lobbying for particular policies, thereby
creating new knowledge needs and shaping the world to which scientists respond (cf.
Kingdon 1984; Campbell 2003). Feedbacks between scientists, the knowledge they
produce, and stakeholders inspired or affected by that knowledge, can thus make
research agendas self-reinforcing and establish path dependencies.1 Other agendas –
and the public policies they might motivate or to which they might contribute – could
inspire different groups to seek to influence management, policy, and even subsequent
scientific research. The initial selection of research questions in a given problem area is
thus an important decision point with potentially lasting consequences.
Going back to early work by Robert Merton (1938), the limited research on problem
choice that has been done typically divides the influences on scientists’ decisions
between those internal to and those external to science, with the acknowledgment that
the two realms interact in complex ways (Zuckerman 1989). The few researchers
interested in problem choice have implemented a range of methods to enumerate these
various internal and external influences, including large-scale surveys (e.g., Busch et al.
1983; Debackere and Rappa 1994) and economic modeling (e.g., Carayol and Dalle
2007). Commonly cited amongst internal criteria are solubility of a potential problem
within an appropriate (often institutionally-defined) timeframe and congruency with
current theory; external influences include economic and other socially-defined needs
(e.g., Gieryn 1978; Zuckerman 1978; Ziman 1981; Zuckerman 1989). There has been a
relative lack of empirical attention, however, on how scientists integrate and weigh
1 ‘‘Path dependence’’ refers to a phenomenon wherein circumstances and decisions at one point constrain
choices available in the future and encourage further decisions in that same direction (North 1990).
214 M. W. Neff
123
these internal and external influences as they evaluate potential research problems. An
exception (albeit in a different context) is Brown (1998), who utilized Q method to
experimentally examine how students’ personal histories relate to problem choice, in
this case selection of topics for research papers in a graduate seminar.
Scientists for the most part can only research topics that someone is willing to
pay them to study, which seems to put research managers in the driver’s seat. Public
policy and political science scholars conceive of the relationship between funders of
science and scientists as a problem of delegation – wherein science funders have
ideas of what science they want to support, but must rely on scientists to conduct
that research – and use principal agent theory to understand and describe this and
other delegation relationships within science policy (e.g., Braun 1993; Guston 1996;
Braun and Guston 2003; Shove 2003). By controlling the purse strings, funding
agency program managers (the principals) assert some degree of control over the
research portfolios of individual investigators (the agents). These connections,
however, are far from straightforward. Shove (2003), for example, suggests that the
traditional theoretical designation of program managers as principals is in fact
overly simplistic because individual scientists assemble their research funding by
choosing amongst a variety of programs from any number of research sponsors, a
strategy which in effect inverts the relationship to make the investigators principals
and the program managers agents.
Beyond being able to solicit research funding from multiple sponsors, scientists,
in fact, collectively constitute many of the institutions that establish and administer
the research policies and incentives under which they operate. They do so, for
example, through peer review, instructing and advising students, serving on hiring
and tenure committees, and serving on journal editorial boards (Gieryn 1978; Ziman
1981; cf. Giddens 1984; Ziman 1987; Neff 2011).
Because the relationship between research manager and researcher is not a
simple delegation problem, the question of how individual scientists and the
scientific community more broadly evaluate the merits of potential research topics
becomes central to the task of linking knowledge production to knowledge needs. It
is critical for individual scientists, academic institutions and research sponsors with
outcome-oriented goals for their science to understand and be reflexive about the
processes by which research agendas emerge so that their efforts can most
effectively contribute to those goals (Miller and Neff 2013).
Coral reef conservation biology represents an ideal case through which to explore
how applied interests affect emergent research portfolios because there is a
consensus that reefs are in a state of crisis and many – if not most – researchers in
the field are motivated by a desire to ameliorate the threats facing reefs. As reported
by numerous recent reviews, diverse anthropogenic stressors threaten coral reefs
worldwide (Aronson et al. 2003; Hughes et al. 2003; Pandolfi et al. 2003;
Buddemeier et al. 2004; Burke and Maidens 2004; Wilkinson 2004; Kleypas and
Eakin 2007; Halpern et al. 2008). In general, the major stressors are reported to be:
pollution, sediment, and sewage discharge from coastal and upland developments;
overfishing and destructive fishing practices; coastal operations, including dredging,
mining of sand and coral, and landfilling; and the effects of climate change on the
marine environment, including changes in salinity, temperature, pH, and sea level;
Research Prioritization 215
123
and coral disease, exacerbated and compounded by all of the above. These reviews
suggest that there is considerable variability in the severity of these threats around
the world, and even within regions. They all agree, however, that the world’s reefs
face significant and unprecedented threats.
The perceived dire situation for the world’s coral reefs presents an interesting
challenge to researchers and research sponsors interested in contributing to coral reef
conservation: Given the reports that the world’s reefs face grave threats – and taking
into account the diversity and complexity of those stressors, the communities that
depend on reefs, and regulatory and management practices – how should research
effort be allocated? In this paper, I use Q method to explore how researchers and
knowledge users evaluate the merits of potential research projects relating to coral
reefs. Utilizing a participant set that includes coral reef conservation scientists,
managers, and activists, I catalogue and describe four different interpretations of
research priorities and how they should be set. I then interpret the implications of the
findings for outcome-oriented research across a variety of disciplines.
Methods
Q method is a social science research tool that combines the capabilities of
quantitative techniques with those of qualitative research to inductively identify
attitudes toward a topic present within a relevant community (Brown 1980; Brown
1996; Webler et al. 2009; McKeown and Thomas 2013). To accomplish this, the
investigator presents participants with statements about an issue and asks them to
rank the statements according to how well they agree or disagree with each. Ideally,
the statements come from the participant community itself with minimal editing so
that through the process of ranking the statements, each participant is able to
articulate and situate her/his opinions amongst the ideas of his or her broader
community. The researcher then typically interviews participants after the ranking
step to better understand their thought processes and collects relevant demographic
and other background data using a survey.
An underlying premise of the method is that within a community, there are fewer
ways of thinking of a given topic than there are people. By applying multivariate
data reduction techniques to the statement ranking results (using persons as
variables), the investigator can identify shared ideas present within the community,
colloquially known to Q method researchers as ‘‘factors.’’ These factors are perhaps
best thought of as ideal types, not representing individuals but rather capturing or
describing areas of broad overlapping thinking within the participant community.
Although factors are not people, authors colloquially refer to a factor’s ‘‘way of
thinking’’ to highlight the shared logic captured by that factor.
In analyzing the results, the investigator qualitatively examines the factors’
rankings of the statements, alongside interview and survey data collected at the time
of the ranking, to better understand the underlying logic and reasoning associated
with that factor (Brown 1980; Brown 1996; Robbins and Krueger 2000; Webler
et al. 2009; McKeown and Thomas 2013). Amongst other topics, the technique has
been applied to explore attitudes toward conservation, ecosystem management, and
216 M. W. Neff
123
environmental issues (e.g., Addams and Proops 2000; Niemeyer et al. 2005; Davies
and Hodge 2007; Hall 2008; Woolley and McCginnis 2008). Most directly related to
this study, Bischoff (2010) used Q method to understand conservation professionals’
attitudes of the coral reef ‘‘crisis,’’ and Sandbrook et al. (2011) used the approach to
explore conservation professionals’ underlying values. Brown (1989) introduced the
idea of using the method to analyze how participants evaluate research priority
statements, and Neff (2011) used Q to investigate the considerations of ecological
scientists as they evaluate the merits of potential research topics.
Data Collection
In keeping with the goal of using statements drawn from the participants’ vernacular,
I identified statements of research priorities from scientific, funding agency, and
management literatures on coral reef science and management as follows: First, I
read 26 research prioritization documents issued by various governmental agencies
in multiple nations, non-governmental organizations, individual scientists and their
societies, and multilateral collaboratives (see Supplementary Table 1 for a list of
source documents). I identified therein 582 statements of coral reef research priorities
and knowledge needs. In the next step, I isolated a subset of those statements that is
of a size (n=59) manageable for participants to sort. It is important that this subset of
statements (known as the Q sample) covers the diversity of ideas present in the
statement concourse so as to not leave out elements that are critical to participants’
ways of thinking of the issue. To accomplish that, I categorized the 582 statements
into groups based upon theme, and used a structured sampling strategy based upon
those categories (McKeown and Thomas 2013) to identify a subset that covered the
breadth of statements present in the initial list. I edited these statements minimally to
ensure understandability and consistent syntax while preserving content.
I chose to utilize statements enumerating specific research priorities rather than
broad norms about research priorities (which might read something along the lines of
‘‘research should focus on applied management needs’’). That choice allowed
participants to draw upon their own intuitions about what makes for important
research, rather than simply responding to abstracted norms about ‘‘good’’ or
‘‘important’’ research. In pilot work on this topic, abstract statements about broad
norms invited my test-participants to invoke received wisdoms rather than deep
reflection on the purposes and promises of various research topics. The study, as
administered, allowed participants to invoke their own justifications in follow-up
interviews and led to nuanced explanations of their prioritization processes.
With the help of three research assistants, I conducted the statement sorting, survey,
and interview portions of this research in a booth in the exhibit hall at the 11th
International Coral Reef Symposium, held in Ft. Lauderdale, Florida in July 2008.
Using conference registration as a pre-filter for my participant sample, I recruited 48
participants via convenience sampling of the attendants. Because people are the
variables in Q method and the purpose of the method is to uncover the discourses active
in the community, rather than to assess the prevalence or identify demographic
predictors of those discourses, there is no need to randomly sample participants from
Research Prioritization 217
123
the population of interest (Brown 1980). Participants, however, must be from the same
community that served as the source of the statements and must be appropriate to the
goals of the study. These participants met both requirements in that they represented
coral reef scientific, social scientific, and management/conservation communities that
a) potentially use coral reef knowledge, and b) wrote the documents that served as the
source for the statements. The survey of professional background filled out by each
participant verified that participants were members of the aforementioned groups; two
who were not were excluded from the analysis presented here.
My research assistants and I followed standard protocols in leading participants
through the process of ranking the importance of the 59 Q sample statements (Brown
1980). First, we asked participants to read through the statements, printed on index
cards, and to create three piles: the statements they saw as important for coral reef
researchers to pursue, those they felt were unimportant, and those that they either did
not understand or that did not elicit a strong response either way. We then instructed
participants to refine their initial rankings into a Gaussian distribution with eleven
categories, ranging from most unimportant (-5) to most important (?5). The number
of statements allowed in each category was as follows: 3,4,4,7,7,9,7,7,4,4,3. To
assign statements to these categories, we asked participants to pick up their
‘important’ piles, find the three statements that were most important to them and
place them on to the far right on the table in front of them. We then asked them to
pick up the ‘unimportant’ pile, identify the three that they found to be most
unimportant, and place them to the far left. Participants alternated between their
‘important’ and ‘unimportant’ piles and drew from the middle pile when necessary to
add the appropriate number of statements to each category from -5 (most disagree) to
?5 (most agree) to complete the ranking. We instructed participants to leave any
statements that they did not understand or were not relevant to their way of thinking
in the middle category. Participants were free to refine their rankings at any time.
As participants completed the sorting task, my team interviewed them to better
understand the thoughts and considerations that drove their choices. We asked all
participants what it was that was important about the items placed in the ?4 and ?5
categories, why they felt the statements in the -4 and -5 categories were
unimportant, and if they used any other criteria to help them to assess statement
importance. We used a series of probes to get beyond initial answers. When, for
example, participants cited a desire that research bring about better outcomes, we
inquired about what types of outcomes they hoped for, why those were the most
appropriate goals, and how that participant’s highly rated statements would better
contribute to those outcomes than would other possible research trajectories. As
mentioned previously, participants filled out a short survey of their demographic and
professional backgrounds upon completing the sorting and interview tasks.
Data Analysis
I used PQMethod (v.2.11) and SPSS (v16) for statistical analyses. I used principal
component analysis (persons as variables) to identify shared attitudes (best thought
of as generalizations of attitudes present in the broader community, and henceforth
218 M. W. Neff
123
referred to as ‘‘factors’’) based upon participants who had similar rankings of
priorities. Based upon the scree plot of eigenvalues, I initially retained and rotated
(varimax) four factors.
As described in the results, below, two participants were negatively associated
with Factor 3, indicating that their beliefs were best represented in this initial
analysis as being the opposite of those associated with that factor. In order to isolate
the differences between those positively and negatively associated with this factor, I
duplicated factor three, inverted one copy, and repeated the analysis.2 The resulting
factors are labeled 3A and 3B in the description below, and the final analysis thus
yielded a total of five factors.
The ultimate goal of Q method analyses is to understand and describe shared ways
of thinking about a topic. I accomplished this by analyzing the qualitative interview
data alongside the quantitative statement rankings using an approach called grounded
theory, a common social science technique to analyze data ill-suited to quantifica-
tion. Grounded theory is an iterative and inductive process by which investigators
test nascent explanations against all available data, and dismiss or refine those
explanations until they are robust, consistent with all data, and parsimonious (Glaser
and Strauss 1967). Each of the factor descriptions that emerged from my analysis
(see section Results) is consistent with the logic of the participants (ascertained
through post-sorting interviews) who were statistically associated with that factor. In
describing each factor’s reasoning, I attempt to highlight consensus justifications, as
well as identify the shared priorities for which there are mixed justifications.
Results
Participants uniformly felt that coral reefs face daunting and unprecedented threats,
and all indicated that their primary consideration in evaluating the statements was
that research should serve efforts to mitigate, alleviate, or otherwise address those
threats. This study identified four distinct interpretations of coral reef research
priorities among the participants despite their shared conviction that research should
serve conservation. The statements and each factor’s evaluation of them are found
in Table 1.
Factor 1: Understand and Interact with Local Communities to Save the Reefs
The logic associated with this factor was that to help address the threats facing coral
reefs, researchers must engage with and better understand communities adjacent to
the reefs. Participants statistically associated with this factor – who come from a
variety of disciplinary and professional backgrounds, including many in the natural
sciences – valued primarily social science research topics that they felt would yield
social and political information about the communities that use and depend on reefs
2 Steven Brown describes this analytical approach in an email to the Q method listserv (https://listserv.
kent.edu/cgi-bin/wa.exe?A0=Q-METHOD ) on September 9, 2008.
Research Prioritization 219
123
Ta
ble
1R
esea
rch
pri
ori
tyst
atem
ents
wit
hfa
cto
rsc
ore
sre
con
stit
ute
do
na
-5
(mo
std
isag
ree)
to?
5(m
ost
agre
e)sc
ale
Sta
tem
ent
Fac
tor
#
12
3A
3B
4
1R
esea
rcher
ssh
ould
pro
vid
eco
mpel
ling
evid
ence
toco
nvin
ceth
epubli
c,poli
cym
aker
s,m
anag
ers,
and
regu
lato
rsth
atd
ecli
nin
gco
ral
hea
lth
and
incr
ease
sin
dis
ease
are
cau
sed
pri
mar
ily
by
chan
ges
inla
nd-u
se
pat
tern
sth
atco
ntr
ibute
todeg
raded
wat
erqual
ity
thro
ugh
eutr
ophic
atio
n,
sedim
enta
tion,
and
chem
ical
load
ing
40
4-
15
2R
esea
rcher
ssh
ould
eval
uat
ech
anges
inw
ater
qual
ity
todet
erm
ine
the
succ
ess
of
man
agem
ent
acti
ons
tore
duce
sed
imen
t,n
utr
ien
t,an
dch
emic
alp
oll
uta
nts
and
oth
erfa
cto
rsth
atd
egra
de
wat
erq
ual
ity
10
5-
31
3R
esea
rcher
ssh
ould
dev
elop
pro
gra
ms
toin
volv
eco
mm
unit
ies,
reso
urc
euse
rs,
the
pri
vat
ese
ctors
and
oth
ers
in
mon
ito
rin
gth
eco
nd
itio
no
fco
ral
reef
san
dre
late
dec
osy
stem
s
5-
15
32
4W
en
eed
add
itio
nal
eco
log
ical
kn
ow
led
ge
of
reef
com
mu
nit
ies
bey
ond
the
do
min
ant
cora
lsan
db
on
yfi
shes
25
1-
41
5W
enee
dto
know
more
about
the
effe
ctiv
enes
sof
curr
ent
stra
tegie
sto
rest
ore
deg
raded
reef
s(e
.g.,
cult
uri
ng
cora
lsin
ala
bora
tory
,tr
ansp
lanti
ng
frag
men
ts,
and
crea
ting
cora
lnurs
erie
s),
takin
gin
toac
count
the
abil
ity
to
mai
nta
ing
enet
icv
aria
bil
ity
,m
itig
ate
sou
rce(
s)o
fth
ed
amag
e,m
ain
tain
the
his
tori
cal
dis
trib
uti
on
of
the
spec
ies
wit
hin
that
hab
itat
,an
dre
sto
reh
abit
atfu
nct
ion
20
3-
14
6W
en
eed
tok
no
wm
ore
abo
ut
the
resp
on
ses
of
key
org
anis
ms
toch
ang
ing
wat
erq
ual
ity
(sed
imen
ts,
nu
trie
nts
,
met
als,
pes
tici
des
,li
gh
t)in
exp
erim
ents
cross
edw
ith
chan
gin
gcl
imat
e(t
emp
erat
ure
,p
H)
12
4-
30
7In
esta
bli
shin
gan
dm
anag
ing
MP
As,
man
ager
snee
dto
under
stan
dhow
the
area
sm
ayim
pac
tth
epeo
ple
who
use
them
,an
dh
ow
use
rs,
intu
rn,
imp
act
tho
sear
eas
and
no
n-M
PA
area
s
3-
11
25
8W
en
eed
tok
no
win
wh
atw
ays
loca
lk
no
wle
dg
ean
dsc
ien
tifi
cin
form
atio
nin
flu
ence
ho
wad
jace
nt
com
mun
itie
s
use
and
pro
tect
cora
lre
efre
sou
rces
40
12
2
9W
en
eed
tok
no
wm
ore
abo
ut
the
effi
cacy
of
mea
sure
sto
red
uce
anth
rop
og
enic
stre
ssors
(in
clu
din
g
sed
imen
tati
on
,p
oll
uti
on
,eu
tro
ph
icat
ion
,cl
imat
ech
ang
e,o
ver
fish
ing,
and
ship
gro
un
din
gs)
inen
han
cin
g
reco
ver
yo
fex
isti
ng
po
pula
tio
ns
of
cora
lsan
dp
rom
oti
ng
sex
ual
recr
uit
men
t
31
44
-2
10
We
nee
dto
docu
men
thow
the
incr
easi
ng
scal
eof
hum
an-i
nduce
deu
trophic
atio
nal
ters
ecosy
stem
s1
25
-2
-2
11
Res
earc
her
ssh
ould
inves
tigat
em
icro
bia
lorg
anis
ms
asin
dic
ators
of
nutr
ient,
sedim
ent,
and
chem
ical
poll
uta
nts
inco
ral
reef
ecosy
stem
s
-1
34
-1
2
12
We
nee
dto
kn
ow
mo
reab
ou
tth
eso
cio
eco
no
mic
imp
acts
of
exis
tin
gan
dp
rop
ose
dfi
sher
ies
man
agem
ent
pla
ns
that
affe
ctco
ral
reef
eco
syst
ems
41
15
1
220 M. W. Neff
123
Ta
ble
1co
nti
nu
ed
Sta
tem
ent
Fac
tor
#
12
3A
3B
4
13
Res
earc
her
ssh
ould
inves
tigat
eth
eex
tent
tow
hic
hth
ediv
ersi
tyof
aco
mm
unit
ydet
erm
ines
(a)
‘‘st
abil
ity,’’
(b)
pro
duct
ivit
y,
(c)
resi
stan
ceto
inv
asio
no
rd
isea
se,
and
(d)
abil
ity
tore
cov
erfr
om
nat
ura
lan
dh
um
an
imp
acts
15
12
-1
14
Res
earc
her
ssh
ou
ldd
evel
op
too
lsto
red
uce
the
pre
val
ence
of
dis
ease
s,m
itig
ate
thei
rim
pac
ts,
and
trea
taf
fect
ed
cora
ls
-2
-1
30
4
15
Res
earc
her
ssh
ould
surv
eyre
efs
and
sho
als
toas
sess
the
per
form
ance
of
mar
ine
pro
tect
edar
eas
10
04
3
16
We
nee
dad
dit
ion
alu
nd
erst
and
ing
of
the
role
of
mic
rob
esin
the
fun
ctio
nin
go
fh
ealt
hy
and
stre
ssed
reef
s-
23
2-
40
17
Res
earc
his
nee
ded
on
the
bas
icb
iolo
gy
and
ph
ysi
olo
gy
of
cora
lsto
serv
eas
ab
asel
ine
for
cora
lh
ealt
han
d
dis
ease
inves
tigat
ions
and
tobet
ter
dis
tinguis
hbet
wee
ndis
ease
and
nat
ura
lch
anges
(e.g
.,gro
wth
and
repro
du
ctio
n)
-1
3-
1-
13
18
We
nee
dto
kn
ow
wh
ich
dis
ease
syn
dro
mes
are
infe
ctio
us
-2
-2
3-
54
19
We
nee
dto
kn
ow
more
abo
ut
the
imp
act
of
chan
gin
gh
um
and
emo
gra
phic
so
nco
ral
reef
eco
syst
ems
2-
31
03
20
Res
earc
her
ssh
ould
det
erm
ine
the
lev
elo
ffi
shin
gp
ress
ure
and
the
dis
trib
uti
on
of
effo
rtfo
rsu
bsi
sten
ce,
recr
eati
onal
,an
dco
mm
erci
alfi
sher
ies,
and
the
impac
tof
thes
eac
tivit
ies
on
fish
erie
sre
sourc
esan
dco
ral
reef
hab
itat
s
4-
1-
13
2
21
We
nee
dsc
ienti
fic
crit
eria
todet
erm
ine
the
carr
yin
gca
pac
ity
of
the
reef
ecosy
stem
,an
ddet
erm
ine
the
level
of
recr
eati
on
alu
se(e
.g.,
div
ing,
sno
rkel
ing
,an
db
oat
ing
)th
atsp
ecifi
car
eas
can
sup
po
rt
2-
20
-1
3
22
We
nee
dad
dit
ional
info
rmat
ion
on
the
ecolo
gic
alef
fect
so
fover
fish
ing
and
des
truct
ive
fish
ing
pra
ctic
es,
incl
ud
ing
the
effe
cts
on
no
n-t
arg
eted
spec
ies
and
on
ben
thic
cora
lre
efh
abit
ats
2-
1-
11
2
23
Res
earc
her
ssh
ould
stud
ylo
cal
atti
tud
esan
dp
erce
pti
on
sre
gar
din
gh
abit
atco
nse
rvat
ion
effo
rts
bec
ause
they
cou
ldb
eim
po
rtan
tto
the
long
-ter
msu
cces
so
fre
sto
rati
on
and
con
serv
atio
nef
fort
s
5-
1-
21
0
24
We
nee
dto
kn
ow
more
abo
ut
the
eco
no
mic
dri
ver
sth
atco
ntr
ibu
teto
lan
d-b
ased
sou
rces
of
mar
ine
po
llu
tio
n3
-4
2-
10
25
We
nee
dm
ore
cert
ain
kn
ow
led
ge
abo
ut
the
po
ten
tial
for
and
the
lim
its
on
rapid
adap
tati
on
of
reef
cora
lsto
risi
ng
sea
tem
per
atu
res
-1
4-
55
5
Research Prioritization 221
123
Ta
ble
1co
nti
nu
ed
Sta
tem
ent
Fac
tor
#
12
3A
3B
4
26
Res
earc
her
ssh
ou
ldd
evel
op
mo
del
sto
fore
cast
long
-ter
mef
fect
so
fd
isea
seo
np
op
ula
tio
nd
yn
amic
s,co
mm
un
ity
stru
ctu
re,
and
ecosy
stem
fun
ctio
nin
corp
ora
tin
gin
form
atio
no
nb
ioti
cag
ents
,en
vir
on
men
tal
fact
ors
,an
d
anth
rop
ogen
icst
ress
ors
kn
ow
no
rp
red
icte
dto
affe
ctd
isea
sep
rev
alen
cean
din
cid
ence
.
02
21
-2
27
We
nee
dto
know
how
anth
ropogen
icor
nat
ura
lch
anges
hav
efa
cili
tate
dth
ees
tabli
shm
ent
of
invas
ive
po
pula
tio
ns,
and
wh
ether
man
agem
ent
acti
on
sm
itig
atin
gth
ese
chan
ges
can
redu
ceth
ein
tro
du
ctio
nan
d
spre
ado
fin
vas
ive
spec
ies
1-
20
22
28
We
nee
dk
no
wle
dge
on
the
bas
icli
feh
isto
ries
of
cora
ls,
spo
ng
es,
and
oth
erb
enth
icin
ver
teb
rate
s0
5-
4-
3-
3
29
Res
earc
her
ssh
ould
det
erm
ine
whet
her
spec
ies
intr
oduct
ions
hav
em
ajor
conse
quen
ces
for
mar
ine
ecosy
stem
fun
ctio
n
00
00
2
30
Res
earc
her
ssh
ould
det
erm
ine
ho
wg
enet
ic,
spec
ies,
and
eco
syst
emd
iver
sity
var
yin
spac
ean
dti
me
atd
iffe
ren
t
regio
nal
scal
es,
and
wit
hin
hab
itat
sw
ith
inth
ese
regio
ns
-1
41
-4
-2
31
We
nee
dto
kn
ow
ifth
ead
dit
ion
of
hu
man
imp
acts
and
the
frag
men
tati
on
of
cora
lre
efh
abit
ats
(aff
ecti
ng
gen
e
flow
)under
min
esco
ral
reef
ecosy
stem
resi
lien
cyan
dm
akes
them
more
susc
epti
ble
toco
ral
ble
achin
g
01
-2
11
32
Res
earc
hn
eed
sto
focu
so
nin
stit
uti
on
alar
ran
gem
ents
and
ho
wla
ws,
po
lici
es,
and
org
aniz
atio
nal
rela
tio
nsh
ips
infl
uen
ceth
eu
se,
man
agem
ent,
and
pro
tect
ion
of
reef
eco
syst
ems
5-
30
4-
2
33
Res
earc
her
ssh
ould
esta
bli
shst
andar
dte
rmin
olo
gy
,m
eth
odo
log
yan
dp
roto
cols
rela
tin
gto
cora
ld
isea
se-
3-
22
11
34
We
nee
dto
kn
ow
more
abo
ut
ho
wv
aria
tio
ns
inca
lcifi
cati
on
rate
saf
fect
asso
ciat
edo
rgan
ism
s,fo
od
web
dy
nam
ics,
carb
on
and
nu
trie
nt
cycl
ing
,an
dec
osy
stem
serv
ices
-1
32
-2
-3
35
We
nee
dm
ore
info
rmat
ion
on
the
cell
ula
ran
dm
ole
cula
rm
echan
ism
sof
cora
lble
achin
g-
42
2-
2-
2
36
Res
earc
her
ssh
ould
des
ign
and
con
duct
dem
on
stra
tio
np
roje
cts
toev
alu
ate
scie
nce
-bas
edm
anag
emen
to
pti
on
s
for
imp
rovin
gsh
ore
lin
est
abil
ity
,w
hil
em
ain
tain
ing
cora
lre
efec
osy
stem
fun
ctio
ns
0-
43
-2
0
37
Res
earc
his
nee
ded
tou
nd
erst
and
and
pre
dic
tch
ang
esre
sult
ing
fro
mex
trem
eev
ents
ver
sus
nat
ura
lv
aria
bil
ity
-1
4-
52
1
38
Res
earc
her
ssh
ould
exp
lore
,d
isco
ver
,m
ap,an
dch
arac
teri
zesh
allo
wd
eep
cora
lre
efs
and
oth
erse
nsi
tiv
eb
enth
ic
com
mu
nit
ies
01
0-
2-
2
222 M. W. Neff
123
Ta
ble
1co
nti
nu
ed
Sta
tem
ent
Fac
tor
#
12
3A
3B
4
39
Res
earc
her
ssh
ould
use
long
-ter
mre
cord
so
flo
cal
sea
surf
ace
tem
per
atu
res
from
cora
lco
res
and
oth
er
pal
eocl
imat
icso
urc
esto
pla
cem
ore
rece
nt
reco
rds
of
ble
achin
gev
ents
and
chan
ges
inco
ral
com
munit
ies
wit
hin
alo
nger
tem
pora
lper
spec
tive
-5
12
20
40
Res
earc
her
ssh
ould
det
erm
ine
whet
her
rece
nt
dec
lines
inco
ral
hea
lth
are
unpre
ceden
ted,
or
whet
her
sim
ilar
dec
lin
eso
ccu
rred
inth
ep
ast
inth
eab
sen
ceo
fan
thro
po
gen
icst
ress
ors
-4
0-
53
4
41
We
nee
dso
cial
scie
nce
rela
ted
toh
abit
atre
sto
rati
on
and
pro
tect
ion
toin
ves
tig
ate
the
fact
ors
dri
vin
gsu
ppo
rtfo
r
or
op
posi
tio
nto
such
effo
rts.
Th
isco
uld
incl
ud
ean
exam
inat
ion
of
dif
fere
nt
inst
itu
tio
nal
fact
ors
dri
vin
gb
oth
the
use
and
pro
tect
ion
of
cora
lre
efec
osy
stem
s
2-
30
5-
1
42
Co
ral
reef
rese
arch
ers
sho
uld
coll
abo
rate
wit
hcl
imat
em
od
eler
sto
bu
ild
nes
ted
mo
del
sfo
rd
ow
nsc
alin
gcl
imat
e
chan
ge
scen
ario
sto
regio
nal
-sca
leco
ral
ble
achin
gth
reat
-2
4-
43
-1
43
Res
earc
her
ssh
ould
quan
tify
the
soci
oec
onom
icim
pac
tso
fco
ral
ble
achin
gev
ents
on
use
rgro
ups
and
the
econom
yan
din
ves
tigat
euse
rgro
up
per
cepti
ons
of
cora
lble
achin
gev
ents
2-
2-
30
-1
44
Res
earc
her
ssh
ould
det
erm
ine
wh
ether
incr
ease
sin
env
iro
nm
enta
lh
eter
og
enei
tyin
spac
ean
dti
me,
incl
ud
ing
dis
turb
ance
,in
crea
seb
iod
iver
sity
,es
pec
iall
yin
the
coas
tal
zon
es
01
02
-4
45
Met
ho
ds
nee
dto
be
dev
elo
ped
toas
sess
the
val
ue
of
thes
en
on
-eco
no
mic
‘env
iro
nm
enta
lse
rvic
es’
and
inco
rpora
teth
emin
todec
isio
ns
about
the
pro
tect
ion
of
thes
eim
port
ant
reso
urc
es
3-
2-
20
-3
46
Res
earc
her
ssh
ould
dev
elo
pto
ols
tod
etec
tan
dd
escr
ibe
dec
adal
chan
ges
inre
lati
on
ton
atu
ral
and
anth
rop
ogen
ic
dis
turb
ance
s
-1
2-
10
-5
47
We
nee
dto
know
tow
hat
deg
ree
rest
ora
tion
inte
rven
tions
signifi
cantl
yen
han
cere
cover
yof
cora
lre
ef
com
munit
ies
inco
mpar
ison
tow
hat
would
be
achie
ved
by
nat
ura
lre
cover
ypro
cess
esover
a5–10
yea
rti
me
scal
e
1-
3-
44
0
48
Res
earc
her
ssh
ould
inves
tigat
ew
hat
wil
lbe
the
net
effe
ctof
chan
ges
inse
a-le
vel
on
sedim
ent
pro
duct
ion,
resu
spen
sio
n,
and
tran
spo
rto
fp
oll
uta
nts
,n
utr
ien
ts,
and
larv
ae
-3
-1
-2
00
49
Res
earc
her
ssh
ould
dev
elo
psp
atia
lm
od
els
of
bio
div
ersi
tyw
ith
par
ticu
lar
emph
asis
on
spec
ies
turn
ov
eran
d
env
iro
nm
enta
ld
riv
ers
-2
20
-4
-3
Research Prioritization 223
123
Ta
ble
1co
nti
nu
ed
Sta
tem
ent
Fac
tor
#
12
3A
3B
4
50
Res
earc
her
ssh
ould
dev
elo
pre
mo
tely
op
erat
edo
bse
rvat
ori
esan
dsu
pp
ort
ing
tech
no
log
ies
(e.g
.,in
situ
sen
sors
,
sate
llit
eim
ager
y)
toal
low
for
ob
serv
atio
ns
of
rem
ote
cora
lre
efs,
incl
ud
ing
mon
ito
rin
gre
mo
teco
ral
reef
sfo
r
un
auth
ori
zed
fish
ing
0-
2-
2-
1-
1
51
The
idea
that
cora
lsca
n‘s
wap
out’
sym
bio
nts
should
be
addre
ssed
inth
eco
nte
xt
of
cora
lble
achin
g.
-4
2-
11
-4
52
Res
earc
her
ssh
ou
ldh
elp
dev
elo
pm
od
els
toad
dre
ssw
ays
top
red
ict,
con
tain
,p
rev
ent,
and
mit
igat
eth
ep
ote
nti
al
imp
acts
on
the
gen
etic
div
ersi
tyo
fw
ild
sto
cks
and
the
rele
ase
of
op
port
un
isti
cp
ath
og
ens
on
nat
ive
po
pula
tio
ns
fro
maq
uac
ult
ure
acti
vit
ies
01
-3
-2
-1
53
Lo
ng
-ter
mst
ud
ies
on
mar
ine
bio
tash
ou
ldn
ot
focu
sex
clusi
vel
yo
nth
eir
use
asin
dic
ato
rso
fg
lob
alcl
imat
e
chan
ge
-3
0-
1-
2-
5
54
Res
earc
her
ssh
ould
dev
elop
algori
thm
sto
des
ign
connec
ted
syst
ems
of
man
agem
ent
zones
(e.g
.,m
arin
e
rese
rves
)bas
edon
the
loca
lth
erm
alre
gim
e,ble
achin
g-s
tres
s,an
dla
rval
connec
tivit
y
-2
0-
31
-4
55
Res
earc
her
ssh
ould
iden
tify
the
com
po
nen
t(s)
inai
rsa
mp
les
from
du
stso
urc
es(e
.g.,
Afr
ica
and
Go
bi
Des
ert)
and
do
wn
win
dsi
tes
that
are
tox
icto
cora
lre
efo
rgan
ism
s
-5
-4
-1
-3
1
56
Res
earc
her
ssh
ould
esta
bli
shap
pro
pri
ate
model
syst
em(s
)fo
rst
andar
diz
edco
ral
studie
sin
the
labora
tory
-4
-5
-2
-5
0
57
Res
earc
her
ssh
ould
dev
elo
pan
du
tili
zea
com
bin
atio
no
fre
mo
tely
sen
sed
ob
serv
atio
ns
of
win
ds,
tem
per
atu
re,
sea
surf
ace
hei
ght,
and
oce
anco
lor
todefi
ne
regio
ns
of
oce
anic
conver
gen
cean
dth
eli
kel
yac
cum
ula
tion
of
mar
ine
deb
ris,
and
then
dev
elo
pm
eth
od
sto
trac
kan
din
terd
ict
mar
ine
deb
ris
atse
ab
efo
reit
dam
ages
cora
l
reef
ecosy
stem
s
-2
-4
-3
0-
1
58
Res
earc
her
ssh
ould
dev
elo
pin
tera
ctiv
eco
mp
ute
rg
ames
tom
od
elth
ed
yn
amic
so
fth
eec
osy
stem
itse
lfin
such
a
way
that
isac
cess
ible
toth
eg
ener
alp
ub
lic,
allo
win
gth
eu
ser
toin
pu
td
iffe
ren
tv
aria
ble
san
dse
eth
eir
resu
ltin
gim
pac
to
nth
eec
olo
gy
-3
-5
-2
0-
5
59
Res
earc
her
ssh
ou
ldcr
eate
ap
rop
riet
ary
bio
info
rmat
ics
syst
emth
atw
ill
per
mit
des
kto
pb
iod
isco
ver
yta
ilo
red
for
clie
nts
or
med
ical
ther
apie
s
-5
-5
-4
-5
-4
224 M. W. Neff
123
(Statement 39 & 50), clarify why they use them as they do (Statement 47), and
identify what might lead those communities to change their behavior when
researchers saw their behaviors as detrimental. A better understanding of the human
dimensions of reef systems, they felt, would lead to immediate and significant
changes to reef condition. Engaging locals by enlisting their help in monitoring
reefs (Statement 5) – a top-ranking priority for this factor – would foster community
stewardship and a sense of ownership to bring about those changes. In addition to
ranking highly the statements that referred to local communities, participants
frequently mentioned the importance of engaging with those communities in their
justifications of those priorities.
Some participants associated with this factor found it important to study the local
communities based upon a belief that locals are the ones best able to make changes
that will immediately affect reef conditions, either by altering their own behavior or
through policing that of others. As one participant said, ‘‘the only way to make
changes in the environment is to deal with people; people – and money – make
decisions.’’ Another stated that ‘‘educating local communities is important because
they are the ones using and impacting the resource daily’’ and that we ‘‘need to have
them help protect the resource.’’ In other follow-up interviews, however, participants
portrayed the local people as villains who need more oversight. One participant
quipped that we need to know more about how local communities interact with reefs
‘‘so people aren’t dynamiting the coral at night or breaking it.’’ Another participant
who was statistically associated with this factor said that she thought it was important
to interact with communities adjacent to reefs because there are ‘‘too many people –
dumb people.’’ That participant completed her thought with a comment that vividly
summarizes her opinion of communities adjacent to coral reefs: ‘‘A good pandemic
would fix a lot.’’3
Priority statements that focused on natural science are comparatively or
completely unimportant to this factor, even though many of the associated
participants are active university or government-sponsored natural scientists. This
factor has a particular distaste for statements referring to research on large spatial
and temporal scales (e.g., Statements 45, 38), which some see as ‘‘nice to have in the
long run,’’ or ‘‘important to an extent, but not a priority because [the information
does not] deal directly with management.’’ Others find that type of information as
less useful because ‘‘there’s a need to concentrate on what’s happening now; not in
the future or over time’’ and that there are ‘‘better uses for funding because we can’t
do anything about [phenomena on these scales].’’ The priority statements relating to
climate change are unimportant to this factor because, no matter how much we
know about phenomena on global scales, ‘‘this is the category we can do least
about.’’ Knowledge, for this factor, is most important when it contributed directly to
changes on the ground. Most who were statistically associated with this factor felt
confident that enough is known about the biology, geomorphology, and history of
coral reefs to manage them well.
3 Statements such as these were not common, but are included here to demonstrate the diversity of
motivations and justifications behind shared understandings of ‘‘important’’ research.
Research Prioritization 225
123
Factor 2: Basic Reef Science First
Participants associated with this factor thought that reefs could not be adequately
protected and managed without additional biological understanding. The best way
researchers can contribute to improving reef condition was, according to this factor,
by addressing basic natural science questions. Key goals for this factor included
describing the life histories of vertebrates and invertebrates (Statement 2), expanding
knowledge inquiry beyond dominant corals and bony fishes (Statement 22), and
improving theories about the causes of biodiversity and its link to ecosystem function
(Statement 53). Building upon this knowledge, which this factor saw as fundamental
and prerequisite for investigating other topics, this factor valued understanding the
potential impacts of climate change on reef communities (Statement 16) and
integrating knowledge about reefs into climate models (Statement 15).
Participants associated with Factor 2 shared a single logic for valuing what they see
as basic science questions: Good policy requires, first and foremost, well-established
scientific understanding. For example, one participant said ‘‘We need basic/baseline
knowledge/understanding of the system to make other information relevant. We need
to understand the system before we can understand impacts, especially socio-economic
[impacts].’’ Another stated: ‘‘if we know more about the limits of adaptation [of coral
reef organisms], we can understand more about what climate change is acceptable,
which is important for many reasons, especially for policy changes’’ and ‘‘regional
models can predict bleaching of reefs, which would prioritize reefs which are most at
risk, then we can spend money to help those reefs that are least at risk.’’ Focusing on
high-risk reefs, this participant felt, would be an ineffective use of resources as
compared to protecting relatively pristine areas. Another stated that ‘‘biodiversity is
useful knowledge for anything – you need to understand it to address problems.’’ For
this factor, knowledge of the basic biology of reefs and reef organisms was a
prerequisite for management interventions that might improve their condition.
Factor 3A: Humans are Responsible! Let’s Do Research That Will Leadto Change
Factor 3A’s priorities were defined by two underlying beliefs: humans are the main
driver of damage to coral reef ecosystems, and research should be prioritized based
upon the potential for additional information to improve behavior or policy. They
felt there was benefit, for example, in documenting the impacts of anthropogenic
eutrophication (Statement 59) because, as associated participants explained in the
interviews, local drivers of damage are more easily managed than global-scale ones.
Climate change research was unpopular for this ideal type. The least popular
statement for this factor was one that claimed a need for more knowledge about the
‘‘potential for and limits on rapid adaptation of coral reefs to rising sea temperatures’’
(Statement 16). Also unpopular was a statement that declares that coral reef
researchers should work with climate modelers to downscale global models to provide
information relevant to local reef bleaching (Statement 15). Some who were
associated with this factor opposed these statements because they felt this knowledge
226 M. W. Neff
123
to be already secured: ‘‘Corals do not adapt rapidly’’; others because they were
concerned that studying these questions ‘‘markets false hope’’ by suggesting to the
public feel that global climate change may not pose a threat to corals and coral reefs.
Similarly unpopular were statements declaring a need for better understanding of
long-term records of coral health to provide context for recent events (Statement 12).
These respondents were confident that corals are less healthy now than at any prior
point, and that anthropogenic eutrophication is largely to blame. They favored
research statements they believed would lead to action to mitigate those stressors.
People associated with this factor believed many topics represented in the Q
sample to be over-studied and unrelated to solution-driven knowledge needs. Some
participants associated with this factor were in fact quite cynical about scientists’
motivations. For example, one expressed concern that ‘‘It’s hard to know if these
scientists [claiming the need for more research] just want more money.’’ Another
explained his critique of long-term and theoretical research by saying that people
can ‘‘study these topics to death’’ without any benefit to coral reefs.
Factor 3B: Ecosystems Are Dynamic; Policy Affects People, and People AffectReefs
In the initial four-factor solution, this ideal type was best described as having
opinions opposite those of Factor 3A. By analyzing it separately as its own factor,
however, it is possible to more accurately characterize this set of attitudes. This
subsequent analysis reveals that Factor 3B differs from 3A in part by assigning the
various stressors on reefs different levels of importance, which makes sense given
the spatial heterogeneity of threats to a global ecosystem type. Compared to 3A, this
factor treated eutrophication as a lower priority for future research (Statements 36,
59, 18, and 33 all received comparatively lower scores).
This factor valued instead research along two lines. The first includes topics that
would lead to better understanding of local human communities that rely on and affect
reef health (Statements 40, 50, 47), an interest shared with Factor 1. This ideal type
recognizes that conservation policies impact the human communities that depend on
reefs, and that for those policies to work well the local communities must accept them.
As one participant stated, ‘‘Good science doesn’t matter if policy doesn’t work out.’’
The second main area of research priority for this factor includes topics related to
understanding anthropogenic stressors against the background of ecosystem
dynamism, especially in light of climate change. This factor saw it as important
to understand the limits of coral adaptation to rising sea temperatures (Statement
16), and assessing whether recent changes in coral health are unprecedented, or if
similar changes might occasionally occur absent anthropogenic stressors (Statement
31). Overall, this factor sees human behavior and natural systems as being dynamic
and intertwined. This factor’s favored research topics focused on these intercon-
nections between the natural and the social.
Both of the participants associated with this ideal type worked primarily in the
Philippines, and their priorities were driven by what one described as an ‘‘urgency
to solve climate change problems.’’ These participants felt that placing current
Research Prioritization 227
123
events in a longer-term context will help solve problems by giving a clear message
to the public, and ‘‘getting a clear message out to the public is important.’’ One
stated that ‘‘more research in global warming [will generate] more knowledge
[which will lead to] more convincing evidence to convince the public of global
warming.’’ Whereas those associated with Factor 3A felt that there was little
marginal benefit to additional knowledge on climate change impacts because of the
intractability of the policy issue, Factor 3B felt that contributing to a consistent
message of that stressor’s importance was imperative for coral reef researchers.
Factor 4: Develop and Refine Management Tools
This factor was confident that land use changes are the primary driver of reef
degradation; reef conditions would improve once the public, managers, policy-
makers, regulators and others understood that connection (Statement 11). Whereas
Factor 1 believed that the knowledge base was sufficiently settled for managers to
go ahead and take the steps to improve reef condition, this factor saw a critical role
for researchers in building additional scientific understanding of reef systems and
evaluating management practices (Statements 42, 25). Science, this factor felt,
should, generate restoration and management tools, establish baseline measure-
ments of coral reef condition, and improve biological knowledge to inform long-
term reef conservation and management. Although natural science questions
dominated this factor’s priorities, they were also interested in understanding how
people interact with marine protected areas because that interaction, they felt,
determines the effectiveness of marine protected areas as a conservation tool. They
shared this mode of thinking with Factor 3B.
Though participants who were statistically associated with this factor largely shared
research priority rankings, some believed that nearly all were worth pursuing, while
others believed many to not merit any research attention whatsoever. A graduate
student in chemical ecology strongly associated with this factor had a hard time
identifying statements that were even comparatively unimportant. Another participant
who was a coral management specialist for a United States federal agency and who
herself conducted mixed socio-ecological research, however, had a hard time
identifying any important statements, stating that ‘‘there are some things we shouldn’t
do at all … I could put half [of the entire] stack in -5,’’ indicating the ‘most unimportant’
category during the final step of the sorting process. These two, however, rated the
relative importance of the statements similarly, and thus are together in Factor 4. In
selecting research to conduct or fund, scientists and their funders nearly always operate
under resource constraints, and thus the use of a forced distribution in this study is
roughly representative of the choices that those involved with research have to make.
Overall Results
The interviews revealed that participants in this study unanimously felt that coral
reefs face unprecedented challenges to their long-term health and survival. Though
228 M. W. Neff
123
the goals differ, this study replicates findings from related Q method studies seeking
to understand conservation controversies in that it identified narratives about the
environment asserting that conservation is a technical issue to be implemented by
experts (Factor 2), as well as others that view conservation to be a complex process
involving social and ecological components (e.g., Mattson et al. 2006; Mattson et al.
2011).
When queried about their approach to the sorting task, all participants said that
they evaluated the priority statements based on their own experiences or instincts
about what kinds of information might be useful in conserving reefs. Despite this
shared motivation, there were no consensus statements, meaning that at least one
pair of factors had a significantly differing interpretation of the importance all of the
included research priority statements. The variability in the assessment of
importance of stressors on reef health, a consideration that represented a large
part of the division between Factors 3A and 3B, is to be expected given the diversity
of reef systems with which the participants are familiar and the uneven distribution
of those stressors.
Differing ideas of the role of science in bringing about societal change, however,
also influenced how people responded to statements about various stressors. Some
individuals saw climate change as the most important stressor on coral reefs and
used that as a justification for additional research on the topic; others were similarly
confident that climate change was an important (or the most important) stressor, but
rated it as a relatively unimportant research topic because of its perceived political
intractability. Yet others eschew climate change research based upon a concern that
supporting additional such research might telegraph to the public that current
scientific understanding is inadequate to justify action.
Although some participants indicated that they ranked highly the types of work
that they personally do because that is what they know the most about, others did the
exact opposite, explaining that they felt their own disciplinary expertise to be
relatively unimportant or ineffectual in bringing about better outcomes. When
pressed further, several of these latter participants indicated that they entered their
fields because of a belief that their disciplines would help improve environmental
conditions, but said that in the course of their work on coral reefs they had come to
believe that other types of research were comparatively more important because
they were more likely to lead to effective reef conservation. This sentiment was
most common amongst practicing natural scientists. One US-trained scientist
working in Fiji offered a common sentiment when he said ‘‘I’ve been a scientist for
a long time. Science is not going to change the world; people are.’’ Another
explained that in her experience young people get into science because they want to
help solve problems but that as they age they realize they have misplaced their
efforts and that change was more likely to come about from outreach, social science,
and policy work.
These natural scientists who favored researching and interacting with human
communities frequently felt that their personal research trajectories were determined
early in their careers – frequently in their undergraduate programs – and that
because of the need to advance their careers they had little opportunity to change
their personal foci as their careers progressed. Not only did they feel unable to shift
Research Prioritization 229
123
their personal research foci, but several also expressed that they could not even
advocate for a shifting allocation of resources to address topics other than that upon
which they personally focused without damaging their own ability to bring in grant
money. Tellingly, the most common refrain from all participants in this study was
some variant of ‘‘Don’t tell my colleagues I said this was the most important
research topic.’’ One participant, noting that this tension goes beyond individuals to
include institutions, said that the Coral Reef Targeted Research group at the Global
Environment Facility recommended a greater focus on social science research, but
that it was limited in how vocally it could advocate that focus because its funding is
tied to ‘‘hard science.’’
Discussion
Three aspects of these findings suggest that forums for deliberating research
priorities could help to ensure that research is optimally effective in contributing
to better outcomes. First, the different understandings of research priorities derive
not just from differential understandings of the threats to reefs4, but also from
variety in participants’ understandings of how research does and should contribute
to change on the ground. Some participants felt that effort should be dedicated
primarily to stressors they felt were locally manageable because that is where
research can have the strongest impact; others, however, wanted to use coral reefs
as a case study to develop evidence to justify action on global stressors such as
climate change, hoping that a more evidence of the impacts of climate change
might compel action on that hotly contested set of policy issues. Some participants
felt future research should focus on aspects of the system about which we already
know something; others thought the opposite: that research should focus on areas
where there is currently little information. What made a stressor appealing as a
study-subject to one person was occasionally the same attribute that made it
unappealing to another.
Many of the above considerations, used by scientists and non-scientists alike to
determine which stressors are worth studying, are based on policy preferences,
understandings of managers’ knowledge needs, and ideas of how behavioral,
institutional, and legal change come about. Scientists do not necessarily have
training or direct experience that would yield expertise on these considerations.
Others, however, do. Formalized and ongoing conversations with management
practitioners, bureaucrats, elected officials and representatives from stakeholder
groups could improve scientists’ ability to identify and respond to knowledge needs.
Similarly, experts from other academic fields, such as political science and
behavioral psychology, could lend additional insights as to which issues or
controversies may be influenced by what types of additional knowledge. Including
such perspectives would ensure that these varied considerations are informed by
democratic accountability and expertise rather than by anecdote and speculation.
4 The severity of stressors in a given region is a question that is plausibly amenable to technical
adjudication, but that would also likely benefit from non-technical forms of expertise (cf., Wynne 1989).
230 M. W. Neff
123
Secondly, the comment from one participant attributing coral reef damage to the
existence of ‘‘too many people – dumb people,’’ and her later conclusion that ‘‘A good
pandemic would fix a lot’’ should serve as a reminder that neither individual scientists
nor the scientific enterprise as a whole inherently represent the values of the citizens
who fund their work (See also Neff and Larson 2014). Some research topics may be
better than others in contributing to the goals of the societies that fund science (Bozeman
and Sarewitz 2011). To ensure that science best contributes to democratically-
determined goals, an increasing number of philosophers and science policy scholars
have called for strengthened ties between science and democracy (Kitcher 2001;
Kitcher 2003; Guston 2004; Latour 2004; see also Lasswell and McDougal 1992).
The third element in these results to suggest that conservation science could
benefit from more open conversations about research priorities is that participants
reported that disciplinary norms and pressures created mismatches between what
some scientists do professionally and what they feel is likely to lead to better
outcomes. While these disciplinary norms are in many ways critical to the
advancement of academic disciplines (Zuckerman 1978; Kuhn 1996), they also pose
a challenge to researchers who are motivated by a desire to address real-world
problems. Participants revealed that many scientists feel that they learn through
experience what might be effective in bringing about better outcomes, however
defined. Those participants commonly suggested that the incentive structures and
institutions under and within which they operate preclude them from addressing
those topics and encouraging others to do so.
This pattern may create lasting legacies of path dependence that discourage
future researchers from studying those preferred topics. This is because whether or
not they believe their particular research project to be the most important,
researchers must make that argument to funding entities, journal editors, hiring and
tenure committees, and other institutions of science in order to advance their
careers. To the extent that each researcher is successful in making that claim, those
arguments create additional incentive for subsequent researchers to address that and
related topics (Zuckerman 1978; Callon et al. 1986; Ziman 1987; see also Neff and
Corley 2009). The net result is that conservation researchers’ collective efforts may
be systematically steered away from the topics researchers feel to be most effective
in bringing about better outcomes. For a field like coral reef conservation biology
that seeks solutions to pressing real-world problems, disciplinary path dependencies
may artificially constrain the contributions that research can make.
Priority setting processes open to genuine discussion with broader communities
could help to ameliorate the path dependence problem by creating forums to discuss
and share lessons learned through not only formal research, but also through
experience that researchers and other practitioners gain through lifetimes of hands-
on experience. In addition to minimizing unintended path dependencies, such
forums could create an opportunity for the scientific community to be more directly
informed by democratic processes and other forms of relevant expertise, and ensure
that science is effective in contributing to better outcomes.
Some coral reef research funding bodies undertake priority-setting exercises with
the intent of integrating insights from a variety of stakeholders, and to the extent that
these processes involve a range of stakeholders and nuanced discussion and debate
Research Prioritization 231
123
about what science should be prioritized and why, they may serve the above
purposes. The Australian Institute for Marine Science, for example, generated a list
of coral-related research priorities through iterative interactions with stakeholders
alongside a series of peer review exercises (Australian Institute of Marine Science
2007). Users of research were involved throughout the process, and the result is a
fairly cohesive set of research priorities that are, at least potentially, better informed
by insights from a range of stakeholders and scholars. The recent literature in
sustainability science and related fields suggests that the knowledge that results
from interactions such as these is likely to contribute to better outcomes, especially
the collaborative work with knowledge users continues as the scientists do their
work (Cash et al. 2003; Cash et al. 2006; Clark et al. 2006; SPARC 2010).
In what superficially appears to be a similar process, the National Oceanic and
Atmospheric Administration (NOAA) in the United States solicited ideas from a
range of stakeholders and from technical documents in order to compile an
analogous research priority document (National Oceanic and Atmospheric Admin-
istration 2007a, b). The parties contributing suggestions of national research
priorities, however, never met to discuss their suggestions, and thus the resulting
document reads as if it is a comprehensive list of all possible research questions that
some scientist somewhere might want to address. The document provides scant help
to researchers hoping to identify priority questions, and the process likely did little
to stem the path dependence problems I identify in this paper.
Formatted properly, these forums can improve research priority setting, but
without additional work they do not ensure that researchers – who, as described in
the Introduction, have a fair degree of autonomy – will direct their attention to those
priorities. A further complication for a globalized science such as coral reef
conservation biology is that some research priorities may be relevant over large
geographic scales whereas others might be incredibly important, but for only a
localized region. Both of the above suggest that discipline-wide or nation-wide
prioritization exercises, while necessary, are insufficient. For coral reef conservation
science to contribute most meaningfully to better outcomes, the interactions I
advocate in this paper must also occur on smaller scales as well, through on-going
collaborations between scientists, knowledge users, and other relevant parties.
There is an effective model for organizing these ongoing collaborations: Boundary
organizations (Guston 2001; McNie 2007), institutions that occupy the void between
science and policy-making. The formats of these boundary organizations are variable,
but a key characteristic is that successful ones are accountable both to scientists and to
the users of scientific knowledge, and thus have incentive to bridge these worlds with
lasting genuine interchange of ideas. Guston (2001) describes a number of boundary
organizations, and a thriving literature on their application to environmental science
and policy has since emerged (Miller 2001; Cash et al. 2003; e.g., Cash et al. 2006; van
Kerkhoff and Lebel 2006). Scholars have documented the characteristics that make
these organizations successful at linking science to decision making, and at least one
recent paper has begun to identify changes within science funding and research
incentive structures that can facilitate such exchanges (Matso et al. 2008).
As the findings of the current study suggest, experimentation with boundary
organizations and other approaches to rethinking processes of research effort
232 M. W. Neff
123
prioritization may enhance the ability of science to contribute to improved outcomes.
Solving problems is, after all, the motivation of the participants in this study and many
of the sponsors of research.
Acknowledgments With thanks to D. Sarewitz, D. Guston, E. Corley, C. Miller, A. Smith, and A. Kinzig
for critical feedback on this research, and L. Hidinger and K. Darby for assistance with data collection. I
am also indebted to Editors Weingart and Taubert, as well as multiple reviewers for their insightful
suggestions. This material is based on work supported by the National Science Foundation (NSF) under
grants #0345604 and #0504248. Any opinions, findings, conclusions, and/or recommendations expressed
in this material are those of the author and do not necessarily reflect the views of the NSF.
References
Addams, Helen, and John Proops. 2000. Social discourse and environmental policy: An application of Q
methodology. Northampton, MA: Edward Elgar Publishing.
Aronson, Richard B., et al. 2003. Causes of coral reef degradation. Science 302: 1502–1504.
Australian Institute of Marine Science. 2007. Australian Institute of Marine Science Research Plan
2007–2011. Townsville, Queensland, Australia: AIMS.
Bischof, Barbel G. 2010. Negotiating uncertainty: Framing attitudes, prioritizing issues, and finding
consensus in the coral reef environment management ‘‘crisis’’. Ocean & Coastal Management 53:
597–614. doi:10.1016/j.ocecoaman.2010.06.020.
Bozeman, Barry, and Daniel Sarewitz. 2011. Public value mapping and science policy evaluation.
Minerva 49(1): 1–23. doi:10.1007/s11024-011-9161-7.
Braun, Dietmar. 1993. Who governs intermediary agencies? Principal-agent relations in research policy-
making. Journal of Public Policy 13: 135–162.
Braun, Dietmar, and David H. Guston. 2003. Principal-agent theory and research policy: An introduction.
Science and Public Policy 30: 302–308.
Brown, Steven R. 1980. Political subjectivity: Applications of Q methodology in political science. New
Haven, CT: Yale University Press.
Brown, Steven R. 1989. A feeling for the organism: Understanding and interpreting political subjectivity.
Operant Subjectivity 12: 81–97.
Brown, Steven R. 1996. Q methodology and qualitative research. Qualitative Health Research 6:
561–567.
Brown, Steven R. 1998. Autobiography and problem selection. Society for Policy Sciences conference,
Yale University School of Law. http://facstaff.uww.edu/cottlec/QArchive/Yale98.html.
Buddemeier, Robert W., Joan A. Kleypas, and Richard B. Aranson. 2004. Coral reefs and climate
change: Potential contributions of climate change to stresses on coral reef ecosystems. Washington,
DC: Pew Center on Global Climate Change.
Burke, Lauretta, and Jonathan Maidens. 2004. Reefs at risk in the Caribbean. Washington, DC: World
Resources Institute.
Busch, Lawrence, William B. Lacy, and Carolyn Sachs. 1983. Perceived criteria for research problem
choice in the agricultural sciences—A research note. Social Forces 62: 190–200.
Callon, Michel, John Law, and Arie Rip. 1986. Mapping the dynamics of science and technology:
Sociology of science in the real world. London: Macmillan.
Campbell, Andrea Louise. 2003. How policies make citizens: Senior political activism and the American
welfare state. Princeton, NJ: Princeton University Press.
Carayol, Nicolas, and Jean-Michel Dalle. 2007. Sequential problem choice and the reward system in open
science. Structural Change and Economic Dynamics 18: 167–191.
Cash, David W., Jonathan C. Borck, and Anthony G. Patt. 2006. Countering the loading-dock approach to
linking science and decision making. Science, Technology & Human Values 31: 465–494.
Cash, David W., et al. 2003. Knowledge systems for sustainable development. Proceedings of the
National Academy of Sciences 100: 8086.
Clark, William C., Laura Holliday, and National Research Council (U.S.). 2006. Linking knowledge with
action for sustainable development the role of program management: Summary of a workshop.
Washington, DC: National Academies Press.
Research Prioritization 233
123
Davies, B.B., and I.D. Hodge. 2007. Exploring environmental perspectives in lowland agriculture: A Q
methodology study in East Anglia, UK. Ecological Economics 61: 323–333.
Debackere, Koenraad, and Michael A. Rappa. 1994. Institutional variations in problem choice and
persistence among scientists in an emerging field. Research Policy 23: 425–441.
Giddens, Anthony. 1984. The constitution of society: Outline of the theory of structuration. Berkeley, CA:
University of California Press.
Gieryn, Thomas F. 1978. Problem retention and problem change in science. Sociological Inquiry 48: 96.
Glaser, Barney G., and Anselm L. Strauss. 1967. The discovery of grounded theory: Strategies for
qualitative research. New York: Aldine de Gruyter.
Guston, David H. 1996. Principal-agent theory and the structure of science policy. Science and Public
Policy 23: 229–240.
Guston, David H. 2001. Boundary organizations in environmental policy and science: An introduction.
Science, Technology, and Human Values 26: 399–408.
Guston, David H. 2004. Forget politicizing science. Let’s democratize science! Issues in Science and
Technology 21: 25–28.
Hacking, Ian. 1999. The social construction of what? Cambridge, MA: Harvard.
Hall, Clare. 2008. Identifying farmer attitudes towards genetically modified (GM) crops in Scotland: Are
they pro- or anti-GM? Geoforum 39: 204–212.
Halpern, B.S., et al. 2008. A global map of human impact on marine ecosystems. Science 319: 948–952.
Hughes, T.P., et al. 2003. Climate change, human impacts, and the resilience of coral reefs. Science 301:
929–933.
Kingdon, John W. 1984. Agendas, alternatives, and public policies. New York: Harper Collins.
Kitcher, Philip. 2001. Science, truth, and democracy. New York: Oxford University Press.
Kitcher, Philip. 2003. What kinds of science should be done. In Living with the genie, eds. Alan
Lightman, Daniel Sarewitz, and Christina Desser, 201–224. DC: Island Press.
Kleypas, Joan A., and C. Mark Eakin. 2007. Scientists’ perceptions of threats to coral reefs: Results of a
survey of coral reef researchers. Bulletin of Marine Science 80: 419–436.
Kuhn, Thomas S. 1996. The structure of scientific revolutions, 3rd ed. Chicago: University of Chicago
Press.
Lasswell, Harold D., and Myres S. McDougal. 1992. Jurisprudence for a free society: Studies in law,
science, and policy. The New Haven Studies in International Law and World Public Order.
Dordrecht, Netherlands; Boston: M. Nijhoff.
Latour, Bruno. 2004. Politics of nature. Cambridge, MA: Harvard University Press.
Matso, Kalle E., et al. 2008. Establishing a minimum standard for collaborative research in federal
environmental agencies. Integrated Environmental Assessment and Management 4: 362–368.
doi:10.1897/IEAM_2007-070.1.
Mattson, David, Susan G. Clark, Kimberly L. Byrd, Steven R. Brown, and Bart Robinson. 2011. Leaders’
perspectives in the Yellowstone to Yukon Conservation Initiative. Policy Sciences 44: 103–133.
doi:10.1007/s11077-011-9127-5.
Mattson, David J., Kimberly L. Byrd, Murray B. Rutherford, Steven R. Brown, and Timothy W. Clark.
2006. Finding common ground in large carnivore conservation: Mapping contending perspectives.
Environmental Science & Policy 9: 392–405. doi:10.1016/j.envsci.2006.01.005.
McKeown, Bruce, and Dan Thomas. 2013. Q methodology, 2nd ed. Quantitative Applications in the
Social Sciences 66. Thousand Oaks, CA: SAGE.
McNie, Elizabeth C. 2007. Reconciling the supply of scientific information with user demands: An
analysis of the problem and review of the literature. Environmental Science & Policy 10: 17–38.
Merton, Robert K. 1938. Science, technology and society in seventeenth century England. Osiris 4:
360–632.
Miller, Clark. 2001. Hybrid management: Boundary organizations, science policy, and environmental
governance in the climate regime. Science, Technology & Human Values 26: 478–500.
Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: How scientists
shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva
51(3): 295–315. doi:10.1007/s11024-013-9234-x.
National Oceanic and Atmospheric Administration. 2007a. Coral reef ecosystem research plan (Part I:
National research priorities). Silver Spring, MD, USA: National Oceanic and Atmospheric
Administration.
234 M. W. Neff
123
National Oceanic and Atmospheric Administration. 2007b. Coral reef ecosystem research plan (Part II:
Regional research priorities-jurisdiction wide). Silver Spring, MD, USA: National Oceanic and
Atmospheric Administration.
Neff, Mark W. 2011. What research should be done and why? Four competing visions among ecologists.
Frontiers in Ecology and the Environment 9: 462–469. doi:10.1890/100035.
Neff, Mark W., and Elizabeth Corley. 2009. 35 years and 160,000 articles: A bibliometric exploration of
the evolution of ecology. Scientometrics 80: 657–682.
Neff, Mark W., and Brendon M. H. Larson. 2014. Scientists, managers, and assisted colonization: Four
contrasting perspectives entangle science and policy. Biological Conservation 172: 1–7. doi:10.
1016/j.biocon.2014.02.001.
Niemeyer, Simon, Judith Petts, and Kersty Hobson. 2005. Rapid climate change and society: Assessing
responses and thresholds. Risk Analysis 25: 1443–1456.
North, Douglass C. 1990. Institutions, institutional change, and economic performance. Cambridge; New
York: Cambridge University Press.
Pandolfi, J.M., et al. 2003. Global trajectories of the long-term decline of coral reef ecosystems. Science
301: 955–958.
Robbins, Paul, and Rob Krueger. 2000. Beyond bias? The promise and limits of Q method in human
geography. Professional Geographer 52: 636.
Sandbrook, Chris, Ivan R. Scales, Bhaskar Vira, and William M. Adams. 2011. Value plurality among
conservation professionals. Conservation Biology 25: 285–294. doi:10.1111/j.1523-1739.2010.
01592.x.
Shove, Elizabeth. 2003. Principals, agents and research programmes. Science and Public Policy 30:
371–381. doi:10.3152/147154303781780308.
SPARC. 2010. Usable science: A handbook for science policy decision makers. http://cstpr.colorado.edu/
sparc/outreach/sparc_handbook/index.html. Accessed 26 Aug 2011.
Van Kerkhoff, Lorrae, and Louis Lebel. 2006. Linking knowledge and action for sustainable
development. Annual Review of Environment and Resources 31: 445–477. doi:10.1146/annurev.
energy.31.102405.170850.
Webler, Thomas, S. Danielson, and S. Tuler. 2009. Using Q method to reveal social perspectives in
environmental research. Greenfield, MA: Social and Environmental Research Institute.
Wilkinson, Clive. 2004. Status of coral reefs of the world: 2004. Townsville, Queensland, Australia:
Global Coral Reef Monitoring Network & Australian Institute of Marine Science.
Woolley, John T., and Michael Vincent McGinnis. 2008. The conflicting discourses of restoration.
Society & Natural Resources 13: 339–357.
Wynne, Brian. 1989. Sheep farming after Chernobyl: A case study in communicating scientific
information. Environment 31: 10–39.
Ziman, John M. 1981. What are the options? Social determinants of personal research plans. Minerva 19:
1–42.
Ziman, John M. 1987. The problem of ‘‘problem choice’’. Minerva 25: 92–106.
Zuckerman, Harriet. 1978. Theory choice and problem choice in science. Sociological Inquiry 48: 65.
Zuckerman, Harriet. 1989. The other Merton thesis. Science in Context 3: 239–267.
Research Prioritization 235
123
