A strategic monitoring approach for learning to improve
Charles B. van Rees
, Darixa D. Hernandez-Abrams
,S. Kyle McKay
, C. Brock Woodson
, Brian McFall
, Safra Altman
,Seth J. Wenger
Odum School of Ecology &River Basin Center, University of Georgia, Athens, GA, United States
U.S. Army Corps of Engineers, Engineer Research and Development Center. Vicksburg, MS, United States
School of Environmental, Civil, Agricultural, and Mechanical Engineering, University of Georgia, Athens, GA, United States
Editor: Prof. Ashantha Goonetilleke Naturalinfrastructure(NI) development,including ecosystem restoration, is an increasinglypopular approachto lever-
age ecosystem services for sustainable development, climate resilience, and biodiversity conservation goals. Although
implementation and planning for these tools is accelerating, there is a critical need for effective post-implementation
monitoring to accumulate performance data and evidence for best practices. The complexity and longertime scales as-
sociatedwith NI, compounded by differing disciplinary deﬁnitions and concepts of monitoring necessitate a deliberate
and strategic approach to monitoring that encompasses different timeframes and objectives. This paper outlines a ty-
pology of monitoring classes differentiated by temporal scale, purpose of data collection, the information beneﬁts of
monitoring, and the responsible party. Next, we provide a framework and practical guidelines for designing monitor-
ing plans for NI around learning objectives. In particular,we emphasize conducting research and development moni-
toring, which provides scientiﬁcally rigorous evidence for methodological improvement beyond the project scale.
Wherever feasible, and where NI tools are relatively new and untested, such monitoring should avoid wasted effort
and ensure progress and reﬁnement of methodology and practice over time. Finally, we propose institutional changes
that would promote greater adoption ofresearch and development monitoring to increase the evidencebase for NI im-
plementation at larger scales.
Water resources management
1.1. The need for monitoring and learning for advancing natural infrastructure
Natural Infrastructure (NI) systems are increasingly recognized as essen-
tial complements to conventional gray infrastructure that provide resilience
under a changing climate in addition to other societal beneﬁts, and are part
of an integrated approach to achieving biodiversity conservation and sustain-
able development goals (Nesshöver et al., 2017;Nelson et al., 2020a, 2020b).
NI (also known as nature-based solutions, nature-based features, Engineering
with Nature®, or Working with Nature) are engineered or restored natural or
semi-natural landscape features that provide key services (e.g., ﬂood risk re-
duction, water puriﬁcation, erosion protection) while providing ancillary
beneﬁts to biodiversity and society including habitat and recreational value
(Bridges et al., 2015;Nelson et al., 2020a, 2020b). For the purposes of this
paper, we include ecological restoration under this deﬁnition, because it sim-
ilarly involves a deliberate manipulation or enhancement of ecosystem ser-
vices or processes and operates on similar levels of ecological complexity
and time scale. This operating deﬁnition also encompasses “green infrastruc-
ture,”although the latter term often refers to a speciﬁcsubsetofNIinurban
systems that provide the services of carbon sequestration, air quality improve-
ment, and especially stormwater management (Grabowski et al., 2022).
NI can be employed in concert with conventional infrastructure to en-
able a self-repairing and self-sustaining system that allows for recovery
after disturbance and adaptation to changing conditions over time. NI is
being implemented worldwide,and is considered a priority part ofsustain-
able development by the European Commission (Maes and Jacobs, 2017;
Nesshöver et al., 2017), United States Army Corps of Engineers (Bridges
et al., 2018, 2021), and international organizations (WWAP/UN-Water
(United Nations World Water Assessment Program), 2018;World Wildlife
Fund (WWF), 2020).
A wide range of NI applications includes increasing water security in
semiarid regions (Everard et al., 2020), alleviating ﬂood risks around rivers
(Hartmann et al., 2019), managing urban stormwater for biophysical and
social outcomes (Christman et al., 2018), and preventing coastal erosion
and reducing storm surge risks (Slinger et al., 2021). Two frequently cited
examples of natural infrastructure are restored ﬂoodplain wetlands from
levee setbacks (Guida et al., 2015;Dahl et al., 2017), which reduce ﬂood
risk, and oyster beds and other natural elements of “living shorelines”
that protect coastal communities and infrastructure from the effects of
storm surge (Bridges et al., 2018). Urban forests are also considered natural
infrastructure thatbuffer vulnerable communities from poor air quality and
heat island effects while reducing storm water runoff (Kowarik et al.,
2019). Many NI projects are considered “win-wins”for society and
Science of the Total Environment 832 (2022) 155078
Received 14January 2022; Received inrevised form 11 March 2022; Accepted 3 April 2022
Available online xxxx
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
biodiversity because they generatefunctional habitat while mitigating risks
associated with urbanization and climate change (Chausson et al., 2020;
Girardin et al., 2021).
Despite the expanding application of NI, the research and evidence
around NI performance and service delivery required to improve imple-
mentation in practice remain limited (Nesshöver et al., 2017;Nelson
et al., 2020a;Albert et al., 2021;B ridges et al., 2021). There is a need to bet-
ter understand NI methods themselves as well as their interactions with
conventional infrastructure, and their role in enhancing the longevity and
resilience of conventional infrastructure remains insufﬁciently explored.
As with ecological restorations more broadly, developing an effective, ap-
plied understanding requires strategic monitoring of project outcomes
over time and space (Stein and Bledsoe, 2013;Delaware Living Shorelines
Committee, 2018;Davis et al., 2022). However, monitoring for learning
is uncommon in many environmental management ﬁelds due to potentially
high costs and long time scales (Bernhardt et al., 2007;Grantham et al.,
2010;Wijsman et al., 2021). Knowledge acquired through thistype of mon-
itoring would not only improve NI implementation, but would also contrib-
ute to a broader understanding of the role of restored and artiﬁcial
ecosystems to sustainability science (Yu et al., 2021).
Rigorous monitoring of NI presents additional challenges beyond con-
ventional infrastructure. Gray infrastructure is often designed with static
structural properties and performance objectives in mind, whereas natural
or nature-based systems can and should change through time as they inte-
grate with surrounding ecological systems and adjust to prevailing environ-
mental conditions (Chausson et al., 2020;Seddon et al., 2020). This
dynamism, combined with the inherent uncertainty in environmental man-
agement outcomes (Stein and Bledsoe, 2013;Galatowitsch and Bohnen,
2021) can lead to signiﬁcant time lags between NI implementation and de-
sired results as well as ambiguity about how and what to monitor (Giordano
et al., 2020). The dynamic characteristics of NI may necessitate more
frequent and more adaptable monitoring than conventional gray
The breadth of goals for which NI is designed and implemented also
greatly multiplies the number of potential monitoring objectives and indi-
cators. Ideally, these will include both lagging and leading (sensu Ota
et al., 2021) indicators that give both a retrospective and predictive view
of changes in system behavior. These should give useful information on
the achievement of project outcomes across realmsof beneﬁts (social, envi-
ronmental, economic), and which are applied using a standardized frame-
work that allows comparison and synthesis across projects and studies
(Eger et al., 2022). The multidisciplinary collaborations necessary for suc-
cessful NI implementation also complicates monitoring because of the po-
tentially conﬂicting terminology, deﬁnitions, and operational concepts
among scientists, engineers, and project funders (Davis et al., 2022). Fur-
thermore, natural systems are themselves inherently complex, displaying
potentially unpredictable behavior (Kay et al., 1999), which adds a high de-
gree of uncertainty to project outcomes and thus increases the number of
appropriate monitoring indicators (Linkov et al., 2006). This uncertainty
in ecological outcomes is further exacerbated by ongoing and accelerating
climate change (Simonson et al., 2021). Consequently, incorporation of
NI into the societal and geophysical systems of civil engineering has been
described as a ‘wicked’challenge necessitating multi- and transdisciplinary
collaboration (Nesshöver et al., 2017).
Where monitoring is not carried out in a deliberate and organized
fashion with the goal of improving future implementations, methods
will fail to provide effective or reliable results for supporting decision-
making or management (Webb et al., 2017). The ﬁeld of stream restora-
tion presents a cautionary example of the risk of widespread adoption of
interventions without a deliberate approach to assessing their efﬁcacy
and to improving practices over time (Downs and Kondolf, 2002). De-
spite billions of US dollars in investment, stream and wetland restora-
tion practices have largely failed to achieve their objectives (Wohl
et al., 2005;Bernhardt et al., 2007). Restoration methods became
“projectiﬁed”(sensu Hodge and Adams, 2016) and entrenched in prac-
tice, preventing adaptive management or reﬁnement of methods from
past experience (Galatowitsch and Bohnen, 2021). Once established as
an industry, this trajectory became difﬁcult to change, as actors had a
vested interest in assuming success rather than spending funds on mon-
itoring programs that risked demonstrating inefﬁcacy (Lave and Doyle,
2021). Despite repeated calls for monitoring and project evaluation,
these are still rarely implemented in restoration practice (Rubin et al.,
As a relatively new form of ecological engineering, contemporary NI
practice has limited monitoring data and a high need for evidence
(Bernhardt et al., 2005;Palmer et al., 2007;Rubin et al., 2017;Davies
et al., 2021). The massive, planned expenditures on global infrastructure,
amounting to tens of trillions of US dollars as of this writing, have the po-
tential to fuel extensive new implementation of NI projects. This offers an
unprecedented opportunity for learning that should not be wasted. There
is also the major risk of broader NI methods becoming entrenched and inef-
fective as stream and wetland restoration methods were inthe past. This en-
trenchment could lead to an inefﬁcient use of these crucial and timely
resources and a failure to address the ongoing biodiversity crisis and
work toward the sustainable development goals.
Given the multitude of potential motivations, beneﬁts of monitoring,
and potentially high costs of NI, a deliberate and strategicapproach to mon-
itoring design is needed (McDonald-Madden et al., 2010;Galatowitsch and
Bohnen, 2021). Although a framework was recently developed for evaluat-
ing NI prior to implementation (i.e., selecting projects based on projected
beneﬁts; Sowińska-Świerkosz and García, 2021), no such generalized con-
ceptual support exists for post-implementation monitoring of NI for re-
search and learning purposes.
Overcoming the challenges of monitoring NI requires a conceptual
guide thatcontextualizesand integrates various forms ofmonitoring and fa-
cilitates monitoring for learning and improvement. Monitoring protocols
must be sufﬁciently ﬂexible and adaptable to accommodate natural com-
plexity as well as constraints related to cost and responsibility, while per-
mitting the collection of meaningful data for institutional and disciplinary
learning. A strategic approach to monitoring should monitor biophysical
and socio-economic parameters that are feasible to collect and fund but
also provide information that beneﬁts long term learning (Wijsman et al.,
2021). In this discussion paper, we present a strategic framework for devel-
oping monitoring plans for NI, ecological restoration programs, and related
projects, and provide practical guidance for collecting evidence for NI re-
search and development through systematic monitoring. We ﬁrst present
an inclusive typology of monitoring approaches across disciplines, then
provide guidance on whether and how to implement monitoring for re-
search, methodological development, and institutional or disciplinary
learning. Because the speciﬁc choice of monitoring indicators will vary
greatly across study systems, projects, and monitoring types, our goal is to
encourage an explicit and organized accounting of monitoring goals for
NI projects, rather than to provide speciﬁc guidance on indicators and
This content is intended to provide ﬂexible,practical guidance for inter-
disciplinary working groups todesign effective and informative monitoring
plans for natural infrastructure projects by providing a working vocabulary
of monitoring types, and taking a deliberate step back to develop monitor-
ing plans that avoid missed opportunities for learning, and cultivate a com-
munity of practice to promote NI research and development. Our
framework is directed speciﬁcally toward a multidisciplinary audience in-
cluding infrastructure planners, project engineers, landscape architects, en-
vironmental professionals, decision-makers and project managers, as well
as restoration ecologists and conservation scientists engaged in natural in-
frastructure research and development. Where other recent work on moni-
toring ecological restorations provides general insights on the selection of
indicators and workﬂows for adaptive management (Galatowitsch and
Bohnen, 2021;Ota et al., 2021;Eger et al., 2022), we cater this paper to
the broader, more interdisciplinary audience of practitioners and re-
searchers involved in natural infrastructure projects. We expand upon pre-
vious frameworks by providing a guide for learning to improve natural
infrastructure practice over time.
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
2. A typology for NI and restoration project monitoring
For the purposes of this paper, we deﬁne monitoring as the collection of
empirical data (qualitative or quantitative) on the past and present condi-
tion of an ecological or infrastructure system, usually through repeated ob-
servations, using in-situ sampling and/or remote sensing. We treat this as
distinct from forecasting future conditions (i.e., modeling). Monitoring is
undertaken to accomplish a variety of practical goals and plays an essential
role in formal decision making (e.g., Multi-Criteria Decision Analysis), pro-
ject compliance, adaptive management, and collecting evidence for learn-
ing (Lyons et al., 2008;Stein et al., 2012;Marttunen et al., 2019). Much
of the time, however, planners and practitioners are not explicit about the
purpose, context and expectations of the monitoring strategies associated
with a project.
Monitoring plan design begins with identifying monitoring objectives,
which themselves stem from the intendedoutcomes of a project (end-points
or management objectives) and the priorities and values of the project ac-
tors and stakeholders (Erwin et al., 2016;Delaware Living Shorelines
Committee, 2018). A strategic approach to setting monitoring objectives
should operate from a clear understanding of different types and purposes
of monitoring, such that monitoring objectives match the questions and in-
terests of project stakeholders. Such an intentional approach is particularly
important for natural infrastructure projects, which require multidisciplin-
ary approaches and therefore run the risk of miscommunications due to in-
consistent terminology and contrasting disciplinary visions for monitoring
goals (Corsair et al., 2009;Lindenmayer and Likens, 2009;Marttunen
et al., 2019;Davies et al., 2021). A hallmark of a strategic approach is an
“openness to learning”(sensu Galatowitsch and Bohnen, 2021)that
marks effective adaptive management and avoids missed opportunities
for learning and improvement.
Here, we provide a classiﬁcation of monitoring typesorganized accord-
ing to the time frame of monitoring activities, the parties typically
responsible for carrying out monitoring, and the scale at which data are
used (e.g., project scale, scale of the project's effect on abroader ecological
or social system, or for broader researchpurposes). We describe three broad
categories of monitoring: initial performance monitoring, long-term perfor-
mance monitoring, and research and development monitoring (hereafter
R&D monitoring), which themselves are based on the purpose or ultimate
objective for which monitoring is undertaken (Fig. 1). The motivations
and objectives of a monitoring plan, as well as intended time-frames and re-
sponsible parties, must be speciﬁed as part of the project design phase. All
three types of monitoring can be conducted onthe same project and use the
same set of indicators or parameters, although alternative indicators oper-
ating at different temporal scales or focusing on different aspects of system
behavioror function may be usedto accomplish different monitoring objec-
tives (Ota et al., 2021). In other words, although distinct objectives are nec-
essary to achieve different types of monitoring goals, the monitored
parameters and indicators may overlap signiﬁcantly. A review of potential
indicators and best practices for monitoring ecosystem condition is beyond
the scope of this paper, and has been effectively presented elsewhere
(e.g., Stein and Bledsoe, 2013;Prach et al., 2019;Science and Resilience
Institute, 2020;Davis et al., 2022;Eger et al., 2022). To further clarify the
differences between these types of monitoring objectives, we list example
questions for each type in Table 1.
The ﬁrst distinction in our typology separates monitoring to evaluate
performance from monitoring for learning. Monitoring to evaluate perfor-
mance involves collecting information about whether a project is meeting
design objectives in order to take management actions accordingly. Project
objectives could include the efﬁcacy in delivery of expected beneﬁts and
values, the response to perturbations in the system (e.g., resistance, resil-
ience), the need for maintenance, adjustment, or adaptive management,
or changes in beneﬁt delivery across time. For NI approaches, performance
may also be based on how well a design replicates or behaves likea natural
or reference system of interest.Conversely, NI may be expected to produce
Fig. 1. A typology for common monitoring classiﬁcations used for natural infrastructure and ecological restoration organized by the purpose of monitoring, the scale of
beneﬁts accrued, the typical time frame, and the responsible party. The time scales suggested here are based on what is typically observed in the ﬁeld, and are in no way
prescriptive; actual monitoring time scales will depend on monitoring goals, chosen indicators, and the nature of the project.
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
speciﬁc beneﬁts in excess of those expected from natural systems
(e.g., treatment wetland designed for higher rates of denitriﬁcation than
those observed in natural systems). These ﬁner distinctions are highly de-
pendent on the type of project (e.g., conventional or gray infrastructure,
restoration, natural infrastructure) and its landscape context, so we com-
bine these endpoints under a broader category of performance.
The highly dynamic nature of NI noted above implies that performance
must be assessed through time, since the design will be evolving and
adapting along with the dynamics of the surrounding system. Similarly,
where there is signiﬁcant uncertainty about whether a NI project will
meet its design objectives, these monitoring efforts can effectively be mon-
itoring for learning (see Research and Development monitoring below). Ac-
cordingly, the categories provided below are not necessarily mutually
exclusive, but provide the conceptual basis for a more explicit and struc-
tured approach to NI monitoring, with a particular emphasis on monitoring
for interdisciplinary learning.
We further divide performance monitoring into initial performance
monitoring and long-term performance monitoring according to the time
scale of monitoring practice. The objective of initial performance monitoring
is the short-term veriﬁcation that a project meets design speciﬁcations or is
affecting a given system attribute as intended in the management objec-
tives. Based on various deﬁnitions of performance, other authors have
called this implementation, validation or effectiveness monitoring, in that
it focuses on short-term metrics of project performance and impact (Stein
and Bledsoe, 2013;Roni and Beechie, 2012;Theiling et al., 2015;
Marttunen et al., 2019). Initial performance monitoring is differentiated
from otherforms of monitoring inour typology by its timeframe: typically,
one to ﬁve years after project completion. This timeframe is descriptive and
approximate: that is, the time scales associated with initial performance
monitoring are thosethat we typically associate with these sorts of monitor-
ing questions (Table 1), and not a requirement for this type of monitoring.
The actual time scale of any type of monitoring will depend on monitoring
objectives, indicators, and the time scale at which the planned NI project
operates and performs its desired functions.
Post-project inspections, in which the structural elements of a project
are compared to the initial design goals (e.g., “as builts”), also fall under
this deﬁnition. Initial performance monitoring can include examining the
condition of structural or physical elements of the project, or ecosystem
endpoints that the project is intended to affect. The permittees or actors re-
sponsible for project construction are typically responsible for this type of
monitoring. This category of monitoring is most often legally mandated
by funding or governmental bodies (see Compliance monitoring below).
Consequently, monitoring activities are typically more intense at the early
stages of project implementation, with a higher frequency of data collection
on a wider range of variables.
Long-term performance monitoring is the ongoing assessment of longer-
term (often more than ﬁve years beyond project completion) aspects of pro-
ject performance relative to planned objectives or desired conditions. This
category encompasses outcome monitoring, trends assessment, and
surveillance monitoring (Bernhardt et al., 2007;Stein and Bledsoe, 2013;
Vugteveen et al., 2014) in that it focuses on processes and longer-term end-
points as affected by the project through time. Repeated monitoring prac-
tices like condition monitoring, structural health monitoring, and asset
management that assess the structural elements of projects for signs of
long-term degradation or potential failure (e.g., Brownjohn, 2007)also
fall within this category. This type of monitoring is done to ascertain the
need for repair, replacement, or adaptive management. Long-term perfor-
mance monitoring also includes evaluating the achievement of process-
based objectives like the maintenance or provision of key ecosystem func-
tions or services (beneﬁts and values), and assessing how the design re-
sponds to changes and perturbations in the system.
Long-term performance monitoring is particularly important for NI,
where short-term evaluations will likely fail to capture whether key bio-
physical processes were re-established over ecological time scales, missing
trajectories of development and self-organization and leading to a prema-
ture designation of success (Herrick et al., 2006). Long-term performance
monitoring is repeated on a consistent interval (e.g., every one to three
years) to prevent catastrophic failures and gain insight into project perfor-
mance over time (National Research Council, 1995). The iterative nature
of long-term performance monitoring makes it highly relevant to adaptive
management and reﬁnement at the single-project scale (passive learning,
sensu Grantham et al., 2010). The application of monitoring data beyond
the present system is typically limited because this type of monitoring is
not hypothesis-driven and does not necessarily include replication and con-
trols (but see Research and Development monitoring, next section). Because
of the longer time frameassociated with this type of monitoring,a different
agency is often responsible for this monitoring than the one responsible for
construction. Notably, long-term performance monitoring data can be use-
ful for learning if carried out rigorously in systems on whichlittle empirical
research has been carried out previously.
Research and Development monitoring is undertaken with the explicit goal
of testing hypotheses on project performance in a way that is falsiﬁable,
controlled, and repeatable (i.e., scientiﬁc) such thatnew knowledge and ev-
idence are accumulatedto improve project implementation beyond the cur-
rent project system. This category encompasses similar monitoring
strategies like investigative monitoring, monitoring for learning, and mon-
itoring forscience and management understanding (Suding, 2011;Friberg
et al., 2016;Delaware Living Shorelines Committee, 2018;Weber et al.,
2018). The goal of R&D monitoring is to provide an evidence base that is
useful for learning at an (inter)disciplinary scale; this is also called social
learning (Grantham et al., 2010). R&D monitoring is the informational
foundation of an evidence-based approach to NI projects, and an important
part of improving and encouraging their implementation for climate adap-
tation, biodiversity conservation, and other beneﬁts (Nassauer and Opdam,
2008;Albert et al., 2021).
In R&D monitoring, practitioners view a project as an experiment from
which stakeholders, managers, and researchers can learn more about how
complex systems function in order to improve design and implementation
in the future. This is parallel to Grantham et al. (2010)’sconceptofactive
learning or active adaptive management, and involves setting speciﬁc
learning objectives that correspond to research questions and focus moni-
toring on key knowledge gaps (Marttunen et al., 2019). Investment in
R&D monitoring can help guide the managementof a project based on pres-
ent observations (active adaptive management), or inform, improve, or re-
ﬁne future projects by contributing to an evidence base (Kondolf, 1995;
Bernhardt et al., 2005). The difference between these two applications of
R&D monitoring is the scale; the former type pertains only to the manage-
ment of a particular project, while the latter may be applied to future or on-
going projects elsewhere. For R&D monitoring data to be applicable
beyond adaptive management of the project, a higher degree of methodo-
logical rigor is required.
R&D monitoring requires the delineation and recognition of explicit hy-
potheses or learning outcomes, and should be implemented with replica-
tion and controls or counterfactuals that are sufﬁcient for hypothesis
testing. The indicators and scale of this type of monitoring depend on the
Examples of questions among different types of monitoring within the typology
outlined in this paper.
Monitoring type Example questions
-Was the infrastructure constructed as designed and planned?
-Is the project performing as expected?
-How is project performance changing across time?
-Are ecological, geological, hydrological or other dynamics
behaving as expected?
-How is the project affecting other aspects of the surrounding
-What about this project worked well and could improve future
-How effective is the implemented method at achieving the
-How can future projects be improved?
-What mechanisms led to project performance or lack thereof?
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
research question —that is, what the resource managers, decision-makers,
research team, and other stakeholders desire to learn and improve in the fu-
ture. The need for replication, controls, and an explicit experimental design
potentially involve non-trivial expense and labor to be included in monitor-
ing design. This can involve studying multiple sites, including controls
where no action is taken, and reference sites where, for example, ecologi-
cally intact systems serve as a baseline for comparison (Delaware Living
Shorelines Committee, 2018). Before-After-Control-Impact (BACI) designs,
already applied in the context of ecological restorations (e.g., Block et al.,
2001;Geist and Hawkins, 2016;Muller et al., 2016), are highly amenable
to implementing R&D monitoring for learning. In particular, the deliberate
collection of pre-implementation monitoring is essential for comparison
and ascertaining project efﬁcacy.
The degree to which R&D monitoring is necessary may depend on the
responsible party's perception of the potential for learning or the value of
generated knowledge to the broader management community. Highly ex-
perimental projects using untested or poorly understood methods may rep-
resent a large opportunity for learning, and thus may require extensive
monitoring. Conversely, an iteration of a standard design might necessitate
only sufﬁcient monitoring for adaptive management, or as necessary to sat-
isfy other monitoring needs (see above). As with long-term performance
monitoring, the responsibility for R&D monitoring may fall to an agency
other than the one responsible for initial project construction. The knowl-
edge generated by R&D monitoring is a “public good,”and often these
forms of monitoring fall under government and non-proﬁt-led efforts.
Two additional secondary characteristics of monitoring plans, which in
other frameworks have been treated as their own types of monitoring, are
also worth mentioning to conceptually reconcile them with this typology.
We consider both of these categories as characteristics of monitoring activ-
ities, and not types of monitoring intheir own right given that their spatio-
temporal scale and requirements can vary depending on the project, and
could thus overlap with any of the three monitoring types outlined above.
In other words, they are orthogonal to short- and long-term performance
and R&D monitoring, and could be associated with a monitoring plan
that was structured according to any of those three monitoring types.
Compliance monitoring refers to any monitoring practice that is man-
dated by law or policy (e.g., monitoring endangered species populations
under the US Endangered Species Act; Malcom et al., 2017)orbythepro-
ject funding agency. Any of the preceding three types of monitoring could
have a component of compliance, although most compliance monitoring
is focusedon project implementation (e.g., endangeredspecies “take”dur-
ing construction), post-project inspections (initial performance monitoring)
and condition and failure risk monitoring (long-term performance monitor-
ing). Stakeholder engagement monitoring involves stakeholders as community
scientists in the collection of monitoring data to develop meaningful partic-
ipatory relationships with those who are affected by the project and its ob-
jectives (Martin and Lyons, 2018). Involving the community in the
collection of monitoring data on a restoration or infrastructure project
can increase project buy-in and legitimacy with the surrounding commu-
nity, facilitate social cohesion, create a sense of place (Russ et al., 2015),
or community of practice (Slough et al., 2021), and provide public educa-
tion opportunities (Huddart et al., 2016;Buntaine et al., 2021). Stakeholder
engagement monitoring is increasingly viewed as an attractive and viable
approach to collecting valuable data while improving knowledge transfer
and transparency in science (Irwin, 2018). Importantly, stakeholder en-
gagement should always be considered an added beneﬁt and not the pri-
mary purpose of monitoring. Involving stakeholders for collecting data
that are trivial or will not be used for adaptive management or disciplinary
development is disingenuous and could reduce trust and legitimacy.
3. General guidelines for monitoring natural infrastructure
Before focusing speciﬁcally on the design and implementation of R&D
monitoring plans, it is valuable to brieﬂy cover a set of considerations
that are applicable to monitoring activities falling under any of the three
categories described above. Concrete recommendations for monitoring
design, indicator selection, and data collection for ecological restorations
and speciﬁc types of NI are covered thoroughly elsewhere (e.g., Eger
et al., 2022;Davis et al., 2022), so we treat these topics brieﬂy here as a pre-
lude to our guiding questions for R&D monitoring and to provide a general
introduction to these topics for readers.
Practitioners should explicitly establish the purpose of monitoring
(i.e., monitoring objectives; what questions monitoring data are intended
to answer) as a ﬁrst step in plan design. Ideally, planners will conduct pro-
ject design and goal-setting using a repeatable and standardized approach
like structured decision making (Kondolf, 1995;Palmer et al., 2007;
Bernhardt and Palmer, 2011). Given the multiple desired outcomes and
co-beneﬁts across multiple sectors (e.g., ecological, hydrological, social)
for which NI are constructed, the direct and comprehensive involvement
of stakeholders in the goal-setting process is essential (Bridges et al.,
2018;Nelson et al., 2020a). Along with stakeholder engagement monitor-
ing, the intentional involvement of stakeholder groups and decision-
makers in this process should manifest as a form of knowledge co-
production (Nel et al., 2015).
Which management and monitoring objectives are considered most im-
portant will depend on the values and needs of stakeholder groups,
decision-makers, planners, and other involved parties. Ideally, the process
of stakeholder consultation and other stages of the broader co-production
process will involve the elucidation, communication, and reconciliation of
these different values to ﬁnd common ground and acceptable compromises
(Nel et al., 2015). This process is beyond the scope of this paper, but has
been addressed elsewhere in the environmental management literature
(e.g., Couix and Gonzalo-Turpin, 2015), especially in Integrated Water Re-
sources Management (Agarwal et al., 2000;van Rees et al., 2019).
This participatory process establishes the criteria for monitoring suc-
cess. In other words, the stakeholders, decision-makers, and other actors in-
volved in designing the monitoring plan, in explicitly acknowledging their
goals, also outline what information a successful monitoring plan will yield.
A clear accounting of these priorities and objectives is important not only to
ensure that monitoring delivers the necessary information, but also to en-
able broaderreﬂection on what can, or should, be learnedfrom a given pro-
ject (see next section).
Project planners should also clearly delineate the spatial and jurisdic-
tional scale of monitoring activities according to these objectives. For exam-
ple, compliance monitoring may focus on structural aspects of a project at
the sub-site scale, while performance monitoring questions might encom-
pass an entire site (e.g., a section of coast for a living shoreline). Learning
objectives for R&D (see next section) monitoring may extend well beyond
the site scale to understanding broader landscape or watershed dynamics.
Ultimately, the spatial scale is determined by the monitoring information
needed to assess performance or compliance, or elucidate key dynamics
for adaptive management or broader learning. These monitoring objectives
are in turn dictated by the values and needs of planners, stakeholders, and
other involved parties.
4. Guiding questions for research and design monitoring of natural
Given the need for an evidence base for the efﬁcacy of NI implementa-
tion, we present a set of guidelines for integrating R&D monitoring into
monitoring plans for such projects (Fig. 2). Most existing guidance for mon-
itoring environmental management projects is organized into questions
that can be asked at various stages of monitoring (Stein et al., 2012;
Delaware Living Shorelines Committee, 2018). We use this format to ex-
tend existing frameworks with ﬁve questions for planning R&D monitoring.
Althoughthey are organized to guide new monitoring plans, many of these
considerations are also applicable to the modiﬁcation and implementation
of existing monitoring plans.
Our framework assumes that management and monitoring objectives
have already been clearly delineated based on an analysis of potential ben-
eﬁts and constraints (e.g., as in Sowińska-Świerkosz and García, 2021).
Among monitoring objectives, wefocus speciﬁcally on learning objectives,
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
which are those objectives associated with the creation of new knowledge
via R&D monitoring. This framework complements and broadens other
published frameworks that provide more general guidance on setting mon-
itoring objectives for living shorelines (Delaware Living Shorelines
Committee, 2018;Science and Resilience Institute, 2020)and
hydromodiﬁcation monitoring (Stein et al., 2012;Stein and Bledsoe,2013).
For practitioners and resource managers, this process begins with
envisioning the idealized outcome of R&D monitoring within a project
from the perspective of multiple stakeholders, and then allowing realistic
constraints to determine what portions of this idealized vision can be imple-
mented. This order of operations is intentional; it promotes the collection of
a broad range of potential monitoring objectives for R&Dﬁrst, followed by
elimination steps based around existing constraintsand resources, thus of-
fering transparency in the selection of monitoring objectives and their cor-
responding metrics. An additional advantage of this approach is that it
prevents the premature elimination of learning objectives that might have
been feasible given additionalresources and added value from other stake-
As an additional note, we recommend carefully documenting decisions
made in eachstep of this framework to archivethe rationale and logic of the
planned monitoring strategy. The motivations for particular monitoring ap-
proaches are often lost over the long monitoring windows typical of R&D
monitoring (e.g., 10–20 years).
Question 1: What can be learned from monitoring this project?
Strategic monitoring begins with a careful articulation of monitoring
objectives, which can fall into any of the classes of monitoring discussed
above (e.g., compliance, short-term performance), but we focus here on
the selection of learning objectives for R&D. The planning stage should
begin with a consideration of what could begained through R&Dmonitor-
ing. Two major factors worth examining are whether the system to be stud-
ied or methods to be employed are well-established or relatively new, and
whether there are signiﬁcant uncertainties around project performance
that could provide information for future projects (Stein and Bledsoe,
2013;Delaware Living Shoreline Committee, 2018). The existence of re-
lated projects or initiatives, for which data from the present project could
be useful, may offer additional impetus for R&D monitoring, and could
help establish new collaborations or avenues to support monitoring efforts.
For example, restoration efforts by the U.S. Army Corps of Engineers in the
Upper Mississippi River System are collectively monitoredby a cooperative
program involving the U.S. Geological Survey, U.S. Fish and Wildlife Ser-
vice, U.S. Environmental Protection Agency and several others.
Stakeholder engagement in the form of transdisciplinary collaboration
and knowledge co-creation is askey a part of planningmonitoring for learn-
ing as it is for setting management goals and performance metrics. Co-
production and stakeholder participation may reveal learning opportunities
and resources that had not been integrated into the project at its outset
(Wijsman et al., 2021). Academic researchers can beneﬁt from such collab-
orations by learning about important research applications and new ques-
tions that they would not have encountered otherwise. The input of
community stakeholders, including non-proﬁt organizations and local
governing bodies, is critical for prioritizing services provided by projects
with multiple objectives because monitoring associated with services of
particular local value can attract greater resources and support (Morandi
et al., 2017). Engagement beyond the locale of the project—for example,
with other organizations implementing or researching similar types of pro-
jects—can also be a highly productive activity, allowing resources to be
pooled and research questions and study designs to be harmonized
(Ferraro and Agrawal, 2021). This coordination is further enhanced by
standardized indices and frameworks for data collection, enabling compar-
ison and synthesis of monitoring data among sites across space and time
(Eger et al., 2022), and communities of practice like the European Union's
NetworkNature initiative (www.networknature.eu).
Fig. 2. A workﬂow with guiding questions and actions for designing monitoring plans for research and development monitoring of natural infrastructure and ecological
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
Question2: What dataneed to be collected tomeet learning objectives?
Once a project's monitoring objectives have been explicitly stated, the
next task is to translate these objectives into indicators to be monitored
along with associated protocols. If the project has learning objectives for
R&D, it also becomes necessary to consider the sampling design necessary
to achieve these objectives. Monitoring designs for learning goals include
the use of controls, which may include reference sites or pre-
implementation (e.g., pre-restoration, or pre-construction) data, as well as
replicates for statistical inference (Stammel et al., 2012;Prach et al.,
2019). Power analyses and simulations may be employed to determine
the effect size detectable with a given sampling design (Green, 1989). Sam-
pling designs will likely differ among learning objectives, and should be ad-
dressed separately for each. Once data needs and indicators have been
established, it can be helpful to look for synergies among the data needs
of learning objectives and other monitoring objectives. For example,
water quality data collected forcompliancemonitoring of stormwater con-
trol structures (to evaluate whether these structures are efﬁciently reducing
pollutants and meeting state water quality standards) could also be lever-
aged in building hydrologic models to learn how to maximize pollutant de-
tention capacity through strategic placement of future structures. This
could be done in conjunction with monitoring socioeconomic data such
as recreational and cultural uses of natural wetlands with stormwater con-
trol and retention functions (e.g., van Rees, 2018).
Question 3: What learning objectives can be achieved within project
Logistical constraints are one of the most oft-cited limitations on moni-
toring implementation for river restoration projects (Bernhardt et al., 2007;
Wohl et al., 2015), and are rarely explicitly addressed in the design of mon-
itoring plans (McDonald-Madden et al., 2010). Such constraints, budgetary
or otherwise, are inevitable in practice regardless of initial expectations,
and require realistic consideration. After expansively outlining priorities
and objectives for monitoring, it is necessary to confront the idealized vi-
sion of monitoring outcomes with the realities of institutional mandates,
regulations, and material, ﬁnancial, and human resources (Stein et al.,
2012). Given the longer time frames necessary for R&D monitoring, espe-
cially in ecological and natural infrastructure systems for which nonstation-
ary and long-term changes are common (Lindenmayer et al., 2008;
Stammel et al., 2012), funding support beyond a small initial window
(often on the order of 3–5 years) is frequently not available. Having person-
nel responsible for collecting data and maintaining a database is an espe-
cially costly component of monitoring and could present a major obstacle.
In the following section, we propose broader structural and institutional
changes to help alleviate these constraints.
After constraints have been identiﬁed and outlined, the initial, expan-
sive list of monitoring objectives must be reduced to those that are feasible
within these project boundaries. A key step in this process is to seek oppor-
tunities to leverage other resources, including existing datasets or active
monitoring programs, stakeholder participation, and partnerships with
other organizations. The strategic selection of low-cost monitoring metrics
(i.e., those that involve small or one-time costs that do not accrue substan-
tially over time) can also help maximize limited resources. Importantly, this
step should consider a range of quantitative (e.g., empirical data collec-
tion), semi-quantitative (e.g., scoring systems), and qualitative
(e.g., stakeholder surveys, photographs) monitoring approaches to main-
tain breadth sufﬁciently aligned with project objectives. Remote sensing
technology can provide low-cost and regular surveillance of many habitat
metrics that do not require in situ observations (Konrad et al., 2008). Com-
munity science initiatives, discussed in the next part of the framework, can
also generate valuable data if strategically employed.
Question 4: Are there opportunities for community engagement to
achieve learning objectives?
Community science volunteers can collect large amounts of monitoring
data at very low cost, with the additional advantage of providing an avenue
for outreach and engagement with the public (Edwards et al., 2018;Tulloch
et al., 2013). Public engagement should be an integral part of planning the
design and monitoring of any NI project, but stakeholder involvement in
data collection itself can be a particularly valuable entry point for end-
users and community members.
Opportunities for collaborative data collection can improve community
buy-in and public interest in a project, increase legitimacy and trust, and
contribute to the development of a community of practice (Slough et al.,
2021). Though the effort involved varies based on the monitoring indica-
tors and the system, community scientists have been trained to collect a
wide variety of valuable monitoring data, including estimates of inverte-
brate community composition (Huddart et al., 2016), abundances of kelp
ecosystem functional groups within a restored or protected area (Beas-
Luna et al., 2020), microbial communities in river systems (Hassell et al.,
2018); and debris in the environment (Jambeck and Johnsen, 2015).
Possible opportunities for community scientist participation should be
carefully explored, with a particular focus on the overlap between data util-
ity and the community's interest and willingness to participate. There will
inevitably be some cases or systems in which community science monitor-
ing is not cost effective, feasible, or appropriate, but its potential utility
merits consideration. Conversely, there may be situations where commu-
nity involvement is obligatory (e.g., due to site access, funding streams, or
historical distrust between groups) and cannot be omitted.
Question 5: How will data be managed to promote accessibility for fu-
ture learning and synthesis?
Even large amounts of high-quality monitoring data collected using rig-
orous protocols will make minimal contributions to institutional and disci-
plinary learning unless they are managed in a way that makes them
accessible to researchers during and after project implementation. Monitor-
ing data from individual projects should not only serve to guide adaptive
management and informfuture implementation, but should be made avail-
able for use inmeta-analyses and large data syntheses forlarger-scale infer-
ence. Morandi et al. (2017), in reviewing river restoration projects in
Germany, emphasized that the dissemination of monitoring results is essen-
tial to promoting R&D. We strongly recommend that R&D monitoring data
be managed according to the FAIR data principles (ﬁndable, accessible, in-
teroperable, and reusable; Wilkinson et al., 2016) and be disseminated as
soon as possible after collection. Data processing and analysis should follow
best practices for reproducible research (Kitzes et al., 2017) and all code
and metadata should be published alongside data products. Once data are
collected and archived, it is important that they are ultimately used to in-
form future decision making. Many datasets, although already available,
are never analyzed to extend the state-of-practice for designers.
5. Institutional and systemic changes in monitoring to advance the
science of natural infrastructure
In the previous section we presented a series of guiding questions for
strategically developing a monitoringplan that incorporates R&D monitor-
ing. We acknowledge, however, that there are practical constraints and in-
stitutional barriers to developing monitoring plans, and these may be more
limiting given the longer time frames and more exacting data requirements
of R&D monitoring. These constraints may thus occasionally preclude R&D
monitoring. Here we consider institutional and systemic changes that may
remove barriers and reduce constraints to widespread R&D monitoring. In
particular, we see opportunities to foster R&D monitoring through institu-
tional structures and practices that 1) provide ﬁnancial and logistical re-
sources for R&D monitoring, and 2) encourage connections between
practitioners and researchers for the co-production of knowledge.
R&D monitoring often operates at longer time scales and with addi-
tional requirements(replication, experimental controls) than initial perfor-
mance monitoring, and its beneﬁts extend beyond that of a single project.
As such, it is unrealistic and perhapsunreasonable to expect that the agency
responsible for project implementation and construction should bear the
full ﬁnancial burden. These costs should instead be borne by separate orga-
nizations or funding bodies with research as part of their core missions.
These could take the form of cooperative monitoring groups united by a
common geographic region or technical approach of interest (e.g., Stein
and Bledsoe, 2013). Institutions operating at larger spatial scales are
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
perhaps the most appropriate to support this type of work, and they could
steward and allocate funding with a broad contextual understanding of
which knowledge is new and beneﬁcial to the larger ﬁeld of practice.
Funding to support R&D monitoring could be subsidized or cost-shared
across scales and participating organizations.
Institutional structures already exist in many government agencies to
support this model. For example, at the US Army Corps of Engineers, the
Engineer Research and Development Center (ERDC) provides research sup-
port and technical guidance to Corps districts, the Department of Defense,
and other agencies. In the US Forest Service, the Research Stations provide
a similar role to the National Forest System, while the US Geological Survey
provides analogous services to other units of the Department of the Interior.
However, what is missing in each case is a substantial and reliable pool of
funding that can be drawn upon to support both ongoing and novel
science-based monitoring programs. Of course, the monitoring demand
will still tend to exceed the available resources, so one of the roles of the
research-oriented organizations should be to decide when R&D monitoring
is not necessary (Bernhardt et al., 2007). Our R&D monitoring guidelines
would make useful contributions to the selection process.
Relatedly, models already exist for reliably funding and executing mon-
itoring in other contexts. Data collection efforts over large spatiotemporal
scales are commonly supported by federal agencies in the United States
and elsewhere. These programs were often developed for a given purpose
but adopted by researchers for other applications including broader disci-
plinary learning. For example, the Landsat program and the USGS stream
gaging network have both contributed invaluable data to research in multi-
ple ﬁelds (e.g., Wulder et al., 2012;Sepulveda et al., 2019) and are sup-
ported by a cost-sharing model with contributions from dozens of
institutions and agencies. A similar use of funds to intentionally draw useful
data from planned NI projects would bea simple extension of this concept.
The European Union'sNetworkNature community provides an excellent
example of the type of large-scalesupport and coordination that will be nec-
essary to facilitate research and development for widespread implementa-
tion of natural infrastructure. The network, funded by the European
Commission through its Horizon 2020 initiative, brings together academic,
non-proﬁt, and governmental partners with the goal of mainstreaming
nature-based solutions (NBS, used equivalently to NI) in EU countries. Of
particular relevance to R&D monitoring is their primary activity of synthe-
sizing and strengthening the evidence base for nature-based solutions,
which includes strategic and organized monitoring. NetworkNature also in-
cludes specialized task forces, among which are groups for data and knowl-
edge sharing and assessment of NBS projects. The network more broadly,
and these taskforces speciﬁcally, allows researchers to direct monitoring ef-
forts and funding to projects that will yield informative results. This sort of
coordination also allows for replicated or parallel monitoring for morepow-
erful inference. Similar efforts at national, regional, or global scales would
greatly improve the feasibility of useful R&D monitoring for NI.
Co-production of science among researchers, practitioners and stake-
holders isessential to generating a solid knowledge-base and actionable un-
derstanding that are scientiﬁcally rigorous and relevant to real-world
applications (Norström et al., 2020). Efforts to support the development
of a community of practice—a social-professional network of stakeholders,
researchers, practitioners and decision-makers who, through dialogue,
achieve a shared understanding of a problem or ﬁeld of inquiry—enhance
project outcomes and increase learning for adaptive management or
broader learning (Adelle et al., 2021). For example, strategies like collabo-
rative adaptive management are amenable to R&D monitoring because
they treat management projects as learning opportunities, involving re-
search and implementation entities in monitoring (Allen and Garmestani,
2015;Barrett et al., 2021). The development and maintenance of interper-
sonal relationships among these groups is therefore a primary concern. A
major obstacle to increased interactions between researchers and stake-
holders is a bias in career incentives for academics, which tends to reward
grant acquisition and academic publications more than applied products
like decision-making tools, consultations and on-the-ground conservation
results (Djenontin and Meadow, 2018). Likewise, many practitioners may
also not be incentivized to publish monitoring data associated with their
management actions due to constrained staff time, client priorities, or
unfamiliarity with data analysis or publishing norms. Larger shifts in career
incentives and the fostering of networking tools or bridge organizations to
promote working relationships between practitioners and researchers
across scales are needed to better promote co-production via R&D
6. Harnessing opportunities and avoiding pitfalls
The inclusion of NI in recent infrastructure investment efforts in the
United States, many European countries, and China indicates that current
interest in NI is high and continuing to rise (H.R. 3684, Xia et al., 2017;
O’Donnell et al., 2020;van Rees et al., 2021). Given continued urbanization
and population growth and the escalating need for climate change adapta-
tion, more than US$90 trillion in infrastructure investments is needed glob-
ally by 2040 (Global Infrastructure Hub,2019). Major economic p owers are
now beginning to respond to this need with infrastructure investment pro-
grams of historic scale.
Harnessing this ﬁnancial and political capital for long-term gains in the
resilience of engineered systems and enhancement of biodiversity requires
an investment in learning about the effectiveness of NI practices. The clear
importance of ecosystem services and climate change adaptation to address
Sustainable Development Goals and protect vulnerable populations implies
that opportunities for learning cannot be missed (Nesshöver et al., 2017;
Nelson et al., 2020a, 2020b). As a whole, the multidisciplinary community
involved with natural infrastructure development cannot afford to repeat
past errors seen in other branches of restoration ecology. To avoid
“projectiﬁcation”and the ineffective application of large economic invest-
ments, researchers and practitioners must collaborate to generate useful
knowledge from this historic and crucial juncture.
Investment in developing an evidence base for NI is critical to its long-
term successful implementation, as increasingly frequent natural hazards
may diminish future interest in NI, which generally accrue beneﬁts more
slowly than conventional solutions (Nelson et al., 2020a). Promoting learn-
ing at the scale of individual projects, through the development of monitor-
ing plans with clear learning objectives, and at the scale of institutions,
through ﬁnancial support for monitoring efforts, coordination and prioriti-
zation of key research questions, and the development of a community of
practice,are timely interventions to realize the promises of NI.
The guiding questions and priorities outlined above also apply more
broadly to ecological restorations in general and other environmental man-
agement strategies that operate under ecological complexity and longer
time scales. Monitoring over these longer time scales offers the additional
synergy of being easily compatible with learning and hypothesis testing
about design performance and its variations across system conditions. Con-
temporary implementation of NI must employ monitoring that leads tobet-
ter designs. The more strategic approach to monitoring presented in this
paper, especially insofar as it encourages the judicious and deliberate appli-
cation of R&D monitoring, offers one pathway toavoid missing additional
opportunities for useful monitoring information. Ideally, the interdisciplin-
ary scientiﬁc community can take advantage of the rising tide of support
and funding for NI implementation and conduct meaningful monitoring
to ensure the achievement of the multifaceted goals motivating this para-
digm shift in infrastructure and conservation. Such a paradigm shift will
make great contributions in better aligning the increasingly compatible
goals of biodiversity conservation and sustainable, climate-resilient infra-
structure development to secure a safer, healthier future for the global com-
munity and life-supporting ecosystems.
This discussion paper reviewed the importance of monitoring ecological
restoration and natural infrastructure projects for improving practice in this
rapidly growing and broadly important ﬁeld, and highlighted the logistical
difﬁculties it presents to researchers and practitioners. We provided a
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
generalized typology that reconciles deﬁnitions of monitoring acrossthe di-
verse disciplines involved in natural infrastructure research and practice,
and a series of questions to facilitate the development of monitoring plans
tailoredtoward learning. Further implementation of this type of monitoring
will generate the necessary evidence base for broader applications of natu-
ral infrastructure needed to combat the ongoing biodiversity crisis and so-
cietal threats from climate change. Given the opportunities presented by
massive infrastructure spending at the global scale in coming decades, it
is essential that natural infrastructure development be implemented strate-
gically and with deliberate attention to research and development.
CRediT authorship contribution statement
Charles B. van Rees: Conceptualization, Writing (Original Draft and Re-
view &Editing), Visualization, Supervision, Project administration. Laura
Naslund: Conceptualization, Writing (Original Draft and Review &Editing),
Visualization. Darixa D. Hernandez-Abrams: Conceptualization, Writing
(Original Draft and Review &Editing). S. Kyle McKay: Conceptualization,
Writing (Review &Editing). C. Brock Woodson: Conceptualization, Writing
(Review &Editing), Funding acquisition, Project administration. Amy
Rosemond: Conceptualization, Writing (Review &Editing). Brian McFall:
Conceptualization, Writing (Review &Editing). Safra Altman:Conceptuali-
zation, Writing (Review &Editing). Seth J. Wenger: Conceptualization,
Writing (Review &Editing), Funding acquisition, Project administration,
Declaration of competing interest
The authors of the submitted manuscript declare no conﬂict of interest.
This research was conducted as part of the Network for Engineering
with Nature (N-EWN, https://n-ewn.org). This work was supported by
the US Army Corps of Engineers Engineering With Nature® Initiative
through Cooperative Ecosystem Studies Unit Agreement W912HZ-20-2-
0031. The use of products or trade names does not represent an endorse-
ment by either the authors or the N-EWN. Opinions expressed here are
those of the authors and not necessarily those of the agencies they represent
or the N-EWN.
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