ArticlePDF Available

A strategic monitoring approach for learning to improve natural infrastructure



Natural infrastructure (NI) development, including ecosystem restoration, is an increasingly popular approach to leverage 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 longer time scales associated with 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 typology 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 monitoring plans for NI around learning objectives. In particular, we emphasize conducting research and development monitoring, 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 of research and development monitoring to increase the evidence base for NI implementation at larger scales.
A strategic monitoring approach for learning to improve
natural infrastructure
Charles B. van Rees
,Laura Naslund
, Darixa D. Hernandez-Abrams
,S. Kyle McKay
, C. Brock Woodson
Amy Rosemond
, 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 denitions 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 benets 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 scientically 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 renement 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.
Nature-based solutions
Green infrastructure
Climate change
Water resources management
1. Introduction
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 benets, 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., ood risk re-
duction, water purication, erosion protection) while providing ancillary
benets 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 denition, 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 denition also encompasses green infrastruc-
ture,although the latter term often refers to a specicsubsetofNIinurban
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 ood 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 oodplain wetlands from
levee setbacks (Guida et al., 2015;Dahl et al., 2017), which reduce ood
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-winsfor 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:
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 insufciently 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 elds 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 articial
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 signicant 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 benets (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 conicting terminology, denitions, 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 wickedchallenge 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 eld of stream restora-
tion presents a cautionary example of the risk of widespread adoption of
interventions without a deliberate approach to assessing their efcacy
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
projectied(sensu Hodge and Adams, 2016) and entrenched in prac-
tice, preventing adaptive management or renement of methods from
past experience (Galatowitsch and Bohnen, 2021). Once established as
an industry, this trajectory became difcult to change, as actors had a
vested interest in assuming success rather than spending funds on mon-
itoring programs that risked demonstrating inefcacy (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 inefcient 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, benets 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
benets; 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 sufciently exible 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 benets 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 rst 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 specic 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 specic guidance on indicators and
This content is intended to provide exible,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 specically 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 workows 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 dene 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 classication 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 specied 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 signicantly. 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 rst 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 efcacy in delivery of expected benets 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 benet 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 classications used for natural infrastructure and ecological restoration organized by the purpose of monitoring, the scale of
benets accrued, the typical time frame, and the responsible party. The time scales suggested here are based on what is typically observed in the eld, 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
specic benets in excess of those expected from natural systems
(e.g., treatment wetland designed for higher rates of denitrication than
those observed in natural systems). These ner 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 signicant 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 verication that a project meets design specications or is
affecting a given system attribute as intended in the management objec-
tives. Based on various denitions 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 ve 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 denition. 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 ve 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 (benets 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 renement 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 falsiable,
controlled, and repeatable (i.e., scientic) 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 benets (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 specic
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-
ne 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 sufcient for hypothesis
testing. The indicators and scale of this type of monitoring depend on the
Table 1
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
Research &
-What about this project worked well and could improve future
-How effective is the implemented method at achieving the
desired goals?
-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 efcacy.
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 sufcient 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-prot-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 takedur-
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 benet 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 specically on the design and implementation of R&D
monitoring plans, it is valuable to briey 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 specic types of NI are covered thoroughly elsewhere (e.g., Eger
et al., 2022;Davis et al., 2022), so we treat these topics briey 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 rst 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-benets 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 nd 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 broaderreection 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 efcacy 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 ve questions for planning R&D monitoring.
Althoughthey are organized to guide new monitoring plans, many of these
considerations are also applicable to the modication 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-
ets and constraints (e.g., as in Sowińska-Świerkosz and García, 2021).
Among monitoring objectives, wefocus specically 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
hydromodication 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&Drst, 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., 1020 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 signicant 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 benet 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-prot 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 projectfor example,
with other organizations implementing or researching similar types of pro-
jectscan 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 (
Fig. 2. A workow 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 efciently 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, nancial, 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 35 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 identied 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 sufciently 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 (ndable, 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 nancial 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 benets 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 nancial 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 benecial to the larger eld 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 elds (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-prot, 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 specically, 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 scientically rigorous and relevant to real-world
applications (Norström et al., 2020). Efforts to support the development
of a community of practicea social-professional network of stakeholders,
researchers, practitioners and decision-makers who, through dialogue,
achieve a shared understanding of a problem or eld of inquiryenhance
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;
ODonnell 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 nancial 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
projecticationand 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 benets 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 nancial 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 scientic 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.
7. Conclusions
This discussion paper reviewed the importance of monitoring ecological
restoration and natural infrastructure projects for improving practice in this
rapidly growing and broadly important eld, and highlighted the logistical
difculties 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 denitions 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 conict of interest.
This research was conducted as part of the Network for Engineering
with Nature (N-EWN, 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.
Adelle, C., Görgens, T., Kroll, F., Losch, B., 2021. Co-production of knowledge in transdisci-
plinary communities of practice: experiences from food governance in South Africa. Sci.
Public Policy 48, 145153.
Agarwal, A., Bhatia, R., Chéret, I., delos Angeles, M.S., Davila-Poblete, S., Falkenmark, M.,
Villarreal, F.G., Jønch-Clausen, T., Kadi, M.A., Kindler, J., 2000. Integrated Water Re-
sources Management. Global Water Partnership, Stockholm.
Albert, C., Brillinger, M., Guerrero, P., Gottwald, S., Henze, J., Schmidt, S., Ott, E., Schröter,
B., 2021. Planning nature-based solutions: principles, steps, and insights. Ambio 50,
Allen, C.R., Garmestani, A.S., 2015. Adaptive management. In: Allen, C.R., Garmestani, A.S.
(Eds.), Adaptive Management of Social-Ecological Systems. Springer Netherlands, Dor-
drecht, pp. 110.
Barrett, K.J., Cannon, J.B., Schuetter, A.M., Cheng, A.S., 2021. Effects of collaborative moni-
toring and adaptive management on restoration outcomes in dry conifer forests. For.
Ecol. Manag. 488, 119018.
Beas-Luna, R., Micheli, F., Woodson, C.B., Carr, M., Malone, D., Torre, J., Boch, C., Caselle,
J.E., Edwards, M., Freiwald, J., Hamilton, S.L., Hernandez, A., Konar, B., Kroeker, K.J.,
Lorda, J., Montaño-Moctezuma, G., Torres-Moye, G., 2020. Geographic variation in re-
sponses of kelp forest communities of the California Current to recent climatic changes.
Glob. Chang. Biol. 26, 64576473.
Bernhardt, E.S., Palmer, M.A., 2011. River restoration: the fuzzy logic of repairing reaches to
reverse catchment scale degradation. Ecol. Appl. 21, 19261931.
Bernhardt, E.S., Palmer, M.A., Allan, J., Alexander, G., Barnas, K., Brooks, S., Carr, J., Clayton,
S., Dahm, C., Follstad-Shah, J., Galat, D., Gloss, S., Goodwin, P., Hart, D., Hassett, B.,
Jenkinson, R., Katz, S., Kondolf, G.M., Lake, P.S., Lave, R., Meyer, J.L., ODonnell, T.K.,
Pagano, L., Powell, B., Sudduth, E., 2005. Synthesizing US river restoration efforts. Sci-
ence 308, 636637.
Bernhardt, E.S., Sudduth, E.B., Palmer, M.A., Allan, J.D., Meyer, J.L., Alexander, G., Follastad-
Shah, J., Hassett, B., Jenkinson, R., Lave, R., 2007. Restoring rivers one reach at a time:
results from a survey of US river restoration practitioners. Restor. Ecol. 15, 482493.
Block, W.M., Franklin, A.B., Ward Jr., J.P., Ganey, J.L., White, G.C., 2001. Design and imple-
mentationof monitoring studies to evaluate the success of ecological restoration on wild-
life. Restor. Ecol. 9, 293303.
Bridges, T., Wagner, P., Burks-Copes, K., Bates, M., Collier, Z., Fischenich, C., Gailani, J.,
Leuck, L., Piercy, C., Rosati, J., 2015. Use of Natural and Nature-based Features (NNBF)
for Coastal Resilience Final Report. US Army Engineer and Research Development
Center, Vicksburg, Mississippi ERDC SR015-1, 477p.
Bridges, T.S., Bourne, E.M., King, J.K., Kuzmitski, H.K., Moynihan, E.B., Suedel, B.C., 2018.
Engineering With Nature: An Atlas. Army Engineer Research Development Center,
VICKSBURG United States.
Bridges, T.S., King, J.K., Simm, J.D., Beck, M.W., Collins, G., Lodder, Q., Mohan, R.K. (Eds.),
2021. InternationalGuidelines on Natural and Nature-Based Features for Flood Risk Man-
agement. U.S.Army Engineer Researchand Development Center,Vicksburg, MS. https://
Brownjohn, J.M.W., 2007. Structural health monitoring of civil infrastructure. Philos. Trans.
R. Soc. A Math. Phys. Eng. Sci. 365, 589622.
Buntaine, M.T., Zhang, B., Hunnicutt, P., 2021. Citizen monitoring of waterways decreases
pollution in China by supporting government action and oversight. Proc. Natl. Acad.
Sci. U. S. A. 118, e2015175118.
Chausson, A., Turner, B., Seddon, D., Chabaneix, N., Girardin, C.A.J., Kapos, V., Key, I., Roe,
D., Smith, A., Woroniecki, S., Seddon, N., 2020. Mapping the effectiveness of nature-
based solutions for climate change adaptation. Glob. Chang. Biol. 26, 61346155.
Christman, Z., Meenar, M., Mandarano, L., Hearing, K., 2018. Prioritizing suitable locations
for green stormwater infrastructure based on social factors in Philadelphia. Land 7 (4),
Corsair, H.J., Ruch, J.B., Zheng, P.Q., Hobbs, B.F., Koonce, J.F., 2009. Multicriteria decision
analysis ofstream restoration: potential and examples. Group Decis. Negot. 18, 387417.
Couix, N., Gonzalo-Turpin, H., 2015. Towards a land management approach to ecological res-
toration to encourage stakeholder participation. Land Use Policy 46, 155162.
Dahl, T.A., Theiling, C.H., Echevarria, W., 2017. Overview of Levee Setback Projects and Ben-
ets. U.S. Army Corps. of Engineers, Engineer Research and Development Center Vicks-
burg, Vicksburg, Mississippi, United States.
Davies, C., Chen, W.Y.,Sanesi, G., Lafortezza, R., 2021. The European Union roadmap for im-
plementing nature-based solutions: a review. Environ. Sci. Pol. 121, 4967.
Davis, J., Whiteld, P., Szimanski, D., Golden, B.R., Whitbeck, M., Gailani, J., Herman, B.,
Tritinger, A., Dillon, S.C., King, J., 2022. A framework for evaluating island restoration
performance: a case study from the Chesapeake Bay. Integr. Environ. Assess. Manag.
18, 4248.
Delaware Living Shoreline Committee, 2018. Developing Monitoring Plans for Living Shore-
line Projects in Delaware: A Goal-Based Framework.
delawareestuary/PDE+Reports/2018-DELS+Framework+V.2.0._Final.pdf. (Accessed
September 2021).
Delaware Living Shorelines Committee, 2018. Developing monitoring plans for living shore-
line projects in Delaware: A goal-based framework. A report prepared by the Delware Liv-
ing Shorelines committee Standards of Practice Subcommittee.
Djenontin, I.N.S., Meadow, A.M., 2018. The art of co-production of knowledge in environ-
mental sciences and management: lessons from international practice. Environ. Manag.
61, 885903.
Downs, P.W., Kondolf, G.M., 2002. Post-project appraisals in adaptive management of river
channel restoration. Environ. Manag. 29, 477496.
Edwards, P.M., Shaloum, G., Bedell, D., 2018. A unique role for citizen science in ecological
restoration: a case study in streams. Restor. Ecol. 26, 2935.
Eger, A.M., Earp, H.S., Friedman, K., Gatt, Y., Hagger, V., Hancock, B., Kaewsrikhaw, R.,
Mcleod, E., Moore, A.M., Niner, H.J., Razanaivo, F., Sousa, A.I., Stankovic, M.,
Worthington, T.A., Bayraktarov, E., Saunders, M., Vergés, A., Reeves, S., 2022. The
need, opportunities,and challenges forcreating a standardized framework for marine res-
toration monitoring and reporting. Biol. Conserv. 266, 109429.
Erwin, S.O., Schmidt, J.C., Allred, T.M., 2016. Post-project geomorphic assessment of a large
process-based river restoration project. Geomorphology 270, 145158.
Everard, M., Ahmed, S., Gagnon, A.S., Kumar, P., Thomas, T., Sinha, S., Dixon, H., Sarkar, S.,
2020. Can nature-based solutions contribute to water security in Bhopal? Sci. Total Envi-
ron. 723, 138061.
Ferraro, P.J., Agrawal, A., 2021. Synthesizing evidence in sustainability science through har-
monized experiments: community monitoring in common pool resources. Proc. Natl.
Acad. Sci. 118.
Friberg, N., Angelopoulos, N., Buijse, A., Cowx, I., Kail, J., Moe, T., Moir, H., OHare, M.,
Verdonschot, P., Wolter, C., 2016. Effective river restoration in the 21st century: from
trial and error to novel evidence-based approaches. Adv. Ecol. Res. 55, 535611.
Galatowitsch, S., Bohnen, J., 2021. Long-term recovery of a restored palustrine wetland: the
role of monitoring and adaptive management. Wetlands 41, 80.
Geist,J.,Hawkins,S.J.,2016.Habitat recovery and restoration in aquatic ecosystems:
current progress and future challenges. Aquat. Conserv. Mar. Freshwat. Ecosyst.
26, 942962.
Giordano,R., Pluchinotta, I.,Pagano, A., Scrieciu,A., Nanu, F., 2020. Enhancing nature-based
solutions acceptance through stakeholders' engagement in co-benets identication and
trade-offs analysis. Sci. Total Environ. 713, 136552.
Girardin, C.J., Jenkins, S., Seddon, N., Allen, M., Lewis, S., Wheeler, C.E., Griscom, B.W.,
Malhi, Y., 2021. Nature-based solutions can help cool the planet if we act now.Nature
593, 191194.
Global Infrastructure Hub, 2019. Forecasting Infrastructure Investment Needs and Gaps.
Global Infrastructure Outlook. 2.
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
Grabowski, Z.J., McPhearson, T., Matsler, A.M., Groffman, P., Pickett, S.T.A., 2022. What is
green infrastructure? A study of denitions in US city planning. Front. Ecol. Environ.
20, 152160.
Grantham,H.S., Bode, M., McDonald-Madden, E., Game, E.T., Knight, A.T., Possingham, H.P.,
2010. Effective conservation planning requires learning and adaptation. Front. Ecol. En-
viron. 8, 431437.
Green, R.H., 1989. Power analysis and practical strategies for environmental monitoring. En-
viron. Res. 50, 195205.
Guida, R.J., Swanson, T.L., Remo, J.W.F., Kiss, T., 2015. Strategic oodplain reconnection for
the Lower Tisza River, Hungary: opportunities for ood-height reduction and oodplain-
wetland reconnection. J. Hydrol. 521, 274285.
Hartmann, T., Slavíková, L., McCarthy, S., 2019. Nature-based solutions in ood risk
management. Nature-based Flood Risk Management on Private Land. Springer,
Cham, pp. 38.
Hassell,N., Tinker, K.A., Moore, T., Ottesen, E.A., 2018. Temporaland spatial dynamicsin mi-
crobial community composition within a temperate stream network. Environ. Microbiol.
20, 35603572.
Herrick, J.E., Schuman, G.E., Rango, A., 2006.Monitoring ecological processes for restoration
projects. J. Nat. Conserv. 14, 161171.
Hodge, I., Adams, W.M., 2016. Short-term projects versus adaptive governance: conicting
demands in the management of ecological restoration. Land 5, 39.
Huddart, J.E., Thompson, M.S., Woodward, G., Brooks, S.J., 2016. Citizen science: from de-
tecting pollution to evaluating ecological restoration. Wiley Interdiscip. Rev. Water 3,
Irwin, A., 2018. No PhDs needed: how citizen science is transforming research. Nature 562,
Jambeck, J.R., Johnsen, K., 2015. Citizen-based litter and marine debris data collection and
mapping. Comput. Sci. Eng. 17, 2026.
Kay, J.J., Regier, H.A., Boyle, M., Francis, G., 1999. An ecosystem approach for sustainability:
addressing the challenge of complexity. Futures 31, 721742.
Kitzes, J., Turek, D., Deniz, F., 2017. The Practice of Reproducible Research.
Kondolf,G.M., 1995. Five elementsfor effective evaluation of stream restoration.Restor. Ecol.
3, 133136.
Konrad, C.P., Black, R.W., Voss, F.,Neale, C.M., 2008. Integrating remotely acquired and eld
data to assess effects of setback levees on riparian and aquatic habitats in glacial-melt
water rivers. River Res. Appl. 24, 355372.
Kowarik, I., Hiller, A., Planchuelo, G., Seitz, B., von der Lippe, M., Buchholz, S., 2019. Emerg-
ing urban forests: opportunities forpromoting the wild side of the urban green infrastruc-
ture. Sustainability 11, 6318.
Lave, R., Doyle, M., 2021. Streams of Revenue: The Restoration Economy and the Ecosystems
It Creates. MIT Press.
Lindenmayer, D., Hobbs, R.J., Montague-Drake, R., Alexandra, J., Bennett, A., Burgman, M.,
Cale, P., Calhoun, A., Cramer, V., Cullen, P., 2008. A checklist for ecological management
of landscapes for conservation. Ecol. Lett. 11, 7891.
Lindenmayer, D.B., Likens, G.E., 2009. Adaptive monitoring: a new paradigm for long-term
research and monitoring. Trends Ecol. Evol. 24, 482486.
Linkov, I., Satterstrom, F.K., Kiker, G., Batchelor, C., Bridges, T., Ferguson, E., 2006. From
comparative risk assessment to multi-criteria decision analysis and adaptive manage-
ment: recent developments and applications. Environ. Int. 32, 10721093.
Lyons, J.E., Runge, M.C., Laskowski, H.P., Kendall, W.L., 2008. Monitoring in the context of
structured decision-making and adaptive management. J. Wildl. Manag. 72,16831692.
Maes, J., Jacobs, S., 2017. Nature-based solutions for Europe's sustainable development.
Conserv. Lett. 10, 121124.
Malcom, J., Kim, T., Li, Y.-W., 2017. Free aerial imagery as a resource to monitor compliance
with the Endangered Species Act. 204750. bioRxiv.
Martin, D.M., Lyons, J.E., 2018. Monitoring the social benets of ecological restoration.
Restor. Ecol. 26, 10451050.
Marttunen, M., Weber,C., Åberg, U., Lienert, J., 2019. Identifying relevant objectives inenvi-
ronmental management decisions: an application to a national monitoring program for
river restoration. Ecol. Indic. 101, 851866.
McDonald-Madden, E., Baxter, P.W., Fuller, R.A., Martin, T.G., Game, E.T., Montambault, J.,
Possingham, H.P., 2010. Monitoring does not always count. Trends Ecol. Evol. 25,
Morandi, B., Kail, J., Toedter, A., Wolter, C., Piégay, H., 2017. Diverse approaches to imple-
ment and monitor river restoration: a comparative perspective in France and Germany.
Environ. Manag. 60, 931946.
Muller, I.,Delisle, M., Ollitrault, M., Bernez, I., 2016. Responsesof riparian plant communities
and water quality after 8 years of passive ecological restoration using a BACI design.
Hydrobiologia 781, 6779.
Nassauer, J.I., Opdam,P., 2008. Design in science: extending thelandscape ecologyparadigm.
Landsc. Ecol. 23, 633644.
National Research Council, 1995. Measuring and Improving Infrastructure Performance. Na-
tional Academies Press.
Nel, J.L., Roux, D.K., Driver, A., Hill, L., Maherry, A.C., Snaddon, K., Petersen, C.R., Smith-
Adao, L.B., Van Deventer, H., Reyers, B., 2015. Knowledge co-production and boundary
work to promote implementation of conservation plans. Conserv. Biol. 30, 176188.
Nelson, D.R., Bledsoe, B.P.,Ferreira, S., Nibbelink, N.P., 2020a. Challenges to re alizing the po-
tential of nature-based solutions. Curr. Opin. Environ. Sustain. 45, 4955.
Nelson, D.R., Bledsoe, B.P., Shepherd, J.M., 2020b. From hubris to humility: transcending
original sin in managing hydroclimatic risk. Anthropocene 30, 100239.
Nesshöver, C., Assmuth, T., Irvine, K.N., Rusch, G.M., Waylen, K.A., Delbaere, B., Haase, D.,
Jones-Walters, L., Keune, H., Kovacs, E., 2017. The science, policy and practice of
nature-based solutions: an interdisciplinary perspective. Sci. Total Environ. 579,
Norström, A.V., Cvitanovic, C., Löf, M.F., West, S., Wyborn, C., Balvanera, P., Bednarek, A.T.,
Bennett, E.M., Biggs, R., deBremond,A., Campbell, B.M., Canadell, J.G., Carpenter, S.R.,
Folke, C., Fulton, E.A., Gaffney, O., Gelcich, S., Jouffray, J.-B., Leach, M., Le Tissier, M.,
Martín-López, B., Louder, E., Loutre, M.-F., Meadow, A.M., Nagendra, H., Payne, D.,
Peterson, G.D., Reyers, B., Scholes, R., Speranza, C.I., Spierenburg, M., Stafford-Smith,
M., Tengö, M., van der Hel,S., van Putten, I., Österblom, H., 2020. Principles for knowl-
edge co-production in sustainability research. Nat. Sustain. 3, 182190.
ODonnell, E., Thorne,C., Ahilan, S., Arthur, S., Birkinshaw, S.,Butler, D., Dawson,D., Everett,
G., Fenner,R., Glenis, V., 2020.The blue-green path to urban ood resilience. Blue-Green
Syst. 2, 2845.
Ota, L., Firn, J., Chazdon, R.L., Gregorio, N., Mukul, S.A., Viani, R.A.G., Romero, C., Herbohn,
J., 2021. Using leading and lagging indicators for forest restoration. J. Appl. Ecol. 58,
Palmer, M., Allan, J.D.,Meyer, J., Bernhardt, E.S., 2007. River restoration in the twenty-rst
century: data and experiential knowledge to inform future efforts. Restor. Ecol. 15,
Prach, K., Durigan, G., Fennessy, S., Overbeck, G.E., Torezan, J.M., Murphy, S.D., 2019. A
primer on choosing goals and indicators to evaluate ecological restoration success.
Restor. Ecol. 27, 917923.
van Rees, C.B., 2018. Wetland conservation in Hawaii and the need for agship species.
`Elepaio 78, 3741.
van Rees, C.B., Cañizares,J.R., Garcia, G.M., Reed, J.M., 2019. Ecological stakeholder analogs
as intermediaries between freshwater biodiversity conservation and sustainable water
management. Environ. Policy Gov. 29, 303312.
R., Decleer, K., Adriaens, T., Denys, L., Jarić, I., Janse, J.H., Monaghan, M.T.,
De Wever, A., Geijzendorffer, I., Adamescu, M.C., Jähnig, S.C., 2021.
Safeguarding freshwater life beyond 2020: recommendations for the new global
biodiversity framework from the European experience. Conserv. Lett. 14,
Roni, P., Beechie, T., 2012. Stream andWatershed Restoration: A Guide to Restoring Riverine
Processes and Habitats. John Wiley &Sons.
Rubin, Z., Kondolf, G.M., Rios-Touma, B., 2017. Evaluating stream restoration projects: what
do we learn from monitoring? Water 9, 174.
Russ, A., Peters, S.J., E. Krasny, M., Stedman, R.C., 2015. Development of ecological place
meaning in New York City. J. Environ. Educ. 46, 7393.
Science and Resilience Institute, 2020. Measuring Success: Monitoring Natural and Nature-
based Shoreline Featuresin New York State. New York State Department of State, Coastal
Management Program.
measuringsuccess_nalreport_minusappendices_050721.pdf. (Accessed September
Seddon, N., Chausson, A., Berry, P., Girardin, C.A.J., Smith,A., Turner, B., 2020. Understand-
ing the value andlimits of nature-based solutionsto climate change and otherglobal chal-
lenges. Philos. Trans. R. Soc., B 375, 20190120.
Sepulveda,A.J., Schmidt, C.,Amberg, J., Hutchins, P., Stratton, C., Mebane, C., Laramie,M.B.,
Pilliod, D.S., 2019. Adding invasive species biosurveillance to the US Geological Survey
streamgage network. Ecosphere 10, e02843.
Simonson, W.D., Miller,E., Jones, A., García-Rangel, S., Thornton, H., McOwen, C., 2021. En-
hancing climate change resilience of ecological restoration a framework for action.
Perspect. Ecol. Conserv. 19, 300310.
Slinger, J., Stive, M.,Luijendijk, A., 2021.Nature-based solutions for coastal engineering and
management. Water 13.
Slough, T., Rubenson, D., Levy, R., Alpizar Rodriguez, F., Bernedo del Carpio, M., Buntaine,
M.T., Christensen, D.,Cooperman, A., Eisenbarth, S., Ferraro, P.J., Graham, L., Hartman,
A.C., Kopas, J., McLarty, S., Rigterink,A.S., Samii, C., Seim, B., Urpelainen, J., Zhang, B.,
2021. Adoption of communitymonitoring improves common pool resource management
across contexts. Proc. Natl. Acad. Sci. U. S. A. 118, e2015367118.
Sowińska-Świerkosz, B., García, J., 2021. A new evaluation framework for nature-based solu-
tions (NI) projects based on the application ofperformance questions and indicators ap-
proach. Sci. Total Environ. 787, 147615.
Stammel, B., Cyffa, B., Geist, J., Muller, M., Pander, J., Blasch, G., Fischer, P., Gruppe, A.,
Haas, F., Kilg, M., Lang, P., Schopf, R., Schwab, A., Utschik, H., Weisbrod, M., 2012.
Floodplain restoration on the UpperDanube (Germany) by re-establishing water andsed-
iment dynamics: a scientic monitoring as part of the implementation. River Syst. 20,
Stein, E.D., Bledsoe, B.P., 2013. Framework for Developing Hydromodication Monitoring
Programs. Southern California Coastal Water Research Project, Technical Report 752.
ydromodMonitoringFramework.pdf. (Accessed September 2021).
Stein, E.D., Federico, F., Booth, D.B., Bledsoe, B.P., Bowles, C., Rubin, Z., Kondolf, G.M.,
Sengupta, A., 2012. Hydromodication Assessment and Management in California. Cali-
fornia State Water Resources ControlBoard Stormwater Program, Technical Report 667.
wqip/2013-0001/J_References/J116.pdf. (Accessed September 2021).
Suding, K.N., 2011. Toward an era of restoration in ecology: successes, failures, and opportu-
nities ahead. Annu. Rev. Ecol. Evol. Syst. 42, 465487.
Theiling, C.H., Janvrin, J.A., Hendrickson, J., 2015. Upper Mississippi River restoration: im-
plementation, monitoring, and learning since 1986. Restor. Ecol. 23, 157166. https://
the full potential of citizen science monitoring programs. Biol. Conserv. 165,
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
Vugteveen, P., van Katwijk, M.M., Rouwette, E., Hanssen, L., 2014. How to structure and pri-
oritize information needs in support of monitoring design for integrated coastal manage-
ment. J. Sea Res. 86, 2333.
Webb, J.A., Watts, R.J., Allan, C., Warner, A.T., 2017. Principles for monitoring, evaluation,
and adaptive management of environmental water regimes. Water for the Environment.
Elsevier, pp. 599623.
Weber, C., Åberg, U., Buijse, A.D., Hughes, F.M., McKie, B.G., Piégay, H., Roni, P.,
Vollenweider, S., Haertel-Borer, S., 2018. Goals and principles for programmatic river
restoration monitoring and evaluation: collaborative learning across multiple projects.
Wiley Interdiscip. Rev. Water 5, e1257.
Wijsman, K., Auyeung, D., Brashear, P., Branco, B., Graziano, K., Groffman, P., Cheng, H.,
Corbett, D., 2021. Operationalizing resilience: co-creating a framework to monitor
hard, natural, and nature-based shoreline features in New York State. Ecol. Soc. 26.
Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij.J., Appleton, G., Axton, M., Baak, A.,
Blomberg, N., Boiten, J.-W., da Silva Santos, L.B., Bourne, P.E., 2016. The FAIR Guiding
Principles for scientic data management and stewardship. Sci. Data 3, 19.
Wohl, E., Angermeier, P.L., Bledsoe, B., Kondolf, G.M., MacDonnell, L., Merritt, D.M., Palmer,
M.A., Poff, N.L., Tarboton, D., 2005. River restoration. Water Res. Res.
Wohl, E., Lane, S.N., Wilcox, A.C., 2015. The science and practice of river restoration. Water
Resour. Res. 51, 59745997.
World Wildlife Fund (WWF), 2020. Nature-Based Solutions for Climate Change.
Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E., 2012. Opening the
archive: how free data has enabled the science and monitoring promise of Landsat. Re-
mote Sens. Environ. 122, 210.
WWAP/UN-Water (United Nations World Water Assessment Program), 2018. The United Na-
tions World Water Development Report 2018: Nature-based Solutions for Water.
Xia, J., Zhang,Y., Xiong, L., He, S., Wang,L., Yu, Z., 2017. Opportunities and challenges of the
Sponge City construction related to urban water issues in China. Sci. China Earth Sci. 60,
Yu, G., Piao, S., Zhang, Y., Liu, L., Peng, J., Niu, S., 2021. Moving toward a new era of ecosys-
tem science. Geogr. Sustain. 2, 151162.
C.B. van Rees et al. Science of the Total Environment 832 (2022) 155078
... Specialized and strategic approaches to monitoring the interactions of ecosystems and Natural Infrastructure will be necessary to collect further empirical evidence of performance benefits. In this sense, every conservation, restoration, or infrastructure project can be treated as an experiment with specific research questions and opportunities for learning (121). Initiatives like the United Kingdom's nationwide biodiversity indicators (122) and the US National Nature Assessment (123) provide an excellent opportunity to develop an important evidence base for mainstreaming biodiversity in agency activities at the national scale. ...
Civil infrastructure will be essential to face the interlinked existential threats of climate change and rising resource demands while ensuring a livable Anthropocene for all. However, conventional infrastructure planning largely neglects the contributions and maintenance of Earth’s ecological life support systems, which provide irreplaceable services supporting human well-being. The stability and performance of these services depend on biodiversity, but conventional infrastructure practices, narrowly focused on controlling natural capital, have inadvertently degraded biodiversity while perpetuating social inequities. Here, we envision a new infrastructure paradigm wherein biodiversity and ecosystem services are a central objective of civil engineering. In particular, we reimagine infrastructure practice such that 1) ecosystem integrity and species conservation are explicit objectives from the outset of project planning; 2) infrastructure practices integrate biodiversity into diverse project portfolios along a spectrum from conventional to nature-based solutions and natural habitats; 3) ecosystem functions reinforce and enhance the performance and lifespan of infrastructure assets; and 4) civil engineering promotes environmental justice by counteracting legacies of social inequity in infrastructure development and nature conservation. This vision calls for a fundamental rethinking of the standards, practices, and mission of infrastructure development agencies and a broadening of scope for conservation science. We critically examine the legal and professional precedents for this paradigm shift, as well as the moral and economic imperatives for manifesting equitable infrastructure planning that mainstreams biodiversity and nature’s benefits to people. Finally, we set an applied research agenda for supporting this vision and highlight financial, professional, and policy pathways for achieving it.
... What was once an opportunity is now an imperative, what was an emerging challenge is now a crisis, and what seemed possible separately is now impossible without integration. Published online: xx xx xxxx flexible and evidence-based approach that more clearly articulates the purpose of monitoring 9 . Similarly, forecasting infrastructure performance is crucial to engineering decision-making processes. ...
Full-text available
Infrastructure development and biodiversity conservation are often planned and executed in isolation. However, outcomes from these efforts are interlinked, with coordinated actions required to jointly address sustainability challenges. Natural infrastructure — encompassing a spectrum of natural to conventional solutions — is key to the infrastructure–biodiversity connection and should be brought into large-scale application.
... Other methodological frameworks have been developed to monitor environmental services provided by NbS that include plant and soil indicators [8]. A variety of indicators and planning guidelines have also been developed to evaluate the interaction between NbS processes and socioeconomic factors [9][10][11][12][13][14]. While these evaluation methods have helped to qualify NbS benefits, functions, and characteristics, a clear understanding of NbS quantification through sensing and measurement, as a mechanism to address socio-environmental challenges such as climate change, is essential. ...
Full-text available
Sensing and measurement techniques are necessary to study, evaluate, and understand the complex physical and chemical interactions that must occur for the successful deployment of nature-based solutions (NbS). How NbS are measured can determine which solutions best address local environmental and societal challenges, and how these solutions are prioritized and adopted by decision makers. Sensing and measurement techniques can provide useful meteorological and physiological data on nature-based interventions between different spatial, spectral, temporal, and thematic scales. Because NbS encompass research from across different fields, it is essential to reduce barriers to knowledge dissemination, and enable the circulation of information across different jurisdictions. In this study, a bibliometric and systematic analysis of the literature was undertaken to systemize and categorize sensing and measurement techniques for NbS. Opportunities and challenges associated with studying the effects of NbS have also been identified. Sensing and measurement techniques can provide evidence-based information on the efficacy of NbS, in addition to guiding policy formulation for the achievement of sustainable development across communities.
... User-friendly outputs from all forms of e-flows monitoring (such as River Health Scorecards or similar devices, social media posts, mainstream media stories and policy briefs) are needed to build understanding and knowledge exchange with participant stakeholders and broader communities (Djenontin and Meadow 2018; Horne et al. 2022). A well-structured engagement and communication strategy can help convey important messages, exchange knowledge and help shift the mindsets of a wide range of stakeholders, notably community groups and decision-makers, toward e-flows protection.Accordingly, we recommend greater institutional commitment and support of rigorous longterm monitoring and reporting of biophysical and societal outcomes of e-flows in an adaptive management framework tailored toward continuous learning (vanRees et al. 2022). The growing applications of flow-related nature-based solutions (NBS) to river degradation and biodiversity loss warrant system-level monitoring to evaluate their benefits for ecosystems, species and society (vanRees et al. 2023). ...
Full-text available
Environmental flows (e-flows) aim to mitigate the threat of altered hydrological regimes in river systems and connected waterbodies and are an important component of integrated strategies to address multiple threats to freshwater biodiversity. Expanding and accelerating implementation of e-flows can support river conservation and help to restore the biodiversity and resilience of hydrologically altered and water-stressed rivers and connected freshwater ecosystems. While there have been significant developments in e-flows science, assessment and societal acceptance, implementation of e-flows within water resources management has been slower than required and geographically uneven. This review explores critical factors that enable successful e-flows implementation and biodiversity outcomes in particular, drawing on 13 case studies and the literature. It presents e-flows implementation as an adaptive management cycle enabled by 10 factors: legislation and governance, financial and human resourcing, stakeholder engagement and co-production of knowledge, collaborative monitoring of ecological and social-economic outcomes, capacity training and research, exploration of trade-offs among water users, removing or retrofitting water infrastructure to facilitate e-flows and connectivity, and adaptation to climate change. Recognising that there may be barriers and limitations to the full and effective enablement of each factor, the authors have identified corresponding options and generalizable recommendations for actions to overcome prominent constraints, drawing on the case studies and wider literature. The urgency of addressing flow-related freshwater biodiversity loss demands collaborative networks to train and empower a new generation of e-flows practitioners equipped with the latest tools and insights to lead adaptive environmental water management globally. Mainstreaming e-flows within conservation planning, integrated water resource management (IWRM), river restoration strategies and adaptations to climate change, is imperative. The policy drivers and associated funding commitments of the Kunming-Montreal Global Biodiversity Framework offer crucial opportunities to achieve the human benefits contributed by e-flows as nature-based solutions (NBS), such as flood risk management, floodplain fisheries restoration and increased river resilience to climate change.
... It is a flood defence technique that is gaining consideration in flood risk planning and environmental policy (Environment Agency, 2020;HM Government, 2018). However, a lack of reliable evidence as to the performance of NFM installations hinders advocating its more widespread use in flood defence (Bernhardt et al., 2005;Environment Agency, 2018;van Rees et al., 2022). ...
Full-text available
A method using Sentinel-1 synthetic aperture radar (SAR) data to evidence flood changes after floodplain restoration is proposed. A Natural Flood Management (NFM) project on the Ouse river in southern England, undertaken by the Sussex Flow Initiative, was analysed to ascertain any reduction of previous flood risk. GIS operations were conducted on the results of the change detection analysis to identify how flood area, form and compactness were affected after the NFM installation, and how these changes relate to the project aims. Flood records based on internet-published drone footage verified the change detection methodology. A scorecard was developed to evaluate the benefits and disadvantages of spatial changes seen in post-restoration floods in comparison to inundation before the measures were installed. Evaluation results were used in the annual report of the SFI project to demonstrate the attenuation of floodwaters in accordance with the aims of the project.
... were not designed to evaluate consequences of restoration activities or to disseminate monitoring results' ( [3], p. 637). This lack of performance monitoring is still evident, which has motivated the development of a strategic approach in monitoring natural infrastructure projects, such as NFM [4]. Evidence has been gathered in the UK on the use of natural processes in ecosystem services, which includes river and floodplain restoration. ...
Full-text available
This study proposes an evaluation method using C-band Sentinel-1 synthetic aperture radar (SAR) data to provide evidence of flood characteristic changes after the restoration of a floodplain. A portable, flexible evaluation framework has replicated previous change detection research approaches to analyse a Natural Flood Management (NFM) project on the Sussex Ouse river in southern England, conducted by the Sussex Flow Initiative (SFI), to ascertain how control measures have helped mitigate flood risk. GIS operations were conducted on the mapped results of the change detection procedure to identify how flood area, form and compactness have been affected after the NFM installation restored a floodplain to slow river flow and how these changes relate to the overall aims of the project. Innovative means were employed to verify the change detection methodology by sampling flood records from internet-published drone footage. The overall accuracy achieved using the Change Detection and Thresholding (CDAT) technique was 75%. The use of SAR data provides evidence of how NFM features function during significant flood events, providing a mapped delineation of the actual flood extent. A comprehensive scorecard has been developed to evaluate the positive and negative outcomes of the spatial changes that have manifested in post-restoration floods, in comparison to inundation before the installation. Results from this study have been included in the annual report of the SFI project to demonstrate how key features have attenuated flood waters in accordance with design intentions.
Full-text available
Enthusiasm for and investments in nature-based solutions (NBS) as sustainable strategies for climate adaptation and infrastructure development is building among governments, the scientific community, and engineering practitioners. This is particularly true for water security and water-related risks. In a freshwater context, NBS may provide much-needed “win-wins” for society and the environment that could benefit imperiled freshwater biodiversity. Such conservation benefits are urgently needed given the ongoing freshwater biodiversity crisis, with declines in freshwater species and their habitats occurring at more than twice the rate of marine or terrestrial systems. However, for NBS to make meaningful contributions to safeguarding freshwater biodiversity, clear links must be established between NBS applications and priorities for conservation. In this paper, we link common water-related NBS to six priority actions for freshwater life established by the conservation science n community, and highlight priority research and knowledge that will be necessary to bring NBS to bear on the freshwater biodiversity crisis. In particular, we illustrate how NBS can play a direct role in restoring degraded aquatic and floodplain ecosystems, enhancing in-stream water quality, and improving hydrological connectivity among freshwater ecosystems. System-level monitoring is needed to ensure that freshwater NBS deliver on their promised benefits for ecosystems and species.
Full-text available
Nearshore nourishment is a common coastal flood risk management technique that can be constructed beneficially by using dredged sediment from navigation channels. A nearshore nourishment project was completed during the summer of 2021 in Harvey Cedars, NJ, USA, with 67,500 m3 of dredged sediment from Barnegat Inlet placed along approximately 450 m of beach in a depth of 3–4 m. In situ instruments were installed to monitor hydrodynamic conditions before and after dredged material placement, and nine topographic and bathymetric surveys were conducted to monitor nearshore morphological response to the nourishment. Shoreline location was extracted from satellite imagery using CoastSat software to compare historical trends to the shoreline response after construction. Seven months after construction, 40% of the nearshore nourishment was transported from the initial footprint and the centroid of the nourishment migrated towards shore and alongshore (north). The sheltering capacity of the nearshore berm appears to have captured an additional 58% of the placed volume from the longshore transport system and the beach width onshore of the placement increased by 10.9 m. Measured data, satellite imagery analysis, and rapid predictions all indicate that the nearshore nourishment at Harvey Cedars had a positive impact on the adjacent beach.
Full-text available
In response to interdependent challenges, city planners are increasingly adopting “green infrastructure” (GI). Reviewing 122 plans from 20 US cities, we identify what types of city plans address and define GI, including the concepts associated with GI, as well as the types, functions, and benefits of GI. The most common plans that feature GI, some of which focus exclusively on GI, comply with US Clean Water Act regulations for stormwater and sewer systems. Municipalities also address GI through diverse planning processes, including the creation of comprehensive citywide plans. Many of these plans (~40%) do not explicitly define GI. When they do, stormwater concepts predominate, followed by landscape concepts, along with an emergent emphasis on integrating GI with other built infrastructure systems. Large differences in GI types, functions, and benefits across concepts, plan types, and cities indicate a need for synthesis of GI definitions. To facilitate this synthesis, we provide a database of GI definitions from plans used in our analysis. We conclude with a broad synthetic definition of GI to provide clarity and stimulate discussion in rapidly evolving planning, policy, and research arenas.
Full-text available
There is growing interest in the application of nature-based solutions to adapt to climate change and promote resilience, yet barriers exist to their implementation. These include a perceived lack of evidence of their functioning in comparison to conventional solutions and an inability for existing design, policy, and assessment processes to capture the multiple benefits of these solutions. Positing this as a challenge of operationalizing and measuring resilience, we argue that the concept of resilience needs to be given concrete meaning in applied management contexts. Starting with shoreline vulnerability as a policy problem and natural and nature-based shoreline features as a promising solution, we present a case study of a co-creative process to produce an interdisciplinary and locally relevant approach to understanding and capturing the benefits of natural and nature-based solutions. We develop the notion of resilience service to enable a concreteness to resilience that simultaneously takes into account ecological, technical, and social dimensions. Through the co-creative process, our researcher-practitioner network developed a monitoring framework for shoreline features in New York State to facilitate the comparison of natural and nature-based features with conventional shoreline approaches. We describe the process and assess the advantages and drawbacks of integrating scientific input and local knowledge. We present the monitoring framework, showing how the co-creative character of the process is consequential in the formulation of the final framework through the selection of parameters, indicators, and protocols. We argue that interdisciplinarity, co-creation, pragmatism, multi-scalar applicability, and policy relevance are critical principles to understand the functioning and facilitate the implementation of nature-based solutions, while recognizing that this work necessitates compromise and as such will lead to continued deliberation. We posit this is a strength of the process for it acknowledges the creation of resilience as a social process in which values are central and subject to change.
Full-text available
Pervasive overuse and degradation of common pool resources (CPRs) is a global concern. To sustainably manage CPRs, effective governance institutions are essential. A large literature has developed to describe the institutional design features employed by communities that successfully manage their CPRs. Yet, these designs remain far from universally adopted. We focus on one prominent institutional design feature, community monitoring, and ask whether nongovernmental organizations or governments can facilitate its adoption and whether adoption of monitoring affects CPR use. To answer these questions, we implemented randomized controlled trials in six countries. The harmonized trials randomly assigned the introduction of community monitoring to 400 communities, with data collection in an additional 347 control communities. Most of the 400 communities adopted regular monitoring practices over the course of a year. In a meta-analysis of the experimental results from the six sites, we find that the community monitoring reduced CPR use and increased user satisfaction and knowledge by modest amounts. Our findings demonstrate that community monitoring can improve CPR management in disparate contexts, even when monitoring is externally initiated rather than homegrown. These findings provide guidance for the design of future programs and policies intended to develop monitoring capabilities in communities. Furthermore, our harmonized, multisite trial provides sustainability science with a new way to study the complexity of socioecological systems and builds generalizable insights about how to improve CPR management.
Full-text available
Ecosystem is a fundamental organizational unit of the biosphere in which biological communities interact with their non-biological environment through energy flows and material cycles. Ecosystem Science is the study of patterns, processes, and services of ecosystems. Since the 1990s, rising concerns regarding global climate change, biodiversity loss, ecosystem degradation, and sustainability of the human-dominated biosphere have stimulated the growth of Ecosystem Science, which is expected to provide systematic solutions to many of these major issues facing human societies. This paper provides a comprehensive review of the current progress in Ecosystem Science and identifies some key research challenges facing this discipline. We demonstrate that a key feature of the current progress in Ecosystem Science is its evolution from primarily theoretical explorations toward more systematic, integrative and application-oriented studies. Specifically, five major changes in the discipline over the past several decades can be identified. These include: (1) the expansion of the primary goal from understanding nature to include human activities; (2) the broadening of the research focus from single ecosystem types to macro-ecosystems comprising multiple regional ecosystems; (3) the shifting of research methods from small-scale observations and experiments to large-scale observations, network experiments, and model simulations; (4) the increasing attention to comprehensive integration of ecosystem components, processes, and scales; and (5) the shifting from a primarily biology-oriented focus to an integrated multi-disciplinary scientific field. While Ecosystem Science still faces many challenges in the future, these directional changes, along with the rapidly enriched research tools and data acquisition capabilities, lay a promising ground for the discipline's future as a fundamental scientific basis for solving many environmental challenges facing human societies.
Full-text available
1. Forest restoration targets are often planned, implemented, measured, and reported based on few short‐term lagging indicators (i.e. indicators of realised outcomes), such as the number of seedlings and area planted. 2. We propose the use of leading indicators, which denote likelihood of a certain outcome (e.g. odds that seedlings are of quality and properly planted) to complement lagging indicators and describe how this construct differs from the current practice and how they can be used in conjunction with available frameworks for forest restoration. 3. Leading indicators have great promise to complement lagging indicators because they address the near‐term factors more likely to influence the progress and performance of restoration efforts. For example, secure land tenure (leading indicator) can increase the likelihood of long‐term maintenance and protection (lagging indicator), and the use of best practices in quality seedling production (leading indicator) can increase survival rate (lagging indicator). 4. By observing near‐term leading indicators, management can be adapted towards a goal. Long‐term impacts cannot be verified in the early stages of forest restoration, hence claiming success within the length of project cycles is often unrealistic. Reporting on leading indicators can inform the likelihood that forest restoration goals will be achieved in the longer term. 5. Synthesis and applications: Leading indicators complement lagging indicators and can be used in forest restoration beyond monitoring and evaluation. Indicators can also be used in the design, adaptive management and reporting of restoration interventions. Leading indicators can be used to identify issues that might prevent success in a timely manner so they can be addressed.
Marine ecosystems have been used, impacted by, and managed by human populations for millennia. As ecosystem degradation has been a common outcome of these activities, marine management increasingly considers ecosystem restoration. Currently, there is no coherent data recording format or framework for marine restoration projects. As a result, data are inconsistently recorded and it is difficult to universally track progress, assess restoration's global effectiveness, reduce reporting bias, collect a holistic suite of metrics, and share information. Barriers to developing a unified system for reporting marine restoration outcomes include: reaching agreement on a framework that meets the needs of all users, funding its development and maintenance, balancing the need for ‘ease of use’ and detail, and demonstrating the value of using the framework. However, there are opportunities to leverage arising from the United Nation Decades of Ecosystem Restoration and Science for Sustainable Development and with existing processes already developed by restoration groups (e.g. Global Mangrove Alliance, Society for Ecological Restoration). Here we provide guidelines and a roadmap for how such a framework could be developed and the potential benefits of such an endeavor. We call on practitioners to collaborate to develop such a framework and on governing bodies to commit to making detailed reporting a requirement for restoration project funding. Using a standardized marine restoration monitoring framework would enable the application of adaptive management when projects are not progressing as expected, advance our understanding of the state of worldwide marine restoration, and generate knowledge to advance restoration methodologies.
In wetland restoration, adaptive management is a rarely used but potentially effective way to test initial assumptions of factors controlling recovery and adjust ongoing management to encourage ecosystem recovery. Limited development of monitoring practices and governance approaches hinder broader adoption of adaptive management. Reports of adaptive management for small wetlands, in particular, are lacking. We report here on lessons learned from monitoring a 3-ha, restored, palustrine wetland in central Minnesota for 23 years and using this information to guide management. The restoration was undertaken to convert a drained wetland dominated by invasive species to a high-quality meadow and marsh. Invasive species were treated, the drainage-tile system disabled, and 112 native wetland species seeded or planted. We installed a staff gauge for weekly hydrologic monitoring and monuments to delineate 26 vegetation monitoring units that encompassed the entire wetland. Five vegetation surveys were conducted between 2000 and 2019, consisting of comprehensive meanders of each monitoring unit; cover was estimated within each unit for all species observed. Coordination meetings of management staff and scientists were held to review evidence from monitoring that indicated a need to adjust vegetation or water management. Hydrologic monitoring provided evidence that the ecosystem goals needed to be adjusted and vegetation monitoring informed invasive species management. However, the linkage between monitoring and management could have been strengthened with a formal adaptive management plan at the initiation of restoration and with more frequent coordination cycles.
Significance Approximately 70% of China’s rivers and lakes are unsafe for human use. Effective implementation of existing pollution standards can improve the health and well-being of people across China. In this randomized trial, pollution decreased when a nongovernmental organization enlisted volunteers to monitor the quality of urban waterways slated for remediation and disseminated that information to local and provincial authorities. Disseminating information to the public through posters did not have detectable effects on water quality. Nongovernmental organizations can support the efforts of authorities to remediate pollution by providing monitoring that guides action and facilitates oversight between different levels of government, particularly when authorities that set remediation targets have an active interest in responding to public complaints and using monitoring for oversight.
Ecological restoration is a tool for climate change mitigation and adaptation, and yet its outcomes are susceptible themselves to climate change impacts. Drawing on the literature documenting this in theory and practice, we present a comprehensive overview of climate change risks and considerations across the whole life cycle of a restoration initiative. The resulting framework identified seven areas of restoration design and implementation in which climate change is important to address: setting restoration objectives, selecting sites and managing connectivity, choosing target species and ecosystems, managing key ecosystem interactions and micro-climates, identifying and mitigating site-level climate change risks, aligning the project with long-term policies, and designing a monitoring framework that enables adaptive management. A scan of restoration projects focussing on two regions – Brazil and countries of the Association of Southeast Asian Nations, ASEAN – revealed limited inclusion of these considerations in practice, with less than 5% of the projects evidently addressing at least one of the seven areas. We discuss two projects showing good practice in climate resilient restoration: restoration of Atlantic forest in Brazil that plans for climate change in connectivity and hydrological management, species selection, and policy alignment, and crayweed underwater forest restoration in Sydney, Australia, whose careful attention to species provenance, genotype measurement and monitoring provided a “future-proofing” approach to restoration success in the long term. Building on such examples, our framework can be used as a tool to support global restoration targets and the UN Decade on Ecosystem Restoration 2021–2030 through more climate resilient restoration.