Biogeographic assessments: A framework for information synthesis in marine spatial planning

Abstract

This paper presents the Biogeographic Assessment Framework (BAF), a decision support process for marine spatial planning (MSP), developed through two decades of close collaborations between scientists and marine managers. Spatial planning is a considerable challenge for marine stewardship agencies because of the need to synthesize information on complex socio-ecological patterns across geographically broad spatial scales. This challenge is compounded by relatively short time-frames for implementation and limited financial and technological resources. To address this pragmatically, BAF provides a rapid, flexible and multi-disciplinary approach to integrate geospatial information into formats and visualization tools readily useable for spatial planning. Central to BAF is four sequential components: (1) Planning; (2) Data Evaluation; (3) Ecosystem Characterization; and (4) Management Applications. The framework has been applied to support the development of several marine spatial plans in the United States and Territories. This paper describes the structure of the BAF framework and the associated analytical techniques. Two management applications are provided to demonstrate the utility of BAF in supporting decision making in MSP.

Full text

Biogeographic assessments: A framework for information synthesis in
marine spatial planning
Chris Caldow
a,⁎
, Mark E. Monaco
a
, Simon J. Pittman
a,b
, Matthew S. Kendall
a
,
Theresa L. Goedeke
a
, Charles Menza
a
, Brian P. Kinlan
a,c
, Bryan M. Costa
a,c
a
National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science Center for Coastal Monitoring &Assessment, Biogeography
Branch SSMC4, 1305 East-West Highway, Silver Spring, MD 20910, United States
b
Centre for Marine and Coastal Policy Research, Marine Institute, Plymouth University, Drake Circus, Plymouth PL4 8AA, United Kingdom
c
CSS-Dynamac Inc., 10301 Democracy Lane, Suite 300, Fairfax, VA 22030, United States
article info
Article history:
Received 12 May 2014
Received in revised form
23 July 2014
Accepted 28 July 2014
Keywords:
Coastal and marine spatial planning
Spatial predictive modeling
Human uses
Ecosystem-based management
Seascape ecology
abstract
This paper presents the Biogeographic Assessment Framework (BAF), a decision support process for
marine spatial planning (MSP), developed through two decades of close collaborations between
scientists and marine managers. Spatial planning is a considerable challenge for marine stewardship
agencies because of the need to synthesize information on complex socio-ecological patterns across
geographically broad spatial scales. This challenge is compounded by relatively short time-frames for
implementation and limited financial and technological resources. To address this pragmatically, BAF
provides a rapid, flexible and multi-disciplinary approach to integrate geospatial information into
formats and visualization tools readily useable for spatial planning. Central to BAF is four sequential
components: (1) Planning; (2) Data Evaluation; (3) Ecosystem Characterization; and (4) Management
Applications. The framework has been applied to support the development of several marine spatial
plans in the United States and Territories. This paper describes the structure of the BAF framework and
the associated analytical techniques. Two management applications are provided to demonstrate the
utility of BAF in supporting decision making in MSP.
Published by Elsevier Ltd.
1. Introduction
Marine spatial planning (MSP) is rapidly emerging as a viable
approach for comprehensive and efficient management of coastal
and marine environments around the world [14,23,16]. If built on a
foundation of reliable and objective ecological and sociological
information, this evolution of marine planning is expected to
maintain essential ecosystem services, encourage compatible uses,
minimize resource use conflicts, evaluate tradeoffs in an open and
transparent manner, and include significant and meaningful
stakeholder involvement [32]. Implementing MSP, however, is a
considerable challenge for marine stewardship agencies, in large
part because gaps exist in available data and syntheses of data on
spatially heterogeneous and dynamic socio-ecological systems are
extremely complex [14,20,29,79].
While it may be judicious to embrace the enormous complexity
of ecosystems and work toward complete descriptions of ecologi-
cal systems, pragmatism of management systems will likely
necessitate a more limited focus on special areas, vulnerable
resources and a subset of critical patterns and processes such as
key drivers in the structure and function of the system. With this
pragmatic approach, the U.S. National Ocean Policy (NOP), adopted
by Executive Order 13547, advises regional planning bodies to
analyze, assess and forecast information on key characteristics
of coupled social–ecological systems (Box 1). These Regional
Assessments are considered one of the essential elements of the
spatial plan.
Even with this narrowed scope, historically, limited data cover-
age for both spatial and temporal dimensions, combined with
issues of limited data access, has made effective information-based
strategic planning in the marine environment a major technical
challenge. In the past decade, however, there have been rapid
technological advances in environmental sensors, considerable
investments in long-term monitoring and a proliferation in remote
sensing systems for acquisition of marine environmental data at a
range of spatial and temporal scales [11,24,34]. In addition,
advances in the spatial modeling of ecological patterns and
processes, such as ocean hydrodynamics, watershed hydrology,
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/marpol
Marine Policy
http://dx.doi.org/10.1016/j.marpol.2014.07.023
0308-597X/Published by Elsevier Ltd.
n
Corresponding author. Tel.: þ1 301 7133028.
E-mail addresses: Chris.Caldow@noaa.gov (C. Caldow),
Mark.Monaco@noaa.gov (M.E. Monaco), Simon.Pittman@noaa.gov (S.J. Pittman),
Matthew.Kendall@noaa.gov (M.S. Kendall),
Theresa.Geodeke@noaa.gov (T.L. Goedeke), Charles.Menza@noaa.gov (C. Menza),
Brian.Kinlan@noaa.gov (B.P. Kinlan), Bryan.Costa@noaa.gov (B.M. Costa).
Marine Policy 51 (2015) 423–432

biological distributions and larval connectivity, allow us to predict,
visualize and better explain complex ecosystems [68,75,93,46].
Simultaneously, the diversity and geographical scope of mapped
socio-economic data has also increased [6,13]. The development of
reliable and cost-effective spatial models has been aided by
identification of useful surrogates or proxies for complex spatial
patterns that are difficult to map directly, such as species distribu-
tions, ecological function, and ecosystem service values [51,76,87].
Significant progress has also been made in data sharing through
institutional contributions to open access data portals and the
broadening of public participation in data collection (i.e., citizen
science and crowd sourcing) [11,86,30].
Less focus, however, has been directed at the development of
conceptual and analytical frameworks for prioritizing, analyzing
and communicating complex, spatially explicit and non-linear
socio-ecological patterns and processes [81,29,79]. This gap pre-
sents a significant challenge for the operationalization of MSP that
is made more urgent as ocean uses increase and diversify globally.
Typically, the MSP process involves multiple stakeholder groups
with different, sometimes competing, goals for the use and
management of the same geographical space. Therefore, balancing
human uses to minimize conflict between users, ensure long-term
environmental sustainability, and maximize the value of ecosys-
tem services delivered is a primary challenge for MSP ([79,90]).
Effective decision making in MSP, particularly where there are
many stakeholder groups with highly divergent interests, requires
a framework for data synthesis that provides a comprehensive,
transparent and reliable science-based approach, accounts for
uncertainty in the data, and provides sufficient flexibility to enable
objective scenario assessment.
The Biogeographic Assessment Framework (BAF), a flexible,
multi-disciplinary approach to integrate geospatial information
into formats and visualization tools readily useable by coastal
managers has been developed. This framework has evolved from
two decades of close partnerships with natural resource managers
addressing complex problems in both temperate and tropical
marine and coastal environments [55]. The BAF incorporates a
broad spatial ecology perspective that integrates concepts and
techniques from traditional ecology, biogeography, landscape
ecology, sociology and economics, remote sensing and the emer-
ging fields of spatial eco-informatics and computational ecology
[92,54,15,91]. Although the BAF approach shares some attributes
with NOAA's Integrated Ecosystem Assessments (IEA), the two
approaches support different, but complementary objectives. BAF
provides a comprehensive spatial characterization and user con-
flict assessment to support spatial planning, whereas IEAs provide
a structure to assess ecosystem status, risk to ecosystem indicators
and the impact of management decisions within an adaptive
management process [45]. The BAF is a rapid and flexible approach
for responding to the relatively short time scales that are typical
for implementation of management actions, such as the develop-
ment of marine spatial plans, marine protected area management
plans or evaluations of MPA design. The BAF usually relies on
existing data sets, not all portions of the ecosystem need to be
included, indicators are not required, and when compared with
IEA, the BAF focuses more on spatial variation.
This paper presents an overview of the structure of the
Biogeographic Assessment Framework and associated analytical
techniques to demonstrate the utility of the framework in support
of marine spatial planning in the United States of America (USA).
2. Methodology
2.1. Conceptual background for the Biogeographic Assessment
Framework (BAF)
To understand how MSP can benefit from implementing the BAF, it
is necessary to first define the subject of biogeography which provides
concepts and techniques that underpin the framework. In essence,
biogeography is the study of the spatial and temporal distributions of
organisms, including people, and their habitats, and the historical and
biological drivers of distributions [10]. Application of biogeographic
concepts and analytical approaches have made major contributions to
conservation planning, particularly in classifying regions with distinct
characteristics and explaining patterns in species distributions and
biodiversity [48,81]. Typical results from ecological biogeography
range from distribution maps for species or habitats to more complex
ecological analyses that integrate biological, physical and sociological
variables to create maps of biodiversity and human activities within a
region [59,75,56]. Biogeographic studies are usually perceived as global
or continental in spatial extent and often concerned with geological
time scales. However, the approach can be applied at finer scales. BAF
as described here considers spatial and temporal domains that focus
on more recent patterns (typicallyo30 years from present) than
conventional studies in Biogeography and are analyzed at sub-
continental spatial extents.
2.2. Operational attributes of the Biogeographic Assessment
Framework (BAF)
BAF is designed to display diverse, spatially complex and multi-
scale biogeographic information in a readily consumable manner
via maps and spatial analyses aimed at supporting the manage-
ment decision making process. At the core of the BAF analytical
process are a suite of interoperable spatial technologies including
Geographical Information Systems (GIS), remote sensing image
analysis software, statistical data mining algorithms for predictive
modeling, web-based mapping tools and database Management
Applications. Although representation of ecologically realistic
patterns is less problematic in data rich regions, operationally
the BAF approach is flexible enough to also efficiently address
Box 1–Suggested data needs for the Regional Assessment
component of the U.S. Government Framework for Effective
Coastal and Marine Spatial Planning (Interagency Ocean Policy
Task Force, 2009).
1. Important physical and ecological patterns and pro-
cesses (e.g., basic habitat distributions and critical
habitat functions) that occur in the planning area,
including their response to changing conditions;
2. Ecological condition and relative ecological importance
or values of areas within the planning area, using
regionally-developed evaluation and prioritization
schemes;
3. Economic and environmental benefits and impacts of
ocean, coastal, and Great Lakes uses in the region;
4. Relationships and linkages within and among regional
ecosystems, including neighboring regions both within
and outside the planning area and the impacts of
anticipated human uses on those connections;
5. Spatial distribution of, and conflicts and compatibilities
among, current and emerging ocean uses in the area;
6. Important ecosystem services in the area, and their
vulnerability or resilience to the effects of human uses,
natural hazards, and global climate change;
7. Contributions of existing placed-based management
measures and authorities; and
8. Future requirements of existing and emerging ocean,
coastal, and Great Lakes uses
C. Caldow et al. / Marine Policy 51 (2015) 423–432424

situations with very sparse data, or where only specific compo-
nents of the environment are being considered. This is usually
achieved through spatial predictive modeling using techniques
such as machine-learning algorithms and geostatistical modeling
to fill data gaps [74,77]. An important focus of BAF is the
quantification of uncertainty arising from data deficiencies and
data processing. This focus on uncertainty throughout the data
synthesis process has been an important attribute of BAF for
decision-makers faced with public scrutiny in an evidence-based
planning process. As such, BAF has evolved a framework to assess
spatially-explicit errors using statistical model validation techni-
ques and a range of quantitative tools for map accuracy assess-
ment from the fields of remote sensing [58].
Finally, the framework is amenable to both question-driven
science, as well as more exploratory pattern-recognition
approaches such as ecological data mining to identify key envir-
onmental predictors. The approach presented here recognizes that
MSP is a dynamic and adaptive process and decision makers will
have diverse and changing driving forces at different stages of the
process. Thus, the BAF allows for an incremental step-wise
implementation from initial discussions to define the problem,
through to information provisioning for a comprehensive coupled
socio-ecological system, including scenario modeling for future
conditions.
The BAF structure has four major components that support the
marine spatial planning process: Step 1 –Planning involves identifying
specific goals and objectives and the relevant geographical extent(s) or
study area; Step 2 –Data Evaluation includes data collection and
compilation, as well as assessment of the geographical, temporal and
compositional extent of data including evaluation of gaps. Crucial at
this step is understanding data and model errors, corresponding
caveats, methods of visualizing BAF output uncertainty and suitability
of data for the intended purpose; Step 3 –Ecosystem Characterization
involves a synthesis of the best-available data to map and describe
key ecosystem patterns and processes; and Step 4 –Management
Applications focuses on support for operationalization of BAF products
to directly address management problems (Fig. 1).
2.2.1. Step 1: Planning
Developing a direct connection with the management commu-
nity tasked with making spatially-explicit marine management
decisions is instrumental to the success of the BAF. Open dialog
and collaborative goal setting between scientists, managers and
stakeholders serves as the starting point to define the framework
to meet specific needs. As such, the logical sequence of steps is
adaptable and has feedback loops in the information flow. For
instance, goal setting at the planning stage (Step 1) may first
require exploration of available data (Step 2) resulting in a return
to planning after initial data evaluation. Geographically, the plan-
ning step will define: (1) the core focal area and the spatial extent
of the surroundings i.e. site context; and (2) management goals,
targets, priority resources and threats, data providers, collabora-
tors and relevant logistical and political issues. The BAF recognizes
that it is necessary for local planning actions to be placed in the
broader ecological context relative to regional biogeography,
ecological connectivity across multiple spatial scales, threats and
neighboring management activities.
2.2.1.1. Selecting spatial and temporal extents. In general, the spatial
extent for a biogeographic assessment is determined by the
assessment's objectives, although it is common to extend the area
of interest beyond the borders of the management jurisdiction to
provide broader geographical context. As such, a single study may
encompass several spatial scales to understand the distances over
which ecosystem components interact, or they may focus on a single
scale to identify the specific details of a particular organism's
distribution or other system component. This ‘ecological neigh-
borhood’approach to scaling environmental patterns is frequently
applied in terrestrial landscape ecology [1], but is equally relevant to
marine systems [73,69]. With regard to the temporal domain,
biogeographic assessments include consideration of a range of time
scales which may resolve changes in habitat or species distribution
daily, monthly, seasonally, inter-annually or over much broader
Minimizing
Conflicts
Designing Marine
Protected Areas
Managing Marine
Resources
Oceanographic
Analysis
Define Goals &
Objectives
Define the Study Area
Habitat
Analysis
Biological
Analysis
Planning for
Alternative Energy
Planning Ecosystem Characterization Management Applications
Socioeconomic
Analysis
Data Evaluation
Data
Acquisition
Data Gap
Identification
Data Content &
Quality
Fig. 1. NOAA's Biogeographic Assessment Framework (BAF) to support marine spatial planning. A logical sequence of steps in information synthesis: Step 1: talking with
managers to determine priorities; Step 2: assessing the data and identifying data gaps; Step 3: characterizing the ecosystem patterns and processes including human
activities across the area of interest; and Step 4: working with managers to support specific management applications.
C. Caldow et al. / Marine Policy 51 (2015) 423–432 425

temporal scales such as climate change mediated geographical shifts
[70].
Addressing four key questions helps determine the spatial
extent of the area for which data will be acquired and analyzed:
i.) What is the extent of jurisdictional boundaries or sub-regions
of interest for MSP?
ii.) Does the MSP involve only marine areas or does it include
adjacent terrestrial watersheds to account for factors such as
runoff from land that can impact downstream and near-
shore areas?
iii.) Are there important regulatory boundaries, human activities
or anthropogenic influences in adjacent areas that will affect
the focal area?
iv.) Is maintenance of key marine resources connected to and
dependent on populations, patterns and processes occurring
in neighboring areas?
2.2.2. Step 2: Data Evaluation
2.2.2.1. Data acquisition. Raw datasets for biogeographic assessments
often originate from many different organizations, and are collected
by a wide array of sensors and sampling techniques by scientists
representing a range of disciplines. Direct interaction with the
research community and data providers facilitates understanding of
data idiosyncrasies and caveats. The past decade has experienced a
rapid proliferation of online data portals and meta-portals (portals of
portals) housing vast archives of easily accessible environmental
information (e.g. OBIS Ocean Biogeographic Information System –
http://www.iobis.org/; Marine Map http://marinemap.org/). In the
USA, several nationwide portals have emerged recently specifically to
support MSP related processes (e.g., Ocean.Data.Gov, Multipurpose
Marine Cadastre –http://marinecadastre.gov). In some cases, data use
agreements may be required to gain access to data. Once the data is
obtained they are migrated into a standardized geospatial format and
metadata is compiled. Questions that can be asked at this step are:
i.) Do the data contain sufficient temporal and spatial coverage?
ii.) Do the data contain sufficient number of observations?
2.2.2.2. Assessment of data gaps and gap filling techniques. Decision
making in MSP often requires spatially continuous and broad scale
data on the distribution of both living and non-living resources
and their interactions. Geographic and compositional data gaps
can adversely impact decision making. Major issues include how
to appropriately generalize fine-scale data that will necessarily
contain gaps and how to address decision makers' and policy
makers' sensitivity to uncertainty. Direct and spatially continuous
measurements of many key ecological variables, however, are
usually unavailable, even for the most intensively studied
regions of the earth. Gap filling techniques fall into several main
categories: (1) Merging of existing datasets; (2) Acquisition of new
data; or (3) Extrapolating across geographical space, even into
data-less areas, using statistical relationships between existing
data layers (i.e. predictive mapping). These options are not
mutually exclusive and selection of an option or combination of
techniques is typical, and should be considered in the context of
cost-effectiveness, timeliness and data quality. New data collection
can include surveying expert opinion (e.g. workshops, in-person
interviews and online questionnaires) including traditional
ecological knowledge, or deploying new instruments and
conducting new field surveys.
A variety of statistical predictive modeling techniques ranging
from geostatistical interpolation of data (kriging) to statistical
models that use predictor variables (e.g., species distribution
modeling, machine-learning) to dynamic ecological models, or a
hybrid of these approaches, have proven to be rapid, reliable and
cost-effective for filling data gaps [82,18,40,77]. In general,
attempts to “scale up”, or interpolate, using simple geostatistical
or other purely spatial interpolation approaches applied to rela-
tively fine scale and sparse survey data often results in high spatial
error [42]. Surrogate environmental variables, however, particu-
larly remotely sensed satellite measurements, bathymetry and
derivatives that represent primary environmental resources (e.g.,
heat, light, primary productivity, etc.) or quantify seafloor topo-
graphic complexity, often offer useful predictors of marine biotic
distributions [52,76,8].
2.2.2.3. Data content and quality. Regardless of the approach taken
to fill data gaps, errors in the source data or errors accumulated
during processing can proliferate during data synthesis, generating
complex spatial patterns of uncertainty. In MSP, confidence in data
is important and accounting for and communicating uncertainty is
critical to a successful evaluation process. To aid decision-makers
and resource users in understanding data reliability, BAF quantifies
and documents errors and uncertainties throughout project
development.
When statistical methods are used to fill gaps, an estimate of
uncertainty can be generated as part of the algorithm. It is
important to retain and map these uncertainties (Fig. 2), and also
when possible to distinguish and communicate the sources of
uncertainty. Three major sources of uncertainty are as follows:
(1) measurement or observation error (e.g. the observer miscounts
the organism), (2) errors arising from assumptions and approx-
imations made in the statistical modeling process, and (3) inherent
variability in the ecological process of interest (e.g. the organism
moves, aggregates, and disperses) [31,4,12].
Statistical estimates of uncertainty from models are often depen-
dent on the assumptions of the models themselves making it
necessary to assess the accuracy of a predictive model by techniques
such as cross-validation, a procedure in which random subsets of data
are “held out”from analysis and used to estimate model performance.
A variety of statistics can be used to assess different components of
model accuracy and performance [47]. When predictions are mapped,
an independent map accuracy assessment should be carried out to test
the predictions of the model across the entire range of possible
conditions and observations. When interpolating or forecasting highly
uncertain or dynamic processes, a variety of modeling approaches may
be appropriate. For instance, ensemble or model-averaging approach,
in which the predictions or performance of multiple models are
combined or compared is useful for scenario analysis of future change
when the behavior of a complex system is poorly known or poorly
constrained by observations [57].
Finally, even when the methods employed do not allow for
spatially explicit, quantitative assessment of uncertainty, it is still
important and valuable to provide a qualitative or semi-quantitative
estimate of uncertainty. Such estimates can be based, for example, on
the age of information, method of collection, or expert judgment [84].
In fact, even when quantitative information on uncertainty is available
it is often useful to reduce it to qualitative categories for decision-
making and communication purposes (Fig. 2d; [53]).
2.2.3. Step 3: Ecosystem Characterization
The Ecosystem Characterization identifies, quantitatively describes
and maps key patterns related to biological, physical and chemical
processes, as well as human activities and interests that are linked to
the ecology of a region. This baseline information forms the essential
spatial template to support information-based decision making in an
ecosystem-based management strategy for MSP [43,7,20].
C. Caldow et al. / Marine Policy 51 (2015) 423–432426

2.2.3.1. Defining and mapping social, cultural and economic
attributes. MSP requires that the range and complexity of social
connections to marine and coastal ecosystems be represented in the
planning process at spatial scales that are meaningful to both
decision-makers and stakeholders [9,85]. Planning for the
sustainable development of some spaces can be complex because
of the presence of concurrent, mixed-use patterns of human activity,
as well as a diversity of ecological systems. To characterize the
socioeconomic seascape, spatial and temporal data are required on a
wide variety of human uses (e.g. human activities, economic and
values mapping), including the intentional non-use of spaces
[19,88,62,78].
Questions that can be asked to guide the process are
i.) Which ecosystem goods and services do people in the focal
region value most and why?
ii.) What is the spatial and temporal distribution pattern of
human activities and resource use?
iii.) What are the factors affecting these patterns?
iv.) What and where are the areas with most the competition over
the use of marine resources?
v.) How and to what degree is the use of these goods and services
facilitated or mitigated by social policy?
The BAF approach compiles socio-economic data to characterize
the spatial and temporal patterns of human activities in coastal and
marine environments (e.g., development, fishing, oil rigs, ship
traffic, etc.), as well as related ecological threats from those activities
(e.g., pollution from coastal run-off, over-fishing of economically
important species, vessel collisions with large marine mammals,
etc.). Socio-economic data can also be mapped [5,2,61] and BAF
products can include a range of mapped patterns such as social
values , social–ecological hotspots, and analyses that compare the
density of human activities with social values. Additionally, assess-
ments can be conducted that evaluate the trade-offs between
market and non-market values of ecosystem services [83,90],to
Fig. 2. Example of a spatial modeling process in BAF Step 2 (data evaluation) used to fill gaps and communicate uncertainty in ecological data. (A and B) Statistical modeling
to fill spatial gaps by predicting values between original point survey data; (C) uncertainty in the predicted data is modeled and mapped quantitatively; and (D) predictions
and the spatial pattern of uncertainty are combined in a final map product. Adapted from Menza et al. [53] and Kinlan et al. [40].
C. Caldow et al. / Marine Policy 51 (2015) 423–432 427

ascertain the probable opportunity costs related to different man-
agement scenarios, along with the remediation or recovery costs for
the reduction or loss of valued marine uses.
2.2.3.2. Defining and mapping key ecological patterns and processes. The
biophysical component of marine Ecosystem Characterization is central
to the BAF as the foundation upon which all human activities occur.
Integrated analyses of human use maps with maps of important
ecological areas are crucial to the long term sustainability goals of
MSP [27,75,26]. Important ecological areas may include sites of high
productivity and biodiversity (hotspots); locations of special features,
communities and key species that are critical to ecosystem function and
resiliency (e.g. essential fish habitat, spawning and larval source areas);
rare, endangered or functionally vulnerable marine resources, and key
migratory corridors. This activity is central to the conservation of
ecologically and biologically significant areas (EBSAs) as directed by
the Convention on Biological Diversity. Within the BAF, identifying and
mapping discrete areas with characteristics of special interest is referred
to as ‘hotspot mapping’, yet the process can also involve mapping lower
value areas too (i.e. “warmandcoldspots”). A cold spot, for example,
might represent an area with lower concern and could therefore be a
potential candidate for human uses with low potential for conflict [41].
Thresholdsforwhatishighorlowcanbedrivenbypolicyor
established from exploration of the data, underlying ecological
processes, and expert opinion.
A major challenge in MSP is incorporating key ecological
dynamics in the planning process because conventional maps
tend to represent dynamic components as static patterns and
multi-dimensional structure as two-dimensional maps. Essentially
this represents the planning area as a ‘static flatscape’. While still
useful for many applications, in reality many key patterns of
interest change daily, seasonally, and over longer time-scales. It
is important to capture these patterns and processes as spatial
management decisions (e.g. placement of shipping lanes, wind
farms, marine protected areas, etc.) typically do not shift over
short (o1 year) to moderate (decades) time frames. This may be
approached by integrating key statistics of temporal dynamics
(e.g., variance) over an appropriate climatological time scale
chosen to match the planning process, often with stratification
to represent distinct period of short-term variability (e.g., seasons,
El Nino vs non-El Nino years; [41,35]). Alternatively, time series
approaches can be taken that map, model, and convey the full
range of variation through time.
Ecological connectivity is a major factor in the resilience and
sustainable management of marine ecosystems [22] and should be
considered in MSP. Larval connectivity in the ocean influences
population structure, resilience and the performance of manage-
ment strategies and human activities operating outside a managed
jurisdiction can impact what is happening inside. In all coastal
areas, land and sea are intricately linked through material
exchanges, such as runoff, river outflow and inflow and migratory
movements of animals. Capturing this connectivity involves
understanding the inputs into the system and pathways
(e.g. currents, habitat corridors, migrations of organisms) through
which they travel. With the application of tracking techniques
(i.e. telemetry, archival tags) and spatial models for predicting
hydrodynamics and predicting organism dispersal patterns,
ecological connectivity can been incorporated into the BAF
process [38].
2.2.4. Step 4: Management Applications
The Management Applications step of BAF provides support for
operationalization of BAF information products, typically provided
in the format of digital maps, to address specific management
challenges and questions. In support of the rapid emergence of
MSP in the United States, the four key components of the BAF
(Planning, Data Evaluation, Ecosystem Characterization, and Man-
agement Applications) have been variously applied to support
strategic planning for U.S. National Marine Sanctuaries and MSP
for U.S. States including: The Commonwealth of Massachusetts
Ocean Plan, New York Department of State Offshore Spatial Plan
and Oregon State Territorial Sea Plan. In addition, the framework
also may support information acquisition, analyses and interpreta-
tion as required for the “Regional Overview and Scope of Planning
Area”and the “Regional Assessment”tasks to be carried out by the
nine Regional Planning Bodies of the U.S. IOPTF, 2010 [33]. It also
addresses several tasks listed under Steps 5 and 6 “Defining and
Analyzing Existing and Future Conditions”of the UNESCO gui-
dance document for marine spatial planning [17].
An important emerging challenge for the Management Appli-
cations step is the provision of alternative space use scenarios and
consequences to examine options for balancing uses and evaluat-
ing trade-offs [39,90,44,79]. The development and comparison of
alternative management scenarios helps decision-makers to
anticipate the probable implications of competing management
options, such as the ecological and/or social tradeoffs likely to
result from choosing one alternative over another. In this way, the
BAF is invaluable at helping decision-makers to predict potential
impacts from alternate ocean planning strategies.
Finally, the Management Application step also provides fore-
casting of future ecosystem conditions to support longer-term
strategic planning, societal adaptation plans and risk assessment
relative to changes in the availability ecosystem services that are
highly valued by society. Typical examples of the successful
application of BAF spatial products in marine management set-
tings include optimal siting of offshore energy installations [41],
management plan revisions [63], boundary evaluations and siting
of marine protected areas [64,37], and minimization of user
conflict in multi-use areas [3].
3. Case studies
3.1. Case study 1: Data synthesis to support offshore renewable
energy planning for New York
In 2012, a biogeographic assessment was conducted by NOAA's
National Centers for Coastal and Ocean Sciences (NCCOS) to
support the New York Department of State (NYDOS) in evaluating
potential risks to vulnerable habitats and species of conservation
concern from proposed renewable energy installations [53]. BAF
products were used to inform a larger collaborative process
intended to minimize conflict between siting of offshore wind
farms, environmental protection and other existing ocean uses
(Fig. 3).
Step 1 focused on identifying the goals and objectives of the State's
offshore spatial plan, the spatial extent of the planning area and a
comprehensive evaluation of existing information. This initial scoping
process was informed by multiple regional and state-level natural
resource assessments [71,25,21]. The biogeographic assessment was
structured to focus on investigating the offshore distribution of seabirds,
deep sea corals, benthic habitats and oceanography, and on providing
recommendations for integrating these data and other data layers into
New York's offshore spatial plan. The Data Evaluation (Step 2) compiled
existing ship-based visual surveys and remote sensing data to create
maps for living marine resources and physical habitat metrics (e.g.,
bottom types, benthic topographic metrics, surface chlorophyll, water
column stratification). Oceanographic data served as proxies for pre-
dicting the habitat suitability of key living marine resources for which
there were few observations (i.e., less common birds, productivity).
Data were obtained from online spatial databases (e.g. OBIS-SEAMAP
C. Caldow et al. / Marine Policy 51 (2015) 423–432428

http://seamap.env.duke.edu/, NOAA Environmental Research Division's
Data Access Program nodes [http://coastwatch.pfeg.noaa.gov/erddap])
and federal data repositories (e.g. National Geophysical Data Center
http://www.ngdc.noaa.gov/, Deep Sea Coral Research and Technology
Program http://coralreef.noaa.gov/deepseacorals/). Predictive modeling
was used to (1) fill spatial and temporal gaps in measurements when
data were collected with irregular survey effort; (2) translate the
distribution of metrics into spatial and/or temporal scales commensu-
rate with planning decisions; and (3) obtain spatially-explicit estimates
of uncertainty (Fig. 2). Synthesized model outputs from the biogeo-
graphic assessment were compiled into a common spatial framework to
facilitate analysis and integration with additional ecological and human
use data, and analytical methods and caveats of data use were
described. The creation of standardized digital data, metadata and
reports for data users were critical components of Step 2 and supported
improved transparency, credibility and transferability, key elements of
the National Ocean Policy [32].
An Ecosystem Characterization (Step 3) was conducted to
evaluate threats to priority species and habitats [67] using existing
criteria developed by the State to designate significant coastal fish
and wildlife habitats, as recognized by the New York Waterfront
Revitalization and Coastal Resources Act of 1981 [66]. Delineated
unique and vulnerable areas formed a base layer for defining
discrete managed areas and understanding connectivity between
state and federal jurisdictions. Areas recommended by New York
for wind energy production will likely be used to determine
official wind energy areas (WEAs), and could lead to the issuance
of renewable energy production leases on the outer continental
shelf by the U.S. Bureau of Ocean Energy Management (BOEM).
3.2. Case study 2: Biogeographic Assessment to support MPA design
within Gray’s Reef National Marine Sanctuary
The stepwise BAF approach was applied to support Gray's Reef
National Marine Sanctuary (GRNMS) in evaluating design alter-
natives for a new research area within the existing Sanctuary for
the purpose of long term scientific research [36] (Fig. 4).
In partnership with the NOAA Office of National Marine
Sanctuaries planning (BAF Step 1) solicited input through public
meetings and also established a multidisciplinary team of experts
and stakeholders to consider the need, feasibility, and general
characteristics desired for the research area. The working group
used consensus-based decision making to establish geographic
scope, ecological criteria and the need to balance any spatial
restrictions with stakeholder concerns particularly related to its
use for recreational fishing. The BAF took the general criteria
agreed upon during the planning phase and sought ecological and
human-use datasets to represent them (BAF Step 2). Fish and
habitat mapping data were acquired by working directly with
scientific experts in the region to represent ecosystem variables
(15 variables). On-water surveys, positions of prior research, aerial
counts of boater use, and patterns of fishing related marine debris
were acquired through consultation with management agencies,
scientists, and monitoring programs to represent human-use (10
variables). From these datasets, using a GIS-based analysis that
was custom-designed to meet the needs of the working group, the
ecosystem (environmental patterns and human uses) was spatially
characterized [37] (BAF Step 3). Analytical results and a geodata-
base were used by the working group to identify a range of
acceptable alternative boundary scenarios that balanced the con-
flicting scientific needs, enforcement logistics, and fishermen
concerns (BAF Step 4). The alternative boundary scenarios under-
went a socioeconomic impact analysis to estimate costs and
benefits to the local economy. The recommendations from the
working group and analytical results of the BAF were used in the
Draft and Final Environmental Impact Statements used to establish
the research area which was legally established in 2011 [65].
4. Discussion
MSP is an integrated, trans-disciplinary and spatially complex
decision making process requiring a highly effective organizing
framework for data synthesis and interpretation. In the United
Fig. 3. Aflowchart of the inputs, data layers, analyses and management products for the biogeographic assessment of the New YorkBight. The biogeographic assessment was
developed to support effective spatial planning for offshore renewable energy with special consideration for protection of vulnerable species and habitats. Adapted from
Menza et al. [53].
C. Caldow et al. / Marine Policy 51 (2015) 423–432 429

States of America the importance of implementing MSP as an
ecosystem-based management (EBM) approach has recently been
addressed by adoption of the National Ocean Policy in 2010. When
implemented as a comprehensive, adaptive, integrated spatial
planning process, MSP is considered to directly support the
principles of EBM by integrating social–ecological information
for a specific geographical location [72,50]. The BAF provides an
analytical approach to feed ecological data including human uses
into the MSP process to support an EBM approach. Virtually all
specifications of marine EBM share at least three common ele-
ments: (1) a commitment to establishing spatial management
units based on ecological rather than political boundaries; (2) con-
sideration of the relationships among ecosystem components, the
physical environment, and human communities; and (3) the
recognition that humans are an integral part of the ecosystem.
The social sciences provide great insight into the development and
implementation of EBM approaches [72,80]. Anthropology, econom-
ics, legal studies, and sociology provide alternative understandings of
how institutional designs affect human behavior, resource allocation,
and outcomes for the economy, communities, families, and the
environment [90]. It falls within the realm of social sciences to
provide information on the social dimensions of EBM by addressing
issues of governance, property rights, and human behavior. However,
reliable spatial data documenting human activities and values is
extremely sparse for coastal and marine environments. This paucity of
spatially-relevant socioeconomic data is problematic for MSP.
Environmental management can be a subjective endeavor
attempting to manipulate natural resources and the environment
into outcomes desired by humans [90]. MSP provides an open
process to balance competing uses in space and time to optimally
balance protection and utilization of ecological services provided
by coastal and marine ecosystems [80]. If MSP processes are
carefully conducted, compromises can be found to balance com-
peting conservation and societal uses of the coastal ocean [90,14].
Sustainable outcomes across ocean uses, users, and conservation
of coastal ecosystems should be a goal of ecosystem-based
management and the Biogeographic Assessment Framework has
been proven to be a robust approach to facilitate planning,
ecosystem characterization and evaluation of alternative manage-
ment scenarios. As demonstrated in our case studies, the BAF
advances EBM by coupling of science and management
information needs.
A major challenge for any broad scale data synthesis for the
marine and coastal realm is the great variability in the availability
of data. With the use of a wide range of powerful and flexible
predictive modeling algorithms BAF has been applied effectively
across a gradient of data richness from sparse to relatively data
dense regions. In regions where data are extremely limited,
judicious assessment of uncertainty will be required. Furthermore,
a major challenge in multi-disciplinary data synthesis relates to
the diversity of spatial and temporal scales at which data are
collected. Clearly, frameworks that are inherently multi-scale are
necessary to facilitate data integration, but greater focus is needed
to understand the impact of scale and the consequences of
potential mismatches between ecologically meaningful scales
and the operational scales that are relevant to decision making
in MSP. Scale considerations and a full accounting of uncertainty
will be particularly important to consider when MSP incorporates
climate change forecasting into the planning process.
Another potential limitation for effective MSP is the lack of data
on human use patterns in many regions, even for otherwise data
rich regions. However, increasing effort is being focused on the
acquisition of socio-economic data, including mapping of human
use patterns and the cultural and economic values of seascapes
[85,83,89]. Inclusion of local ecological knowledge is also begin-
ning to play an important role in data synthesis for MSP [49]. This
progress will dramatically improve our ability to conduct compre-
hensive biogeographic assessments focused on risk assessment
and conflict resolution through integration of highly resolved
spatial-explicit socio-economic data with environmental data
[28,60]. As the availability of human geography data increases,
trade-off analyses among ecosystem services will play an increas-
ingly important role in MSP [90]. Lastly, improvements are
urgently needed to ensure that information is easily accessible
and effectively communicated to the public to enable an inclusive,
integrated and transparent planning system.
Acknowledgments
We are grateful to the many marine managers and practitioners
of marine spatial planning across the United States whose engage-
ment on the technical challenges of implementing MSP have
helped evolve the framework presented here.
The research was funded by the U.S. National Oceanic and
Atmospheric Administration's National Centers for Coastal Ocean
Science and Office of National Marine Sanctuaries. BPK, SJP and
(BAF 1-2)
Boundary Configurations
Shapes: square, rectangle,
hexagon
Sizes: 4, 6, 9, and 16 km2
Rotations: 0, 30, and 45
(BAF 4)
Step-wise selection process
Stakeholders eliminate unacceptable boundary options
(BAF 4)
Socioeconomic
impact analysis
(BAF 3)
GIS-based sliding
window analysis
(BAF 1-2)
Identify variables
Ledges
Bottom type
Prior research
Fishing variables
Fig. 4. Biogeographic Assessment Framework applied to the design and evaluation of a new marine protected area for long-term research within Gray's Reef National Marine
Sanctuary, Georgia, USA (adapted from [37]).
C. Caldow et al. / Marine Policy 51 (2015) 423–432430

BMC were supported under NOAA Contract no. DG133C07NC0616
with CSS-Dynamac Inc.
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