Spatial_data_Doc7_150913

Integrating Spatial Data into
Marine Protected Area
Monitoring and Management

AUTHORS
Jessica Williamsa, b
, Dina Liebowitzb
, and Paulo Serpac
a
The Division of Science and Environmental Policy, California State University Monterey Bay,
100 Campus Center, Seaside, CA 93955, USA
b
California Ocean Science Trust, 1330 Broadway Suite 1530, Oakland, CA 94612, USA
c
California Department of Fish and Wildlife, 20 Lower Ragsdale Rd., Suite 100, Monterey, CA
93940, USA
ABOUT THIS REPORT
This report is a collaborative effort between California State University Monterey Bay (CSUMB),
California Ocean Science Trust, and California Department of Fish and Wildlife and is intended
to provide information about the importance of spatial data integration in Marine Protected
Area (MPA) monitoring and management. This report is a part of the greater State of the Region
report that shares the results of the North Central Coast MPA Baseline Program and adds to the
comprehensive picture of California’s North Central Coast region.
ACKNOWLEDGMENTS
We thank the Ocean Protection Council’s California Seafloor and Coastal Mapping Program and
CSUMB Seafloor Mapping Lab for the high-resolution seafloor data, the MPA Baseline Program
projects for the ecological and socioeconomic data, and the Applied Marine and Watershed
Science Program at CSUMB for support. We also thank Emily Knight and Erin Meyer for valuable
edits and suggestions.
CITATION
Williams et al. 2015. Integrating Spatial Data into Marine Protected Area Monitoring and
Management. California Ocean Science Trust and California Department of Fish and Wildlife,
California.
PHOTO CREDIT
Cheryl Chen: 7; Claire Fackler: ii; Ryan Meyer: 1, 18; Reef Check: 7; Rick Starr: 12; USGS: 2, 3, 5

Table of Contents
Introduction ....................................................................................................................... 1
California Seafloor and Coastal Mapping in the North Central Coast Region ..................... 3
What is the CSCMP? ................................................................................................ 3
Why is the CSCMP important for monitoring? ........................................................ 4
How has the CSCMP been applied? ......................................................................... 5
Spatial Monitoring Data from the Baseline Period ............................................................. 7
Integration of Spatial Data ............................................................................................... 10
Applications to Modeling, Monitoring, and Management ............................................... 12
Landscape Ecology Models ................................................................................... 13
Ecosystem Connectivity Analyses.... ..................................................................... 14
Derivative Seafloor Maps ...................................................................................... 15
Developing and Future Applications ..................................................................... 18
References ....................................................................................................................... 19

1Integrating Spatial Data
INTRODUCTION
Spatially explicit data are a key component of successful ecosystem-based management,
including developing, monitoring, and managing marine protected areas (MPAs). From
understanding human use patterns to determining seafloor substrates and critical habitat
distribution, spatial data inform many aspects of MPA design, management, and long-term
monitoring. One important application of spatial data is the incorporation of seafloor mapping
data into monitoring to gain a greater insight into the dynamics of an ecosystem. Because
the distribution of marine species is in part governed by available substrate types, seafloor
mapping data are a critical component for tracking region-wide changes in habitats, as well as
species abundances and distributions over time. For example, in a relatively small area, different
species compositions occur over soft sediment versus along rocky substrate (Anderson et al.
2009). Identifying the locations and extents of these different habitat types is fundamental
in understanding the ecosystem as a whole when making spatial and temporal comparisons.
With California’s commitment to ecosystem-based management, spatial data will be integral
for successful management of marine resources, as well as anticipating and adapting for future
changes. Two key programs providing spatial datasets that inform MPAs in the North Central
Coast region of California are the California Seafloor and Coastal Mapping Program (CSCMP),
formerly the California Seafloor Mapping Program (CSMP), and the MPA Baseline Program.
The North Central Coast region includes 22 MPAs and 3 recreational management areas
that were implemented in 2010, covering approximately 20% of state waters in the region. The
region spans 584 kilometers (363 miles) of California’s coastline and contains a wide variety of
habitats, from sandy beaches and kelp forests, to the deep waters around the Farallon Islands.
Collectively, these habitats make up one of the most biologically productive areas in the world.
Until relatively recently, however, the details and distribution of some of the habitats in the
region were largely unknown. In order to improve understanding of the complex ecosystem in
the region, the California Seafloor and Coastal Mapping Program (CSCMP)
was established to map previously unmapped state waters. While
the CSCMP addresses a diverse set of state needs, such as identifying
navigation hazards and active offshore faults, modeling coastal change,
exploring sites for potential offshore energy, and creating a basemap for
research, the primary reason for the program was to support the Marine
Life Protection Act (MLPA) and the implementation of the MPA network.
Data from the CSCMP were initially used to establish representative coverage
of regional habitats within the North Central Coast MPAs and will also be an
important aspect of long-term monitoring and adaptive management.
In addition to the CSCMP, the North Central Coast MPA Baseline Program
was established to gather crucial information across a variety of topics to provide
a broad ecological and socioeconomic baseline characterization, or benchmark,
against which to make future comparisons. This baseline will provide a frame
of reference, and will support assessment of future changes and inform
adaptive management decisions. In order to establish a benchmark for
tracking ecosystem changes and evaluating MPA performance across
these various habitats, the Baseline Program included 11 projects
developed in collaboration with multiple partners, which highlighted

a suite of ecosystem features exemplifying the variety of ecosystems and human uses found
within the North Central Coast MPAs.* Biological, socioeconomic, and habitat mapping
data were collected in order to fill knowledge gaps and inform continued monitoring and
management. Additionally, the California Department of Fish and Wildlife (CDFW) collected
imagery data from remotely operated vehicles (ROV) across several underwater habitats and
aerial imagery of kelp distribution along the coast. ROV data allow for extensive surveys in
deeper water and as validation for remotely sensed substrate and habitat data resulting from
the CSCMP and other mapping projects. Although initially designed to inform MPA monitoring,
these projects also have widely valuable applications for tracking ecosystem changes and
understanding the complex and dynamic habitats of the North Central Coast region over time.
Moving forward, data collected through the 11 Baseline Program projects, CDFW
imagery, and seafloor data collected through the CSCMP will be utilized in long-term monitoring
and informing adaptive management of the region’s MPAs. The integration of biological,
socioeconomic, and seafloor mapping datasets will improve understanding of complex habitats
along the coast, the overall status of ocean health, and MPA efficacy over time. In addition to
informing adaptive management of MPAs, integrating monitoring results with CSCMP mapping
data can help to predict population responses to other changes in ocean conditions through
time, and inform issues such as fishery management and climate change tracking. Here we
present descriptions and maps resulting from the integration of CSCMP and Baseline Program
project data, highlighting examples of where integration of various data types can provide
useful knowledge for management. We discuss the current and potential applications of these
integrations, and where emerging methods can offer greater insight into the dynamics of the
region.
Mixed rocky
substrate
covered in
Watersipora sp.
in the North
Central Coast
*To learn more about the Baseline Program and find the technical reports from
the projects in the region, visit the North Central Coast page on Ocean Spaces.
Integrating Spatial Data 2

CALIFORNIA SEAFLOOR AND COASTAL MAPPING IN THE NORTH CENTRAL COAST REGION
What is the CSCMP?
The California Seafloor and
Coastal Mapping Program was initiated
in 2007 as the State’s first comprehensive
seafloor mapping project. At the time,
only approximately 30% of the State’s
seafloor had been assessed sufficiently
to support the MLPA and other resource
management goals (California Coastal
and Marine Mapping Initiative 2007).
The CSCMP was therefore tasked with
mapping the entire seafloor within state
territory (mean high water line out to
three nautical miles). As a collaborative
effort between state and federal
agencies, the academic community, and
the private sector, the CSCMP resulted
in publicly available digital map layers
of the seafloor substrate along the
California coast (https://walrus.wr.usgs.
gov/mapping/csmp/index.html and
http://seafloor.otterlabs.org/csmp/
csmp_datacatalog.html). In support of
the MLPA goals, an initial objective of the
CSCMP was to create a map set to inform
MPA design, and ensure inclusion of
representative proportions of California’s
rich variety of habitats. Using the
latest remote sensing and Geographic
Information System (GIS) technologies,
the CSCMP collected data to build a
comprehensive, high-resolution set of
seafloor maps. In addition to substrate
data (rocky vs. soft sediment) that were processed and used to inform MPA designation in the
North Central Coast, habitat type, bathymetry, offshore geology, seafloor characteristics, and
sediment thickness information will also be valuable in the continued monitoring of the region,
as well as tracking overall changes in ecosystems through time. These layers have already been
used for a wide variety of applications in addition to MPA delineation, including modeling
benthic ecosystems, updating NOAA nautical charts, providing a benchmark for measuring
and modeling coastal changes, identifying active offshore faults, improving outreach for public
education and awareness, and more. Current and future projects incorporating the CSCMP data
will be essential to the continued monitoring and understanding of the California coast.
Substrate Type
Soft Sediment
Rocky
±0 1 20.5
Kilometers
Figure 1. Example of seafloor habitat in Bodega Bay, CA. Substrate
data, as represented by reddish-brown (rock) and tan (sand),
collected by the CSCMP. Photos obtained from the USGS CSCMP
video and photo portal.

4
Why is the CSCMP important for monitoring?
The health and sustainability of coastal ecosystems involves many environmental
parameters, including seafloor composition and characteristics. Accurate basemaps are
essential to improving the understanding of the state of marine ecosystems along the California
coast. In the North Central Coast region, the CSCMP data have been foundational to monitoring
efforts. Before the CSCMP was initiated, the general knowledge of the composition of the
seafloor was rather limited, but this program has presented a more accurate picture of the
current state of the North Central Coast region and revealed the complexity found in the mosaic
of seafloor characteristics along the coast.
Seafloor characteristics (e.g., depth, slope, sediment type) and associated habitat types,
defined by the different combinations of seafloor characteristics (e.g., steep rocky shelves,
mud flats) play a large role in driving ecosystem differences. Because many plants, fish, and
invertebrates rely on particular habitat types, species compositions tend to differ between
them, especially between areas of flat, soft bottom and areas of hard, rough substrate. Knowing
where these different habitats are located is helpful in understanding the physical and biological
processes in the system and
identifying preferred substrate
availability for different species
in the region. Thus, monitoring
over distinct habitat types is key
to understanding some of the
greater dynamics of the system. For
example, in soft sediment, there
are generally greater abundances
of flatfishes and sea pens, whereas
over rocky reefs and outcrops there
tend to be higher abundances
of rockfishes, sea cucumbers,
and sea anemones (Anderson et
al. 2009; Lindholm et al. 2014).
Furthermore, it is valuable to
specifically locate the boundaries
between different broader habitat
types because species assemblages
in these transition zones are often
different than in the surrounding
areas. By collecting data on
seafloor characteristics, like those
collected through the CSCMP,
seafloor mapping is a tool to
estimate habitat types and species
compositions where extensive
biological surveying in all habitats is
impractical.
Figure 2. Distribution of rocky and soft
substrate throughout state waters in the
North Central Coast region. MPAs are
shown in blue (SMCA) and red (SMR).

How has the CSCMP been applied?
Bathymetry data collected through the CSCMP show the shape of the seafloor, and
highlight areas of rough (rocky) or smooth (soft sediment) terrain in shaded relief images. In
addition to visualization of the substrate through these shaded relief images, using simple
algorithmic measures, rugosity (roughness of the seafloor) is calculated and used for the general
classification of substrate types over a large area. Habitat classification and initial analysis of
CSCMP data for MPA designation were based on rugosity as a proxy for these two substrate
types (rocky and soft) stratified into four separate depth zones (0-30 m, 30-100 m, 100-200 m,
and > 200 m). While much of the region is comprised of soft sediment (sand or mud), there are
several highly complex areas of outcrops and rocky reefs. Figure 2 shows the distribution of the
two substrate types throughout the entire North Central Coast region. CSCMP bathymetry data
also allow the measurement of depth to delineate the zones listed above. Figure 3 shows the
two substrate types, including the delineation of the depth zones used in MPA design. Because
these classifications are often associated with changes in species compositions, they were
chosen as breakpoints to assess distribution of distinct habitats and ensure adequate coverage
of the diversity across different habitat types in MPAs. Monitoring in these habitats region-wide
will be valuable, as the habitat and species compositions change from north to south along the
coast (Regional Profile of the North Central Coast Study Region 2010).
The CSCMP not only illuminated locations of different known substrate types more
specifically than before, but revealed that rippled scour depressions (RSDs), an ecologically
distinct habitat feature, cover a significant area of the North Central Coast region. RSDs were
noted in various places on the seafloor and have only recently been identified as ecologically
important. They were found to contain species compositions different than surrounding areas,
including high numbers of young rockfishes, indicating RSDs as potential nursery habitat
(Hallenbeck et al. 2012). After their discovery as a relatively abundant and ecologically distinct
feature along the California coast, other studies have been initiated to try to understand the
dynamics of benthic habitats and general distribution of RSDs throughout state waters (see
Davis et al. 2013). While RSDs were not considered in the planning and design process for
the North Central Coast MPAs, it will be beneficial to monitor them to inform management
going forward. This new spatial information, in conjunction with biological and socioeconomic
monitoring data collected throughout the region, will provide a more holistic view of the region,
as discussed in the following sections.
Rocky substrate
in the North
Central Coast

Figure3.HabitatclassificationsbasedontheCSCMPsubstratedata
anddepthzonesintheNorthandSouthpartsoftheNCCregion.

SPATIAL MONITORING DATA FROM THE BASELINE PROGRAM
The 11 Baseline Program projects within
the North Central Coast region covered a variety
of ecosystems, and surveyed areas both inside and
outside of MPAs. Collecting spatially explicit ecological
monitoring data, in conjunction with identifying the
locations and extents of different seafloor habitat
characteristics, is important for tracking changes
in ecosystems (including metrics such as species
abundance and distribution) and human use patterns
over time. It allows for researchers and managers to
pinpoint locations of high human activity and rich
species diversity, and to understand the patterns of
species distributions and human uses, so that resources
can be managed accordingly. Mapping the locations
of all of the Baseline Program projects also offers the
opportunity to identify ideal sampling sites in the
future, and where integrating various data types may be
most informative.
Of the 11 Baseline Program projects, eight
(including three citizen science projects) collected
primarily biological data in the intertidal and
subtidal environments. While human use data
were additionally collected in some these projects,
they are not displayed specifically in the map set
below. Another project collected socioeconomic
data throughout the region, and one collected
aerial imagery-based habitat information. One final
project discussing the baseline characterization
of environmental conditions conducted statistical
modeling of ocean metrics to develop an index of
ocean conditions. It was, therefore, less suitable
for spatial representation at this regional scale and
is not included in the following map set. Raw data
from these monitoring projects may be downloaded
from OceanSpaces (www.oceanspaces.org).
Diver with Reef Check CA
collecting monitoring data
during the Baseline period
Harvesting abalone in the
North Central Coast

Biological Data (Fig. 4)
Citizen Science. Identifying citizen science efforts and data collection highlights where the
community has the greatest opportunity to directly connect to the monitoring activities
in the region. Additionally, it brings in new knowledge and experience, leverages a cost-
efficient workforce, and identifies where projects could be augmented to include more
citizen science for greater capacity in monitoring.
Seabirds. Seabirds are an integral component of the marine ecosystem as upper level
predators. Due to their broad habitat use and tight links to fish populations as a food
source, monitoring nearshore seabird populations can be a cost-effective indicator of
overall health of offshore ecosystems.
Subtidal. Integrating seafloor maps with these various projects offers insights regarding why
certain ecosystem patterns are observed, why species are located where they are, why
changes occur in some places and not others, and how to make predictions over the
entire region based on the seafloor characteristics. It also allows for the identification
of specific habitat characteristics that are important to certain stages of a species’ life
cycle, where these characteristics occur throughout the region, and whether they may
be important to adaptively manage. These integrations will help to understand fish
populations and fisheries that are common in the subtidal zone.
Intertidal. Similar to the subtidal environment, integrating seafloor maps with intertidal
biological data offers insights regarding various ecosystem patterns and identifying the
distribution of key habitats. As the intertidal zone is important for many human uses and
can often host zones of high biodiversity, integrating spatial data to understand these
habitats is important for future adaptive management.
Figure 4. Distribution of biological Baseline Program efforts

Socioeconomics (Fig. 5)
Spatial components of socioeconomic data highlight commercial and recreational
fishing, and non-consumptive recreational use occurrences and density across different
habitats within the ecosystem. This allows for understanding of how MPAs are affecting
fisheries and recreational activities in the region. Presented below are just three of many
socioeconomic layers available for public access on CDFW’s web mapping and GIS data
distribution platform MarineBIOS (https://map.dfg.ca.gov/marine/). CPFV Rockfish refers
to a Commercial Passenger Fishing Vessel (CPFV), a type of vessel in which anglers pay
a fee to fish. Recreational activities represented in the map below include beach going,
hiking or biking, watching wildlife, and surfing, among others.
Habitat Mapping
Mapping efforts in addition to the CSCMP, including aerial imagery collected by Ocean
Imaging, augment seafloor habitat maps for a more comprehensive picture of the region
by providing another layer of habitat characteristics that informs biological survey
efforts. A subset of results from these efforts will be shown in more detail in Figure 6 in
the next section.
Figure 5. Distribution of socioeconomic activity from the Baseline Program.

INTEGRATION OF DATASETS
Each of the aforementioned Baseline Program projects contributed important
information about the region, but when viewed collectively and in combination with the
detailed CSCMP data, they provide a broader and more nuanced view of the dynamics along
the North Central Coast. The integration of diverse datasets such as biological, oceanographic,
socioeconomic, and physical seafloor data, can fill knowledge and data gaps in order to inform
adaptive management and more precisely guide site selection for monitoring and ecosystem
health assessments. Figure 6, for example, shows the integration of CSCMP data with the
aerial mapping data collected by the Baseline Program project conducted by Ocean Imaging.
Information about some of the areas that were not able to be mapped through the CSCMP
were partially filled by the additional Ocean Imaging data. As monitoring continues, results from
monitoring projects will also be able to highlight where additional mapping products would be
informative.
Currently, there are several projects working to integrate these various datasets together
to build on existing knowledge and create the resources to draw upon multiple projects.
Imagery data collected using remotely operated vehicles (ROVs) by both the Institute for
Applied Marine Ecology (IfAME) at California
State University Monterey Bay (CSUMB) and
CDFW are being analyzed in conjunction with
data collected through the CSCMP to learn
how these datasets interact and complement
each other and what new knowledge can
be gleaned from combining multiple data
types. A similar integration is overlaying kelp
coverage data collected separately by Ocean
Imaging and CDFW, with the CSCMP seafloor
data layers. When all of these datasets are
joined and compared, it allows for the analysis
of multi-scale, ecosystem-based questions,
efficiently filling gaps in data and knowledge,
and the illumination of additional data
gaps that may still be present. Overlaying
these integrations on the MPA boundaries will also be informative in long-term monitoring as
spatial and temporal changes are assessed. Many synthesized map layers can be accessed and
downloaded from CDFW’s MarineBIOS with more being added continually.
Integrating Spatial Data 10
ADDITIONAL DATA RESOURCES
Here we have focused on CSCMP and MPA Baseline
Program project data. However, there are many other
datasets that have the potential to be integrated to
inform the understanding and management of the
State’s marine ecosystem. Coastal datasets (coastal
change), additional biological datasets (ROV, other
kelp and seagrass mapping), human use datasets
(coastal access, water sports, ports/harbors, fisheries
datasets), and cadastral datasets will all be useful in
the continued monitoring and assessment of change
throughout time and space.

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010.5
Kilometers
BodegaBay
±02.551.25
Kilometers
SubstrateType
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Figure6.Exampleoftheintegrationofseafloormappingdata,habitatmappingdata
collectedbyOceanImaging,andbiologicalmonitoringlocationsinBodegaBay,CA.

APPLICATIONS TO MODELING, MONITORING, AND MANAGEMENT
As seafloor mapping and other spatial data become more readily available, the
applications to monitoring and management continue to grow. Already, the integration of
various data types has allowed better predictions of the abundance and distributions of local
populations and a greater understanding of
habitat connectivity, as we describe below.
These predictions will be able to guide
future monitoring site selection, assess MPA
design and performance, and help inform
adaptive management decisions by enhancing
understanding of regional ecosystem dynamics
and making predictions of populations’
responses to changing ocean conditions. In
addition, there are an increasing number of
derivative seafloor map products becoming
available which could provide inputs for more
accurate models and predictions of species
distributions, and ocean and coastal processes.
Areas that are lacking in spatial information are
also progressively being filled by data from new
mapping technology and prediction methods.
As the ways in which all of these products and
datasets can complement and augment each
other evolve, the applications to a diverse set of
ocean issues will likely increase and encourage continued collaborations and contributions from
partners in various disciplines. Accordingly, the projects mentioned throughout this section are
just a small subset of the projects integrating spatial data with other data types and should be
regarded as a brief example and not an extensive list.
Rosy rockfish
hovering over
the seafloor
BUILDING COMMUNITIES
Beyond the direct application of spatial data to MPA
monitoring, the integration of various data types can
lead to new collaborations among a wide variety of
interdisciplinary partners. Diverse partnerships can
add incredible value to the effort by establishing
new networks, and creating access to a wider array
of knowledge and skills to effectively solve issues.
These collaborations include state and federal
agencies, private industry, and academia in varying
disciplines such as ecology, oceanography, geology,
and technology, with contributions from policymakers,
researchers, students, and the greater community.
Overall, connecting communities through the
integration of data and complementary efforts has
incredible value in assessing, tracking, and managing
ocean health.
12

Landscape Ecology Models
From sea stars to rockfish, and many species in between, integrations of multiple
data types offer better stock assessments and more precise distribution estimates. Along
the North Central Coast, as well as other regions, landscape ecology models - linking habitat
and biological data to make predictions about species distributions and abundances - have
been implemented to assess and predict where certain key populations are found within the
region, and fill data gaps in stock assessments. Many marine species predictably associate
with certain seafloor characteristics, including depth, slope, roughness, and sediment type.
The fine-scale seafloor data collected through the CSCMP provide a foundation of where
different combinations of these characteristics occur. By combining these data with biological
survey data collected with ROVs, researchers are able to develop landscape ecology models
to predict species abundance, population distributions, species richness, and more. Models
can be incredibly useful to visualize where species occur through predictive maps, which are
developed by taking species observation data from a set of narrow transects, identifying the
combination of seafloor characteristics and environmental variables present at each observation
point, and extrapolating to unsurveyed locations (Fig. 7). By analyzing and anticipating various
species’ habitat associations and responses to environmental variables, this research provides
managers with more accurate information about key species populations and distributions,
characterization of essential habitat, and a foundation for adaptively managing for a changing
environment without having to extensively survey large areas of habitat.
Figure 7*. Process for
developing landscape ecology
models based on seafloor
mapping data and biological
data collected through ROV
transects.
*Adapted from California Seafloor Mapping Program: Central Coast Marine Protected
Areas. CSUMB Seafloor Mapping Lab, California, USA. 2013.
ROV image: MARE
Illustration: Brenda Guild Gillespie
Data for images: CSCMP

Several projects along California’s coast are using these types of methods to better
understand marine ecosystems. Young et al. (2010) used ROV and CSCMP data collected
in Cordell Bank, offshore of the North Central Coast region, to predict the distribution of
several rockfish species across the seafloor and assess the predictive modeling techniques.
Collaborations between CDFW and National Marine Fisheries Service (NMFS) are establishing
projects with similar methods to develop models that may be able to inform data poor stock
assessments. While bottom dwelling fish species, like rockfish, have been the subject of many
of these studies because of their strong association to the seafloor, similar methods have also
been employed to predict, and analyze the modeling techniques for, the distributions of several
invertebrate species (Krigsman et al. 2012). In the Central Coast region, Young et al. (2011) used
and tested seafloor mapping tools to analyze squid egg mop distribution, providing information
and techniques that are highly useful for managers. These types of predictions and tools are
still being tested for accuracy and scalability for many different species, but so far studies using
these methods of integration have shown promising and valuable uses for these tools.
Ecosystem Connectivity Analyses
Another way in which multiple datasets can be integrated to gain a deeper
understanding of the ecology, and apply those insights to management of the region, is through
examining the connectivity among ecosystems. Connectivity of nutrients and resources can
strongly influence the distributions of diversity and productivity, especially for ecosystems
such as sandy beaches, which have very little primary production. Beach wrack (detached
pieces of aquatic plants and algae, driftwood, or animal
remains that wash up on the beach) supports beach food
webs, including up to ~ 50% of invertebrate biodiversity,
abundant shorebirds, terrestrial fauna, and several
endangered species. Baseline Program researchers
worked together to draw upon the Ocean
Imaging ecosystem maps and the sandy beach
monitoring project to look at the sources and
patterns of wrack along the North Central
Coast of California, and to investigate the
potential drivers of these patterns. They found
that the beach wrack came from multiple
sources, including kelp forests, rocky intertidal,
and estuarine ecosystems. The local abundance
of their source ecosystems was the most important
predictor of the supply of wrack, while physical factors
(wind, waves, etc.) were only sometimes significant. These
results suggest that simultaneous monitoring of multiple
marine ecosystems is emerging as an important tool for
understanding ecosystem connectivity, and essential for
informing adaptive, ecosystem-based management. This work is being written up and submitted
for publication (Liebowitz et. al., in prep), and results will be available soon on OceanSpaces.

Derivative Seafloor Maps
Many new spatial products are continually being released, including new map layers
derived from CSCMP data. Some of the derivative products available from the CSCMP are
various GIS layers displaying the distribution and detail of specific habitat types (Fig. 8A),
seafloor characteristics (Fig. 8B), and geology (Fig. 9). These layers (shown below in Bodega
Bay as an example), produced by extracting specific information from the original CSCMP data,
add valuable detail and while not used in the initial design process, will be useful moving into
long-term monitoring and adaptive management of MPAs, and assessment of ocean health.
For example, while the region overall consists of about 88% soft sediment and 11% rock
(the classification used in initial MPA design), the makeup of the rocky and soft substrate are
different throughout different parts of the region, making each location unique (Fig. 9). Around
Bodega Bay much of the rocky habitat is extensive Cretaceous granitic rock (dark teal), whereas
in Half Moon Bay the rock is comprised mostly of sedimentary rock in the Miocene and Pliocene
Purisima Formation (light green). Where granitic rock tends to have a fractured surface with
deep faults, the Purisima Formation tends to be “ribbed,” caused by differential erosion (Greene
et al. 2014; Johnson et al. 2014). While both are rocky habitats, the incorporations of enhanced
detail from new geologic layers makes it easier to assess where these different formations occur
and aids in the understanding of the variation seen among different locations in the region.
A B
Figure 8. Habitat and seafloor characteristic layers in Bodega Bay.

16
Additionally, fine-scaled habitat classifications and seafloor characteristics such as
sediment grain type and size can contribute to better predictions of species distributions and
abundances, as well as help understand past and future coastal dynamics through the analysis
of the distribution of sediment types. Derivative maps could also aid in the understanding the
potential impacts of environmental change. Detailed data on geologic structure and sediment
composition and thickness are important components of seafloor assessment to gain a better
understanding of coastline resilience and the need for protection under changing conditions,
such as sea level rise. Used together, these new derivative layers offer a more holistic view of
the seafloor and a better understanding of the dynamics occurring over it.
Due to the high degree of detail available in the new map layers and processed data
from the CSCMP, the categories included in Figures 8 and 9 can be broadened or broken down
into more detailed classifications, depending on the application of the data, for multi-scale
information. While shown here in Bodega Bay and Half Moon Bay, the various CSCMP products
for all of the North Central Coast region are, or soon will be, available for access and download
(https://walrus.wr.usgs.gov/mapping/csmp/).
Figure 9. Geology layers in Bodega Bay (left) and Half Moon Bay (right).

While there is a great amount of detailed
information about the region, there remains
a critical gap—the shallow, very nearshore
zone, where navigation hazards and technical
limitations prevented ship-board mapping, and
turbid water or obstructions prevent successful
remote sensing or aerial techniques. This 50-
500m wide band of unmapped seafloor, a data
gap known as the “white zone,” extends from
shore to 10-15m depth along the length of
the California coast, encompassing much of
the state’s kelp forests and essential habitat
for commercially and recreationally important
species (Fig. 10A). Improved mapping of the
white zone has been repeatedly identified as
an area of critical data need, yet the costs and
labor associated with empirically mapping this
zone statewide are prohibitive. Partnership
for Interdisciplinary Studies of Coastal Oceans
(PISCO), CDFW, and California Ocean Science Trust
leveraged the wealth of seafloor and shoreline
mapping data from the CSCMP and the National
Oceanic and Atmospheric Administration (NOAA)
Environmental Sensitivity Index (ESI) shoreline
habitat categorizations, to develop predictive
maps of substrate characteristics in the white
zone through interpolation (a mathematical
technique to predict missing values in data,
Fig. 10B). In order to determine an optimal
method of interpolation and to test which
combination generated the most accurate and
precise prediction of rock versus sand substrate
across the variety of white zone widths that
occur along the North Central Coast, they tested
ten ArcGIS interpolation techniques at various
scales and habitat conditions. From these tests,
a set of optimal maps were developed (see
Saarman et al. 2015) and are now available to
stakeholders through CDFW’s MarineBIOS (https://
www.wildlife.ca.gov/Conservation/Marine/GIS/MarineBIOS). These maps can be used for a
range of management applications, such as population modeling for key nearshore species,
setting expected rates of population change within MPAs to better evaluate MPA conservation
performance, and setting guidelines for scientific collection permits.
±
Mapped Substrate
Rocky
Soft Sediment
White Zone Predicated Substrate
Probably Rock
Probably Soft Sediment
0 12 0.5
Kilometers
A
Figure 10. Very nearshore region, the “white
zone” (A), and predictive maps of the substrate
in this region (B).

Developing and Future Applications
In addition to the newly available map layers and the projects that are already underway
utilizing integrations of various types of spatial data, there are many other potential applications
that will likely receive more attention in California moving forward. Spatial data collected
through improved seafloor mapping technology, like those used in the CSCMP, will be a critical
component of tracking changes in California waters because it will allow the tracking of changes
in the seafloor and surrounding environment due to landslides, fault activity, strong currents,
or human activity and their various impacts on the ecosystem. Some of this type of work
has been started by Barnard et al. (2012) at the mouth of the San Francisco Bay, estimating
coastal erosion, and by Johnson et al. (2014) assessing offshore faults, both valuable in making
predictions about future change and as a tool for adaptive planning. Improved mapping
technology has also been used by Barnard et al. (2011) and Draut et al. (2009) to analyze and
understand dynamics of sediment transport and temporal changes in seabed morphology
along the coast, with clear future applications to tracking and predicting changes in seafloor
formations. Furthermore, high-resolution spatial data will be useful in tracking change in
California by documenting the distribution of habitat types that are vulnerable or more resistant
to various human impacts, as discussed in Grabowski and Hart (2012), analyzing habitat
availability and recovery potential (Hughes et al. 2013), sample site selection for research in
subtidal environments, and identifying locations that require gear removal attention. Lastly, as
California explores the use of alternative energy, spatial data will also be valuable in assessing
potential sites for offshore energy by identifying areas of essential habitat or those that are
more sensitive to disturbance versus those that may be better suited for supporting offshore
energy infrastructure. All of these projects, and many more, will add an additional layer of
context and insight to inform adaptive management and understand long-term change along
the California coast and beyond to better prepare for the future.

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