OSRI Final Technical Report
Forage fish and near-shore habitat associations
PI Rob J. Bochenek
Axiom Consulting & Design, LLC
Anchorage, AK 99504
Co-PI Ryan Nielson
Western EcoSystems Technology
Laramie, WY 82070
OSRI Contract Number: 08-10-18
Contract Period July 2008 – December 2009
Abstract:
Pacific herring (Clupea pallasi) has been identified as a critical species for North Pacific marine
ecosystems and historically has been a commercially important species. In order to improve
understanding of this key species, datasets which describe herring spawn activities were
integrated with known habitat information and analyzed to determine which habitat has been
critical for herring survival and recruitment. Investigators coupled herring spawn observation
data to Environmental Senstivity Index (ESI), Bioband, and localized ocean depth information
available for Kamishak Bay, Alaska to look for relationships between habitat characteristics and
observed herring spawn events. A standard use-availability resource selection analysis (Manly et
al. 2002) was used to identify habitats typically selected by herring for spawning. The final
resource selection function (RSF) indicated herring have selected shoreline segments for
spawning that have shallow ocean depths, and that herring prefer to spawn along shoreline that
have certain ESI and Bioband (Shorezone) habitat classifications. The final RSF was used to
predict the relative probability of habitat being selected by herring for spawning based upon the
shoreline ocean depth and habitat characteristics. The resulting RSF allowed investigators to
produce a series of spatial datasets and maps that can be used by resource managers and
scientists to better understand what factors describe ideal herring spawning habitat and plan for
future management/policy decisions in Kamishak Bay. Source data and predictive maps
produced from this project have been integrated into the emerging Prince William Sound Science
Center - Headwaters to Ocean (H2O) framework. H2O allows visual and granular access to
source
data
and
analytical
products
through
the
project
web
site
(http://dev.axiomalaska.com/herringhabitat/).
Progress Summary:
The following details the progress and impediments encountered by the study team in regard to
the project objectives.
Objective 1. Process, standardize and integrate herring and Shorezone habitat datasets for
Prince William Sound and Kamishak Bay into a coherent geospatial framework.
This objective was completely met by the study team. This involved processing Shorezone
datasets and metadata for both Prince William Sound (PWS) and Kamishak Bay. Most herring
data for PWS had already been processed (work performed in 2007 for Exxon Valdez Oil Spill
Trustee Council [EVOSTC] project 070822 and in 2008 for EVOSTC project 08082). Only
Kamishak herring data needed to be processed and integrated with other local habitat data. In
addition, localized ocean depth characteristics were worked into the analysis by including the
NOAA 50-1000 meter resolution multi-beam vector ocean depth dataset available for the Cook
Inlet region.
The Shorezone dataset is a spatially explicit classification of nearshore coastal habitat
characteristics. The dataset is composed of a series of ESRI Shapefiles, a Microsoft Access
database, and digital photo and video files. The resource is highly descriptive and details specific
classifications of biological zones in addition to geological characteristics along the shoreline.
The Access database is in a normalized form and contains explicit FGDC metadata. The dataset
delineates biological sections of the Shorezone into the following theme areas: Splash Zone,
Marsh Grass + Herbs + Sedges, Dune Grasses, Sedges, Barnacles, Rockweed, Green Algae,
Bleached, Red Algae, Blue Mussels, Red Algae, Kelp, Soft Brown Kelps, Dark Brown Kelps,
Eelgrass, Dragon Kelp, Giant Kelp and Bull Kelp. Additionally, the dataset delineates the
following geological characteristics of the shore zone: Dominant Morphology, Sediment Type,
Wave Exposure, Oil Residence Index and Shore Modifications. More information regarding
these classifications and the methodology used is available in the Alaska Shorezone Protocol
(http://www.coastalaska.net/pdf_files/protocol.pdf).
The Shorezone data was much more complex than originally assessed and required extensive
data manipulation in addition to some involved spatial analysis to prepare the data for statistical
analysis. The Shorezone dataset unit segments were highly variable in terms of their length and
topological characteristics, which presented problems in the development of the resource
selection function (RSF). Single Shorezone units could be composed of one continuous shoreline
segment or an amalgamation of multiple discontinuous shorelines and islands. Additionally, the
dataset was further complicated by the lack of uniform segment length. In order to proceed with
the statistical analysis portion of the project, the study team developed a data normalization
process to standardize the lengths of the shoreline segments while keeping the Shorezone habitat
characteristics intact. This process entailed breaking each complex geometric instance (single
Shorezone units composed of multiple ‘linestrings’) into individual simple geometries. Once the
Shorezone spatial data was topologically consistent the resulting segments were further
processed to create a relatively consistent sampling length with a target of 50 meters. This
second step in the normalization process involved fractioning Shorezone units into subunits
while propagating the original Shorezone characteristics of the original unit. The resulting
normalized spatial dataset, though much larger in terms of number of features than the original,
provided a spatially consistent dataset for the subsequent spatial/statistical analysis.
All datasets were integrated into a spatially enabled database to produce secondary analytical
datasets to be used in the statistical analysis. These secondary datasets delineated the occurrence
of spawn events within a 200 meter buffer, extending perpendicular from the shoreline and
surrounding the normalized segment, and the average ocean depth within a 200 meter buffer of
the normalized segment. The spatial processing and analysis are further detailed in Appendix I.
Objective 2. Evaluate Shorezone habitat factors contributing to herring recruitment success
through the analysis of spatially and temporally explicit herring spawn, age, sex, length,
weight, and biomass datasets coupled with Shorezone habitat features.
This objective was partially met by the study. This objective was impeded by issues of data
inconsistency between Kamishak Bay and PWS and limitations of the underlying data. The study
team refined their analysis to focus on habitat selection by spawning herring as predicted by
Shorezone type and ocean depth data in Kamishak Bay. The Kamishak Bay data was selected as
the best candidate for the statistical analysis due to the consistent and well documented sampling
efforts of the aerial surveys. The PWS data was determined to be inadequate for robust statistical
analysis due the inconsistent and often undocumented sampling efforts during the aerial surveys.
Figure 1 provides a GIS visualization of historical aerial survey effort in PWS and Kamishak
Bay. Investigators postulated that a resource selection model developed using Kamishak Bay
bathymetric profiles and Shorezone characteristics could also be applied to the PWS area. This
proved problematic due to the discrepancies between regions in terms of their different
Shorezone habitat compositions and localized ocean depth characteristics.
Figure 1. Comparison of aerial survey effort (yellow lines) between Kamishak Bay (left photo)
and PWS (right photo). The Kamishak Bay survey effort is uniform and consistent through time
making it an ideal candidate for the subsequent statistical analysis. The PWS survey effort is
highly irregular, inconsistent through time, and poorly documented.
Due to poor source data and the unforeseen complexity of the Shorezone dataset, the study team
was forced to focus on habitat characteristics (Shorezone and ocean depth) and herring spawn
selectivity of these habitats for the development of the RSF. The analysis undertaken during this
project did not take into account subsequent herring recruitment or Age Sex Length (ASL)
datasets. After consulting with experts in the herring biological community and reviewing the
literature it was concluded that herring recruitment is primarily a function of physical ocean
conditions related to survival, forage abundance, physical ocean conditions, and spawning
biomass (Stokesbury et al. 2002, Norcross et al. 2001, Williams and Quinn 2000, Zheng 1995).
Objective 3. Identify Shorezone habitat types and locations which are critical for herring
reproductive and recruitment success.
This objective was fully met by the study team. It was determined that habitat selection was
inversely proportional to localized ocean depth when all other habitat characteristics (Shorezone)
were held constant. Additionally, it was determined that the ESI classification of sheltered rocky
shores/scarps and wave cut platforms were consistently selected over other Shorezone types in
Kamishak Bay. Furthermore, the bleached red algae Bioband was highly selected for spawn by
herring in Kamishak Bay. The resulting RSF, which was developed to model and predict habitat
selection, was applied to the Kamishak region to develop a spatially explicit spawn prediction
GIS data layer for Kamishak Bay. The resulting map is shown in Figure 2.
Figure 2. Spawn prediction map generated from output of resource selectivity function. Red
segments are predicted to be spawned upon less frequently than blue and violet segments.
Predictions represent relative probability of selection and were scaled so the maximum
prediction was equal to 1000.
The normalized Shorezone dataset for Kamishak Bay was then inspected to determine which
Bioband and ESI characteristics were viable for the statistical analysis based upon their
frequency of occurrence. Resource selection functions are subject to estimation problems when
considering rare or outlier characteristics. Bioband data (which is classified as patchy,
continuous or absent on the supratidal, subtidal and intratidal plane) was further simplified by
ignoring supratidal characteristics and lumping intertidal and subtidal into Bioband presence or
absence along each Shorezone segment. For example, if soft brown kelp were recorded as patchy
or continuous in the subtidal or intertidal zones we classified soft brown kelp as present along
that shoreline segment. Some ESI classifications were combined based on their rarity and sharing
of similar characteristics, and others that were absent or rare in Kamishak Bay were dropped
from the analysis.
An RSF was then developed from the underlying data to model selectivity across known habitat
variables in Kamishak Bay. The specifics of the model development are detailed in Appendix 2:
Statistical Analysis.
The final RSF indicated positive relationships between observed herring spawning habitat
selection and shorelines characterized as having wave-cut platforms, sand and gravel beaches,
sheltered rocky shores, bleached red algae, alaria, green algae, and barnacles (Table 1).
According to the final RSF, there were negative relationships between spawning habitat selection
and marshes, rocky shores, rockweed, and water depth (Table 1).
Table 1. Estimates of model coefficients, standard errors (SE), selection ratios, and 95%
confidence intervals for selection ratios based on the final RSF. A selection ratio represents the
proportional increase in relative probability of selection when a Shorezone type is present, or
when ocean depth is increased by 1 meter. A selection ratio for the intercept term (ESI = tidal
flats was reference level) was not calculated because of difficulty in interpretation.
Selection 95% CI
Variable
Estimate SE
Ratio
LL
UL
Intercept
-5.581
0.262 NA
NA
NA
ESI: Rocky shores
-0.176
0.114 0.838
0.670 1.049
ESI: Wave-cut platforms
1.014
0.044 2.756
2.529 3.003
ESI: Sand/gravel beaches
0.262
0.057 1.299
1.162 1.453
ESI: Sheltered rocky shores
1.156
0.150 3.177
2.367 4.264
ESI: Salt- and brackish-water
marshes
-1.424
0.294 0.241
0.135 0.428
Bioband: Bleached red algae
1.558
0.038 4.747
4.405 5.116
Bioband: Rockweed
-1.563
0.067 0.210
0.184 0.239
Bioband: Alaria
0.655
0.053 1.925
1.737 2.134
Bioband: Green algae
0.218
0.048 1.243
1.132 1.365
Bioband: Barnacle
0.187
0.033 1.205
1.129 1.287
Average water depth (m) within
-0.003
0.001 0.997
0.995 0.998
200 m from shore
Figure 3 shows the estimated relative probability of selection based as a function of water depth
(m), with all other variables held constant. Figures 4 and 5 compare the estimated relative
probability of selection as a function of various ESI and Bioband classifications, holding all
other variables constant. The relative probability of selection is inversely propositional to
localized ocean depth.
Figure 3. Estimated relative probability of spawning herring selecting shoreline segments of
various depths, given all other shoreline characteristics are held constant. The relative
probabilities of selection were scaled so the maximum was equal 1.0.
Figure 4. Selection ratios and 95% confidence intervals (CI) for Environmental Sensitivity
Index (ESI) classifications in the RSF for spawning herring. A selection ratio represents the
proportional increase in the relative probability of selection by spawning herring given a
Shorezone type was present. Since the ESI classification of tidal flats was the reference level
(model intercept term), selection ratios for other ESI values represent the proportional increase or
decrease in relative probability of selection compared an ESI classification of tidal flats. A 95%
CI that encompasses 1.0 indicates selection was not considered statistically significant
(equivalent to a significance level of α = 0.05).
Figure 5. Selection ratios and 90% confidence intervals (CI) for Bioband classifications in the
final resource selection model for spawning herring. A selection ratio represents the proportional
increase in the relative probability of selection by spawning herring given a Shorezone type was
present. A 95% CI that encompasses 1.0 indicates selection was not considered statistically
significant (equivalent to a significance level of α = 0.05).
Accomplishments:
Complicated Shorezone data was acquired, synthesized, and integrated with other ecological data
(herring spawn observations and ocean depth) to develop a resource selection function to predict
relative spawn probability in Kamishak Bay. Additionally, the study team has developed a
standardized methodology for normalizing and prepping Shorezone datasets so that they may be
utilized in the development of other RSFs in addition to other spatial analysis efforts in other
regions and ecological contexts. All datasets utilized and produced from this effort have been
made available for public access in both visual and raw formats via the Prince William Sound
Headwaters
to
Ocean
Program
in
addition
to
the
project
website
(http://dev.axiomalaska.com/herringhabitat/). Figure 6 provides a screenshot of the OSRI
Herring Habitat web application which provides access to underlying datasets utilized and
created during this project.
Figure 6. Project website interface to datasets produced and utilized during this project.
This investigation has identified relationships between ESI and Bioband classifications and
habitat selection by spawning herring in Kamishak Bay. These relationships can be used as a
basis for future analyses in other regions. Provided assumptions about data quality are met, the
methods developed for this analysis can also be applied to data from other regions.
Conclusions:
This project explored methods for the development of resource selectivity models which predict
what habitat will be selected for spawn by herring based upon Shorezone characteristics and
localized ocean depth profiles in Kamishak Bay. The analytical methods produced from this
projects efforts can be applied to other regions where Herring are known or believed to spawn.
Furthermore, the resource selectivity function could be made more robust with the incorporation
of additional habitat and physical ocean condition parameters.
Data Citations
Alaska Department of Fish and Game, Division of Commercial Fisheries. 2002. Kamishak Bay
Data Synthesis, Version 1.0, 13 December 2002. CD-ROM. Homer, Alaska.
Coastal & Ocean Resources Inc. 2009. Cook Inlet and PWS ShoreZone Data, May 2009.
DVD. Sidney, BC.
NOAA-NMFS. Steve Lewis. 2008. Alaska Multibeam Vector Bathymetry (50-1000M),
September 2008. DVD. Juneau, Alaska.