The structure and composition of habitat play key roles in determining the spatial patterns of biota within marine landscapes. Understanding species habitat associations provides the information necessary to predict the diversity and abundance of species thus enabling greater control over species management and sustainability. Landscape ecology is commonly used in the terrestrial environment to understand the relationship between spatial patterns and ecological processes. While some landscape ecology metrics lend themselves to marine spatial studies more recent studies offer new ways of understanding the spatial relationships between species and the marine environment utilizing remote sensing and marine focused spatial pattern measures. Advances in remote sensing and spatial pattern recognition make it possible to assess habitat value within rocky reefs and create predictive models of fish association. Understanding habitat associations and having the ability to predict fish aggregations is a valuable tool for resource managers and marine spatial planning during development or redesign of marine protected areas. The oceans around the world are suffering a variety of abuses which may be lending to the decline in abundance of many economically valuable fish species. Improved resource management is necessary to ensure sustainability of the world’s fisheries. Multiple predictors are often used in development of predictive models for groundfish including complexity (VRM), relative topographic position (TPI), depth, distance to maximum VRM, and slope. Habitat complexity offers shelter from predation, a place for larval settlement, and is believed to be a predictor of species diversity. This study investigated use of habitat characteristics as predictors of species presence or absence at rocky reefs off the coast of Monterey, California over the Del Monte Shalebeds. Using bathymetric and fish aggregation data collected by the Seafloor Mapping Lab (SFML) at California State University, Monterey Bay (CSUMB) a probability model for groundfish aggregations in the shalebeds.

1. Habitat Models:
Predicting Rockfish Presence in the Del Monte Shalebeds
Image credit: L. Jensen. 2012. Seafloor Habitat Classification Using Topographic Terrain Analysis on the Big Sur Coast, CA
Lisa Jensen
Coastal and Watershed
Science and Policy
CSU, Monterey Bay
Seaside, CA

2. Introduction
Structure and composition of habitat play key roles in determining the spatial patterns of biota within
marine landscapes (Greene et al. 1999, McArthur et al. 2010). Understanding species habitat
associations provides the information necessary to predict the diversity and abundance of species
thus enabling greater control over species management and sustainability (Fahrig and Merriam 1985,
Grober-Dunsmore et al. 2008, Pittman et al. 2010). Landscape ecology is commonly used in the
terrestrial environment to understand the relationship between spatial patterns and ecological
processes (Risser et al. 1984, Turner et al. 2001). While some landscape ecology metrics lend
themselves to marine spatial studies more recent studies offer new ways of understanding the spatial
relationships between species and the marine environment utilizing remote sensing and marine
focused spatial pattern measures (Pittman et al. 2009, Pittman et al. 2010, Wedding et al. 2011).
Advances in remote sensing and spatial pattern recognition make it possible to assess habitat value
within rocky reefs and create predictive models of fish association (Young et al. 2010).
Understanding habitat associations and having the ability to predict fish aggregations is a valuable tool
for resource managers and marine spatial planning during development or redesign of marine
protected areas. The oceans around the world are suffering a variety of abuses which may be lending
to the decline in abundance of many economically valuable fish species (Corvalan et al. 2005).
Improved resource management is necessary to ensure sustainability of the world’s fisheries (Clark
1996).
Multiple predictors are often used in development of predictive models for groundfish including
complexity (VRM), relative topographic position (TPI), depth, distance to maximum VRM, and slope
(Iampietro et al. 2008, Pittman et al. 2010). Habitat complexity offers shelter from predation, a place
for larval settlement, and is believed to be a predictor of species diversity (Risk 1972, Friedlander and
Parrish 1998, Matias et al. 2010).
This study investigated use of habitat characteristics as predictors of species presence or absence at
rocky reefs off the coast of Monterey, California over the Del Monte Shalebeds. Using bathymetric and
fish aggregation data collected by the Seafloor Mapping Lab (SFML) at California State University,
Monterey Bay (CSUMB) a probability model for groundfish aggregations in the shalebeds.
Research Methods
Study Site
The study site for this proposal was the Del Monte shalebeds in Monterey Bay, California (121o
51’32W, 36o 36’58N (NW), 121o 54’41W, 35o 38’40N (SE)) (Fig. 1). The Del Monte shalebeds are
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Habitat Models: Predicting Groundfish Presence in the Del Monte Shalebeds
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3. located approximately 1 km offshore of Cannery Row in Monterey, California and cover an area of
roughly 11 km2. The shalebeds are relatively low relief mudstone and sandy siltstone outcrops
characterized by linear shelves ranging from 10 to 70 m in depth (Eittreim et al. 2002, Iampietro et al.
2005). The study site included the shalebeds and granitic outcrops which are relatively high relief
though smaller in patch size in comparison with the shalebeds. There are approximately 4.4 km2 of
rocky substrate within the study site, these patches were the focus of this study.
Data Acquisition
Bathymetric Data
Existing digital elevation model (DEM) data were used for habitat classification. Data for this research
was collected by the Seafloor Mapping Lab at California State University Monterey Bay and included
Figure 1. Study site location, the Del Monte shalebeds, located offshore of Monterey, California. The
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4. multi-beam bathymetric sonar collected aboard the RV Harold Heath using a RESON SeaBat 7125
multi-beam bathymetry sonar system. Data were corrected for the effects of attitude, tide, sound,
velocity and erroneous data soundings using CARIS HIPS software following standard hydrographic
data cleaning procedures (Kvitek et al. 2003, Young et al. 2010).
Fish Aggregation Data
Three tools are used when collecting fish aggregation data. A Seabotix LBV remotely operated
underwater vehicle (ROV) mounted with a video camera is used to collect imagery of aggregations.
Video data is collected with geospatial location data on a second pass over the same area when
sonar is not active. These data allow the investigator to identify species, count individuals, estimate
fish length and groundtruth results of fish aggregation collection tools discussed below.
A Furuno Searchlight CH250 sector scanning sonar is used to identify fish aggregations in the water
column and on the ocean floor. Furuno sector scanning ability is differentiated from multi-beam in that
the Furuno sweeps across the swath incrementally while the multi-beam pings simultaneously across
the entire swath. The Furuno sector scanning technology is similar to that of a radar sweeping around
a point seeing activity at a point in time for a given location. The Furuno identifies differences in
density. The swim bladder in rockfish provides a different density from water and is reflected in the
Furuno screen (Fig. 2). The Furuno provides a coarse identification of fish location and aggregation
size.
Figure 2. Furunu Searchlight sector scanning screen as seen by researchers on the boat. The seafloor is
identified in this image in shades of red and brown, fish aggregations were identified as “clouds” just above
the bottom.
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5. The combined resources of video, multi-beam and sector scanning sonar were used to map the
position of fish aggregations near the bottom and in the water column. Video and a drop camera
mounted on a Seabotix LBV ROV were used for direct observation of fish and groundtruthing the
Furuno Searchlight imagery. The ROV was launched randomly over the course of the study period for
the purpose of groundtruthing.
A total of 320 potential presence points were identified across the five days of data collection. These
points were refined to eliminate small aggregations as well as those in the water column. Additionally,
in those cases where a transect line was run more than once over the same location data were
cleaned to utilize a single set of data points per transect number. This data cleaning required use of
the transect with the most data points be kept and where two, or more, transects had similar
numbers of data the most recent set was retained. This final rule was used under the assumption that
as the research team continued its work they became more proficient at data identification and
collection.
This left a total of 199 medium and large aggregations located in the lower third of the Furuno display,
referred to as “bottom” aggregations or targets. The original geospatial position of each target was
centered below the boat, reflecting the GPS position of the boat at the time the target position was
collected. These data were corrected to reflect the position of the target based on the heading of the
boat and the location of the target (port or starboard and distance) with respect to the boat.
Fish Absence Data
Following Young et al. (2010) potential absence data were extracted from spatially explicit locations
along the transect lines where species were not observed and were required to be not less than 50
meters from an identified presence point. This approach reduces the risk of including false absences
in the model biasing the results (Hirzel et al. 2002). A total of 320 absence points were randomly
selected representing 1.6 times the total number of presence points. Actual presence and derived
absence points were merged together for use in a generalized linear model.
Habitat Analysis
GIS landscape analysis was performed using ArcGIS 10 (ESRI 2011) to classify habitat characteristics
as predictors of the distribution of rockfish within the study area. These included rasters for depth,
slope, aspect, vector ruggedness, or rugosity, (VRM) and topographic position index (TPI) (Fig. 3, Fig.
4). Each of the derivative rasters were generated using the bathymetry DEM. The DEM was corrected
to remove missing data using ArcGIS Spatial Analyst FocalStatistics to calculate the mean value within
a neighborhood (ESRI 2011). The neighborhood size was determined through close examination of
the location of missing data, the adjacent substrate and the nearest differing substrate. To the best
extent possible, this analysis ensured flat areas remained flat and rugged areas remained rugged.
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6. Topographic position index (TPI, Fig. 3) is a scale dependent comparison of elevation for a specific cell
in relation to cells within a defined neighborhood around the specified cell. For the purposes of this
study raw TPI will be used as a predictor raster such that values greater than zero are higher than
neighbors (peaks, crests, pinnacles), near zero are similar to neighbors (flats, plains, or slopes)and
values less than zero are lower than neighbors (valley, crevice, depression) .
Figure 4. The corrected digital elevation model (DEM) will be used to derive predictor variables TPI,
Aspect, Slope and VRM.
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7. TPI analysis was performed using an annulus (donut) shaped neighborhood. In this case the cell under
consideration is within the inner circle and the neighboring cells are in the outer circle. An annulus with
scale factor 25 was used in the development of the model (Weiss 2001).
VRM and TPI will be transformed into binary rasters distinguishing between rock and non-rock (VRM)
along with peak and non-peak (TPI). These will be used to calculate distance from their high values.
Rugosity and TPI are believed to be good predictors of fish aggregations and, while a specific
aggregation may not be directly on top of a rock or peak there may be a relationship between
proximity and fish aggregations (Wedding and Friedlander 2008). These distance to rock/peak rasters
were included in the development of a generalized linear model (GLM) predictive model (Fig. 5).
Figure 4. Topographic position index, a scale-dependent comparison of elevation in a specific cell to the
elevation in cells within a defined neighborhood. For the purposes of this study raw TPI was used as a predictor
raster such that values greater than zero were higher than neighbors (peaks, crests, pinnacles), near zero were
similar to neighbors (flats, plains, or slopes) and values less than zero were lower than neighbors (valley, crevice,
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Habitat Models: Predicting Groundfish Presence in the Del Monte Shalebeds
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8. Aspect is a cyclical variable with both 0 and 360o
indicating North, it was transformed prior to use into
evaluations of Northness and Eastness (Fig. 6) (Guisan et al. 1999, Zhao et al. 2010). Values for
Northness range continuously from 1 (North) to -1 (South). Similarly, Eastness is a continuous raster
ranging from 1 (East) to -1 (West).
Figure 5. Euclidean distance is calculated using a binary raster with a defined cutoff to isolate the
feature of interest. Slight differences in distance to target predictor can be discerned upon close
examination.
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Habitat Models: Predicting Groundfish Presence in the Del Monte Shalebeds
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9. Data Analysis
Fish presence and absence points were used in conjunction with topographic complexity, depth and
aspect to derive a probability raster for fish presence at the Del Monte Shalebeds. A subset of 20% of
fish data was set aside prior to development of models for use in validating the predictive model, this
subset will contain a minimum of 20% of the total data collected for fish presence/absence (Young et
al. 2010).
Predictive models were built using fish presence/absence as response variables and habitat
characteristics as predictor variables using a generalized linear model (GLM, binomial, logit) following
Young et al. (2010). Fish aggregation presence-absence data were associated with the habitat
characteristic values using ArcGIS 10 Extract Multivalue to Point. This feature samples specified
rasters to associate predictor values with the geospatial location of the presence/absence point. Once
associated these values will be used in development of a generalized linear model (GLM) to create a
predictive model for rockfish aggregations. MGET automatically interfaces with the R Statistical
program (R Development Core Team 2010) to analyze habitat associations and develop a predictive
model using a backward stepwise procedure.The Akaike Information Criterion (AIC) was used to
determine variable contribution.
The models built through this analysis were tested for accuracy using Cohen’s Kappa as a measure of
agreement between predicted and actual presence and absence data. Cohen’s Kappa is considered
a robust measure of agreement as it takes into account agreement occurring by chance (Cohen
1960). The having the lowest AIC value was selected as the final model.
Figure 6. Aspect derivatives of Northness and Eastness are calculated based on simple trignometric
formulas.
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Habitat Models: Predicting Groundfish Presence in the Del Monte Shalebeds
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10. Results
The selected model was validated using the 20% fish presence/absence set aside prior to model
development and showed good agreement with the model (Cohen’s Kappa = 0.6135, accuracy =
81%, true presence 90%). Predictor variables Slope, Depth and Eastness were significant (p-value <
0.05) and predictors VRM and distance to TPI showed stronger significance (p-value < 0.001). The
formula used in the final model (Fig. 7) was:
Presence ~ Slope + Eastness + Depth + distance to TPI + VRM
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11. Discussion
This research indicates rockfish presence can be predicted using landscape ecology measures and
remote sensing technology with good accuracy. Further, this research supports the hypothesis that
Figure 7. Probability raster with presence/absence data used in development of the model (hollow) and
validation of the model (filled).
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12. rockfish presence varies predictably with habitat characteristics of complexity and depth. The results
offered here support the conclusions of previous research indicating marine biota can be predicted
using measures of habitat structure and further the use of remote sensing in prediction of rockfish.
Future research should address questions regarding :
• The relationship between rockfish presence and the direction of cold, nutrient rich waters from
upwelling zones.
• The potential for differences in site fidelity at hard substrate of varying relief. Current research is
divided as to the site fidelity of different species of Sebastes spp. generally indicating strong homing
instincts with potentially conflicting results for mobility in and around their home range (Matthews
1990, Reynolds et al. 2010).
• The role isolation plays in distribution and abundance of rockfish aggregations (de Leon 2010).
The viability of economically valuable rockfish species relies on the ability to successfully identify
preferred habitat and the species associations. The results of this study can be used to inform
fisheries managers as to the potential habitat suitability of low or high relief, rocky substrate for
rockfish. This information can identify habitat associations for Sebastes spp. which can be used in the
further development and maintenance of marine protected areas as part of ongoing efforts to rebuild
rockfish stock.
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