Download Brooke Wikgren, New England Aquarium, with Kerry Lagueux

Brooke Wikgren, New England Aquarium, with Kerry Lagueux
[email protected]
Relative Distribution of Baleen Whales in the Gulf of Maine: An innovative approach to
mapping relative species distribution using an ordinary kriging interpolation method
Brooke Wikgren, New England Aquarium
Kerry Lagueux, New England Aquarium
With a growing demand for marine spatial planning to mitigate conflicts between existing
and future ocean uses, determining the relative spatial distribution of marine animals has
become increasingly important. Traditional distribution analyses based on survey
sightings can create highly variable spatial data and is greatly dependent on survey effort.
To help account for this, a methodology was created incorporating survey effort with the
sightings data resulting in an index termed sightings-per-unit-effort (SPUE) and involves
assigning calculated SPUE values to spatially explicit gridded cells based on latitude and
longitude. Mapping SPUE species distributions by gridded cells is a widely used
practice; however, SPUE data is often sparse and can be difficult to interpret. In addition,
many marine animals are highly migratory, and mapping based on arbitrarily defined
cells can be unrealistic as these animals are not constrained to the grid cell they were
sighted in. In response to this, we have developed a kriging methodology to smooth the
relative species density and fill in un-sampled areas with predicted values based on
spatial autocorrelation. The resulting interpolated surface provides a more realistic
species distribution that is easier to interpret and provides a visually enhanced dataset for
mapping purposes. For this exercise annual SPUE data for all baleen whales was used to
compare the more traditional gridded cell mapping method with that of our improved
interpolated method.
Sightings per Unit Effort:
Marine animal distributions are commonly mapped based on survey sightings data. A
major issue, however, with spatial distribution and habitat-use patterns based on raw
sightings data is that the species distributions are usually biased by the distribution of
survey coverage (“effort”). To overcome this possible bias, survey effort is quantified
and sighting frequencies are corrected for differences in effort, producing an index
termed sightings-per-unit-effort (SPUE). The units are numbers of animals sighted per
unit length of survey track. SPUE values are computed for consistent spatial units and
can therefore be mapped or statistically compared across areas, seasons, years, etc.
Development of this method was begun during the Cetacean and Turtle Assessment
Program (CETAP) (1982), and it has been used in a variety of analyses (Kenney and
Winn, 1986; Winn et al., 1986; Kenny, 1990; Hain et al., 1992; Shoop & Kenney, 1992;
Kraus et al., 1993; Pittman et al., 2006).
The SPUE method requires partitioning the study area into a regular grid based on
latitude and longitude. The selected grid size is a compromise between resolution
(smaller cells) and sample sizes (larger cells) and can only be determined after
preliminary examination of the available survey data. Studies have used cells ranging
from 1’ x 1’ to 10’ x 10’. For this project 5’ x 5’ cells were used. Aerial and shipboard
survey tracks were broken down into grid cells and their lengths computed. Sightings
were also assigned to cells and the numbers of sightings per species were summed by
cell. The number of animals in each cell was divided by the effort value and multiplied
by 1,000 to avoid small decimal values, creating a SPUE index in units of animals per
1,000 km of survey track. This analysis was done on custom programs in SAS 9.3.1
(SAS Institute, Inc., Cary, NC).
Mapping SPUE by their corresponding gridded cells is a common practice (e.g., Shoop &
Kenney, 1992; Kraus et al., 1993), however, the SPUE data is often sparse and can be
difficult to interpret. This can be especially true when mapping highly migratory species
who are not physically constrained within the grid cell they were sighted in. To
overcome this issue, a kriging methodology was developed to smooth the relative density
and expand values outside the constrained grid cell boundaries. The resulting
interpolated surface creates a more realistic species spatial distribution that is easier to
interpret and more visually appealing.
Interpolation Methods:
Sightings per Unit Effort (SPUE) data, as described above, was provided from the Right
Whale Consortium (RWC) database and consists of sightings and survey effort from
1978 to 2009 that has been compiled from multiple agencies and organizations into a
single database. The SPUE results were summarized to grid cell center points and
presented in dbase files containing the species; SPUE calculation; latitude and longitude
of 5’ x 5’ cell centerpoint; season (annual, spring, summer, and autumn, and winter);
number of animals, and kilometers of trackline effort. For this exercise, annual data for
a species grouping of all baleen whales was used. The dbase file was imported into
ArcGIS using the latitude and longitude of the 5’ x 5’ centerpoint locations in the WGS
1984 geographic coordinate system. The file was exported into an ArcGIS point feature
class inside a File Geodatabase. The feature class was projected into UTM Zone 19
North, North American Datum 1983 for the kriging interpolation. The resulting point
dataset was a regular spaced grid of points with SPUE values for the annual distribution
for all baleen whales.
ArcGIS’ Geostatistical Analyst was used to create an ordinary kriging interpolation of all
baleen whales distributions from the SPUE point data. Kriging is a geostatistical method
that builds on mathematical and statistical models of spatial autocorrelation and uses
these relationships to create an interpolated surface (ESRI 2010). Spatial autocorrelation
is the tendency of locations closer together to be similar in values, (Bolstad 2008) and
this relationship can be modeled using a semivariogram during the kriging process. The
semivariogram is determined by plotting the average semivariance between all points
used in the model at increasing distances. There are many parameters to specify when
modeling the semivariogram function, these include: type of model to fit (i.e. Guassian or
Exponential), lag distance (distance to bin data) and total number of bins (lags). The
semivariogram model is used to weight the points in the search neighborhood to
determine the spatial prediction. The Guassian model was the best fit model to our data
points and was the model used in the interpolation. The average nearest neighbor of the
SPUE points was used as the lag size and the default, 12, was used for the number of
lags. We used a smoothing neighborhood for our predictions which adjusts the distance
weights determined from the model using a sigmodial function away from the prediction
location up to a distance equal to 2 times the Major Semiaxis (ESRI 2010, Gribov and
Krivoruchko 2004). A smoothing factor of 1 (the maximum) and a major and minor
semiaxis of 20 km to include at least 6 points into our calculations was used for our
predictions. The smoothing neighborhood decreased the interpolated surface values by
approximately a factor of 10, however, the distributions match the overall SPUE point
distributions and the interpolated surface provides the relative abundance for all baleen
whales.
The final model of all baleen whales annual distribution was exported from an ArcGIS
Geostatistical Layer to an ArcGIS raster grid with a 250 meter cell size. Any negative
values as a result of the interpolation were reclassified to zero. The raster grid was
mapped and symbolized on a stretched scale representing baleen whales relative SPUE.
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