Dear MARMAM Colleagues,
We are pleased to announce the following NOAA Technical Memorandum publication:
Berini, C. R., Kracker, L. M. and W. E. McFee. 2015.
Modeling pygmy sperm whale (Kogia breviceps, De Blainville 1838) strandings along
the southeast coast of the United States from 1992 to 2006 in relation to environmental
factors. NOAA Technical Memorandum NOS NCCOS 203. 44 pp.
Abstract:
Pygmy sperm whales are the second most commonly stranded marine mammal in the
Southeastern Unites States (SEUS). They most often strand alive and the causes of these events
remain largely unknown. Generalized linear models were built to identify potential relationships
among environmental factors and the occurrence of pygmy sperm whale strandings in the SEUS.
Two methods were used to model environmental parameters depending on the nature of the data.
One method used data from NOAA buoys compiled over a week before a stranding event.
Predictor variables included hourly wind direction and speed, wave height, average wave period,
barometric pressure, and water temperature. The other method used Sea Surface Temperature
data from satellite images compiled monthly, monthly Multivariate El Niño Southern Oscillation
Index (MEI), and bathymetric data. Frontal features were extracted from the images using
ArcMap Geographic Information System and landscape metrics were computed on these images
in FRAGSTATS. The model compiled from buoy data was relatively stronger (AIC = 497.5) at
predicting strandings. It indicated that more strandings occurred when there were sustained high
wind speeds, low barometric pressures, and swell waves in the week before stranding events.
While the other model was relatively weaker (AIC = 718.7), it showed that less numerous fronts
and high MEI index were generally associated with a higher number of strandings. This study is a
step toward appreciating which environmental factors may contribute to the observed marine
mammal stranding patterns as well as the distribution of pygmy sperm whales. It is an attempt at
building predictive statistical models that could be useful for the management of cetaceans.
Thank you.
Wayne McFee
NOAA/NOS/NCCOS/CCEHBR
219 Ft. Johnson Rd.
Charleston, SC 29412