A large-scale model for the at-sea distribution of Marbled Murrelets (Brachyrampohus marmoratus) during
the breeding season in coastal British Columbia, Canada (DRAFT)
** This paper is currently in preparation for publication. For further interpretation of results, and questions, please contact authors: Peggy Yen and Falk
Huettmann, Centre for Wildlife Ecology, Biology Dept., Simon Fraser University (SFU), 8888 University Drive, Burnaby BC V5A 1S6 Canada, ppyen@sfu.ca
The role that the marine environment plays in the distribution and
abundance of Marbled Murrelets (Brachyramphus marmoratus) during
the breeding season is not well understood. Therefore, it was
investigated how Marbled Murrelet marine distribution and abundance is
affected by the abiotic and biotic components of the marine
environment. Data on the marine distribution of Marbled Murrelets in
British Columbia (BC), densities (birds/km2; 1972~1993), and counts
(no. of birds; 1922~1989), and pertinent environmental variables as
identified from literature were compiled and then organized in a
Geographic Information System (GIS, Arcview). On a 10-km scale,
count surveys were found to be negatively correlated with density
surveys and not used further in this study. This also indicates care
should be used when interpreting count data (relative abundance).
All surveys compiled from multiple sources
by Peggy Yen and Falk Huettmann.
We build a parsimonious model to explain marine densities with
marine predictors. First, significant predictors were identified with GLM
(Generalized Linear Models) by evaluating their shortest distances from
survey locations to the predictors and overlays in a multivariate scenario.
Model predictors selected by using AIC and P values include sea surface
temperature, herring spawn index, estuary locations, distribution of sand
and fine gravel substrates (proxy for sand lance distribution), and
proximity to glaciers. Second, spatially explicit large-scale distribution
model algorithms use this set of significant predictors to predict Marbled
Murrelet abundance (density), distribution and populations in coastal
British Columbia (BC). The modelling algorithms used include GLM,
Classification and Regression Trees (Tree from S-PLUS and CART from
Salford System and Breiman et al. 1984), Multivariate Adaptive
Regression Splines (MARS, Salford Systems), and Neural Networks
(NNet, Venables and Ripley 1994). Model performances were evaluated
by bootstrapping, and standardizing models.
Tree was identified as the best performing model and therefore used to predict maximum carrying capacities and population of
170,500 birds for the marine habitat of BC. The remaining variance of the model was explained with shortest distance to old-growth
forest, which led to the hypothesis of how the Marbled Murrelet distribution and abundance relates to proximity to old-growth forests.
References sited:
Breiman, L., Friedman J. , Olshen, R. and Stone C. 1984. Classification and Regression Trees. Pacific Grive, Wadsworth.
Venables, W. N. and Ripley, B. D., 1994. Modern Applied Statistics with S-Plus 2nd ed. Statistics and Computing. Springer Verlag,
New York, 462 pp.