Predictive Model of Habitat Suitability for the Marbled Murrelet in Western Washington

Abstract

Abstract: The marbled murrelet (Brachyramphus marmoratus) is a small Pacific seabird with a breeding range that extends from the Aleutian Islands to central California. Throughout most of its breeding range, it uses mature and old‐growth coniferous forests as nesting habitat. Although most murrelets seem to nest within 60 km of the coast, occupied nesting habitat has been identified as far as 84 km from the ocean in Washington state. Due to the extensive inland distances within which birds are known to breed, the area requiring surveys to identify breeding sites can be enormous. Therefore, the standard 2‐year survey protocol can be expensive and time‐consuming for forest management agencies and companies to administer. We developed a logistic regression model to determine whether a suite of forest structural characteristics could be used to reliably predict occupancy of a forest patch by marbled murrelets. We tested the performance of the final model using cross‐validation procedures and a sample of independent sites. We used 50 sites surveyed for marbled murrelets to estimate the model, and 48 independent sites were available to test model performance. All 50 sites were on private forestland owned by Rayonier located in the western lowlands of Olympic Peninsula within the Sitka spruce and western hemlock transition zones. We sampled forest habitat at each site, and we collected information on 15 explanatory variables. The best‐fitting logistic regression model contained variables that measured number of canopy layers (P<0.001, approx. F test) and mistletoe (Arceuthobium sp.) abundance (P = 0.031, approx. F test). The model misclassified 2 of 33 (94% correct) unoccupied sites as occupied using a classification cut‐off (c) of c=0.25. In the other direction, under cross‐validation the final model misclassified 2 of 17 (88% correct) occupied sites as unoccupied. On a test of the model against an independent sample, using a classification cut‐off value of c = 0.25, the final model correctly classified 36 of 48 sites (75% correct). The final model misclassified 3 of 31 occupied sites as unoccupied (90% correct). Use of predictive models could greatly reduce the amount of forest that requires surveys by screening out those sites with little probability of use and by focusing remaining effort on higher probability sites, resulting in a higher likelihood of identifying occupied sites and thereby more efficiently conserving marbled murrelet nesting habitat.