Predicting the occurrence of rare mollusks in northern California forests

• Published in: Ecological applications 2004-06, Vol.14 (3), p.713-729
• Main Authors: Dunk, Jeffrey R., Zielinski, William J., Preisler, Haiganoush K.
• Format: Article
• Language: English
• Subjects: Ancotrema; Ancotrema voyanum; California; Coniferous forests; forest; Forest ecology; Forest habitats; Forest service; generalized additive model; geographic range; habitat association; Helminthoglypta; Helminthoglypta talmadgei; Land management; Modeling; mollusk; Mollusks; Monadenia; Monadenia churchi; Monadenia fidelis klamathica; Monadenia fidelis ochromphalus; Northwest Forest Plan; predictive model; Predictive modeling; Spatial models; Species
• ISSN: 1051-0761; 1939-5582
• DOI: 10.1890/02-5322

Terrestrial mollusks are important components of forest ecosystems, yet we know very little about the distribution and habitat of many of these species. We sampled for terrestrial mollusks in northern California with the goal of estimating the geographic ranges and developing predictive habitat models for five species that were assumed to be sensitive to land management activities. The species of interest were Ancotrema voyanum, Helminthoglypta talmadgei, Monadenia churchi, Monadenia fidelis klamathica, and M. f. ochromphalus. We randomly selected 308 plots for sampling from a grid of points across a 2.2 million-ha study area. We used Generalized Additive Models to estimate each mollusk's geographic range and to develop predictive habitat models within their ranges. Models were developed at one microscale (1 ha) and six mesoscales (ranging from 12.5 to 1250 ha) using vegetation, physical, climatic, and spatial location covariates. Estimated geographic ranges varied from 4770 to$15 795 km^2$. Predictive habitat models explained from 40.8% to 94.5% of the deviance in models describing the species' occurrences. Models at the 1-ha scale were generally better than models at larger spatial scales. Of the six mesoscales evaluated, the "best" models were often at very large scales. Spatial location and climatic variables contributed significantly to the predictions of occurrence for most species. Models for species with small geographic ranges generally appeared to be better than models for species with larger geographic ranges, possibly reflecting more restricted environmental conditions. Cross-validation results, however, showed that models for species with more locations were more stable. A. voyanum was more frequently associated with late-successional forests and M. churchi was found to be a habitat generalist. The remaining three species were not detected enough for us to make strong conclusions about their habitat associations. Our results provide important guidance to land managers who are responsible for determining the necessity for surveys and protective measures for these and other terrestrial mollusk species prior to land management activities.