Data from camera surveys identifying co-occurrence and occupancy linkages between fishers (Pekania pennanti), rodent prey, mesocarnivores, and larger predators in mixed-conifer forests.

Habitat Use of Pacific Fishers in a Heterogeneous Post-Fire and Unburned Forest Landscape on the Kern Plateau, Sierra Nevada, California

The Pacific fisher (Martes pennanti) is a rare forest carnivore strongly associated with dense, old forest with high canopy cover for denning and resting. The Sierra Nevada population is very small, genetically distinct, and isolated. Mixed-severity wildland fire is assumed to be a potentially greater threat than logging, and land managers are conducting large-scale forest thinning operations u...

Density of Fishers and the Efficacy of Relative Abundance Indices and Small-Scale Occupancy Estimation to Detect a Population Decline on the Hoopa Valley Indian Reservation, California

We used a mark–resight design to calculate density estimates of fisher (Martes pennanti), a candidate for listing under the United States Endangered Species Act, on the Hoopa Valley Indian Reservation in northwestern California, USA in order to determine population status in 1998 and 2005. Our density estimation results and simultaneous population-monitoring data provided a post hoc opportunity...

Movement ecology of fishers (Pekania pennanti) within a semi-urban landscape

Spatial Co-Occurrence and Activity Patterns of Mesocarnivores in the Temperate Forests of Southwest China

Understanding the interactions between species and their coexistence mechanisms will help explain biodiversity maintenance and enable managers to make sound conservation decisions. Mesocarnivores are abundant and diverse mid-sized carnivores and can have profound impacts on the function, structure and dynamics of ecosystem after the extirpation of apex predators in many ecosystems. The moist te...

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to r...