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USGS2018

Barred owl research needs and prioritization in California

Barred owls (Strix varia) have reached high densities within the range of the northern spotted owl (S. occidentalis caurina) and are rapidly increasing in number within the range of the California spotted owl (S. o. occidentalis). Encroaching populations of barred owls pose a significant competitive threat to the viability of both spotted owl subspecies in California. In response, the Director of the California Department of Fish and Wildlife (CDFW) convened the California Barred Owl Science Team (BOST) to identify and address the threat posed by barred owls to spotted owls in California. BOST is composed of subject matter scientists with a goal to provide objective scientific review and recommendations to CDFW to promote the recovery and conservation of spotted owls in California. In this document, BOST identifies, describes, and prioritizes key research needs for barred owls that have the potential to benefit the conservation of spotted owls in California. Key research needs were identified from multiple in-person and remote meetings of BOST, with considerable input from attending representatives from state and federal natural resource agencies. BOST and liaisons recognize that periodic updates of this document will likely be required as more is learned about the ecology of barred owls in California and research needs and priorities evolve. Research that BOST deemed the most likely to provide a rigorous scientific basis for reducing barred owl populations included experimental removals, along with ecological studies expected to generate information that would provide avenues for the effective management of barred owls. Other high priority research needs include research using biological samples obtained during experimental removals to better understand the ecology of barred owls and the threats they pose to spotted owls and associated wildlife. We conclude the document with a discussion of how projects are related to the State Wildlife Action Plan. In Appendix I, we discuss considerations for maximizing the success of proposed removal experiments and ecological research on barred owls.

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USGS2017

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

The Auk↗