7 September 2004
King’s River Project: C alifornia Spotted Owl Module Study Plan
Principal Investigator s
John Keane
1
and Thomas Munton
2
Sierra Nevada Research Center, Pacific Southwest Research Station, USDA Forest
1
Service.
2
1731 Research Park Drive Davis CA 95618.
2081 E. Sierra Ave., Fresno, CA 93710.
Approved Peter Stine
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Introduction
Knowledge regarding the effects of fuels and vegetation management on California spotted owls ( Strix occidentalis occidentalis )(CSOs) and their habitat is a primary information need for addressing conservation and management objectives in Sierra
Nevada forests (Verner et al. 1992, Franklin et al. 2004). Uncertainty surrounding the effects of fuels and vegetation management on California spotted owl population viability has been at the heart of the controversy in efforts to develop ecologically sound and socially acceptable management direction on public lands in the Sierra Nevada and many regions of the western United States (Noon and Franklin 2002, USDA Forest Service
2004).
Current fuels management concepts advocate landscape-scale timber harvest and forest thinning treatments such as Defensible Fuels Profile Zones (DFPZs), Strategically Placed
Area Treatments (SPLATS), and Wildland Urban Interface Zones (WUIs), as alternative or complementary approaches to modify fire behavior and effects, as well as to facilitate suppression efforts and protect human communities. The combination of proposed treatments to be implemented will vary geographically across the Sierra Nevada (USDA
Forest Service 2004). SPLATs and WUIs will be used on the Tahoe (excluding the
Sierraville Ranger District), Eldorado, Stanislaus, Sierra, and Sequoia National Forests and the Lake Tahoe Basin Management Unit. Fuels management on the Lassen, Plumas, and Sierraville Ranger District of the Tahoe NF will follow direction mandated in the
Herger-Feinstein Quincy Library Group (HFQLG) Act, which consists of DFPZs and
WUIs. Additionally, a group selection timber harvest strategy will be implemented as part of the HFQLG project.
The King’s River Project (KRP) is a key component of the adaptive management strategy for assessing the effects of timber harvest and fuels treatments on wildlife, watersheds, and fire behavior (USDA 2004). The goal of the KRP is to use uneven-aged silviculture and prescribed fire to move forest structure and composition across the greater part of two watersheds toward a desired condition estimated to be more similar to pre-1850 forest conditions prior to fire suppression. Specific objectives are to reduce fire risk near human developments through use of DFPZs within WUIs, establish a sustainable level of timber products through forest thinning and regeneration/group selection harvest, reduce the potential for catastrophic habitat loss to insects and wildfire, and provide opportunities for scientific study of treatment effects.
A description of the desired future condition with supporting logic and documentation is provided by Rojas et al. (2003). The KRP area encompasses 131,500 acres in the adjacent Dinkey Creek and Big Creek watersheds on the Sierra National Forest.
Approximately 72,000 acres are projected for treatments between 2004-2033 (Figure 1).
A combination of forest thinning and regeneration/group selection silviculture prescriptions and prescribed burning will be used to move existing stands toward desired future conditions over a 25-30 year period. Eight management units totaling approximately 13,700 acres are scheduled for treatment between 2004-2008 (Figure 1).
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Details on silvicultural prescriptions are provided in the Proposed Action for the KRP (In review).
Landscape forest thinning and fuels treatment strategies will be implemented at large spatial scales on the order of 100s-1000s of acres and will be the dominant vegetation management activity affecting CSOs and the forest landscape. Resulting changes in vegetation structure and composition from treatments may affect CSOs and their habitat at multiple spatial and temporal scales. Key scientific uncertainties regard the effects of landscape-scale fuels treatments strategies on CSO density, population trends, and habitat suitability at the landscape scale, and how thinning affects habitat quality and habitat use at the core area/home range scale. The goal of this module is to assess the effects of timber harvest and fuels treatments proposed in the KRP on CSO populations and their habitat.
Study Design Constraints and Opportunities
This study was initiated jointly by Forest Service management and the Pacific Southwest
Research Station to address uncertainty surrounding the effects of timber harvest and fuels treatments on select response variables of interest, including CSOs and their habitat.
It is imperative that all interested parties have a clear understanding and realistic expectations of the types of inferences and learning opportunities that are possible under the current study design. The strength of inferences addressing cause and effect relationships is determined by the design of a study and the degree to which treatments are planned and implemented to meet research and/or management objectives. Kendall
(2001) summarizes the range of relationships that can exist when balancing management objectives focused on a management component with research objectives focused on a learning component within an adaptive management framework.
Low emphasis on the management component and high emphasis on the learning component calls for classic experimentation. Classic experimentation requires strong control over the design of treatment types and their implementation in time and space.
The goal of experimentation is to control for as many confounding factors as possible to generate strong inferences regarding cause and effect relationships. While desirable from a scientific perspective, classic experimentation is extremely difficult to conduct in the natural world, particularly at larger spatial scales, because of inherent variation at multiple spatial scales resulting from natural and human induced effects, long time periods needed to understand many response variables, lack of long-term management agency willingness in the face of shifting priorities, difficulty with implementing treatments over large areas in short time periods to meet research needs given limited management personnel to plan and conduct treatments, and logistical and financial challenges working at large scales.
At the other end of the spectrum, passive adaptive management places high emphasis on the management component and low emphasis on the learning component. Under this model, management objectives determine the types and timing of treatments as dictated
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7 September 2004 purely by management priorities. Science can be conducted under a passive adaptive management framework by comparing predicted and observed dynamics (Kendall 2001).
However, inferences regarding cause and effect relationships are much weaker. Under passive adaptive management, research focuses on assessing the association between a treatment and the observed result while attempting to control for as many confounding variables as possible.
This study will be conducted under a passive adaptive management framework.
Approximately 50 CSO territories are known in the KRP area, although not all territories are currently occupied (Figure 2). Initially we explored the possibility of designing a classic experiment where the known CSO territories would be randomly assigned to either a treatment or control group, with territories paired based on current vegetation conditions within home ranges. This rigorous of a design could not be accommodated by management because of: (1) initial fuels treatments needed to be placed in high priority areas to reduce fire risk to human development rather than randomly assigned to meet
CSO study design criteria; (2) initial treatment areas were to be separated in space to reduce possible detrimental effects on other species of conservation concern (e.g., fisher
( Martes pennanti )); and (3) existing management staff levels are inadequate to conduct planning for more than 2-3 treatment areas per year and it would therefore require 10-15 years to treat 20-25 CSO territories.
Given the above design constraints, our study is designed to maximize the amount of information that can be learned from the treatments that will be implemented to meet multiple management objectives. The goal of the KRP is to move the entire project area into the desired condition over the next 25-30 years. It is uncertain if and how CSOs will persist under the desired future conditions across large watersheds. Our goal is to standardize, to the extent possible given design constraints, the types of treatments that each territory receives such that we will be able to generate limited inferences to the effects of the KRP at the CSO individual pair level of organization and PAC/home range scale. Nine CSO territories are scheduled for treatment in the first phase of KRP implementation between 2006-2008 (Figure 3). To attain future desired conditions across these nine territories, all land within a 1000-acre circle centered on a CSO nest site within each territory will be treated. The 1000-acre circle functions as a surrogate for the average sized CSO home range in the study area. Management within CSO territories will differ within the Protected Activity Center (PAC) versus outside the PAC across the remainder of the 1000-acre home range. PACs are 300-acre areas centered on CSO nest and roost sites and are designated to protect the core area of a CSO home range. PAC treatments will follow current management direction for PACs within the Defense Zone of WUIs under the current Sierra Nevada Framework Amendment (2001). Details of
PAC treatments under the current SNFPA ROD and KRP, along with a comparison of proposed PAC treatments under the Revised SNFPA ROD (2004) are provided in Table
1. Treatments outside of PACs within the remainder of the home range will be dictated by the existing condition and desired future condition for each particular forest stand as determined by overall KRP objectives. Approximately one-half of two additional PACs will likely be treated in 2004 as part of the ongoing Shaver Lake Project.
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In general, project implementation will follow normal Forest Service procedures, whereby the planning process involves one year of project planning followed by advertisement of the multiple contracts associated with a project and subsequent awarding of contracts to the selected contractors. Once a contract is awarded, the contractor typically has five years to complete the on-the-ground work. Thus, treatments can occur anytime over a five-year period following award of the contract. While the majority of project work will be implemented under this procedure, it may be possible to control the timing of a subset of treatments in the vicinity of owl cores areas. To the degree that this is attainable, it will be possible to conduct habitat use studies at the home range scale to determine if CSOs exhibit acute responses to treatments and whether or not they exhibit changes in habitat use pre- and post-treatment.
The lack of specific research control over the assignment of treatments to experimental units, and spatial and temporal implementation of treatments, precludes the ability of our research to closely couple monitoring of pre- and post-treatment effects on most response variables and defines the type of research opportunities that are available under a passive adaptive management framework. As outlined by Kendall (2001), it is still possible to conduct science and learn under a passive management framework by evaluating observations against model predictions for response variables. Therefore, our work will focus on developing predictive habitat models at two levels of biological organization, specifically the population level and the individual pair or territory level, for the CSO.
The logic for addressing questions at these two levels of organization is discussed in the following section. These models will be used to project the effects of the proposed treatments on CSO numbers, habitat suitability, and habitat quality. Predictions of the models will then be evaluated following implementation of treatments through direct monitoring of CSO response at the levels of the population and individual owl.
Additionally, we will focus on assessing the acute response of CSOs to treatments within
PACs and home ranges.
Our overarching goals are to: (1) maximize the opportunity to learn as much as possible about CSO habitat associations within the constraints and opportunities of the current design; (2) reduce the uncertainty surrounding the habitat needs of CSOs and the effects of vegetation treatments on CSOs and their habitat; and (3) advance basic scientific knowledge on CSO habitat associations, response to treatments, and habitat modeling.
California Spotted Owl: Overview of Status, Habitat Associations and Research
Needs
Spotted owls have received focused research attention for the past 30 years and are among the most extensively studied bird species in the world (Noon and Franklin 2002).
Research attention first focused on CSOs about 20 years ago (Verner et al. 1992). Most work to date has been observational and focused on estimating population trends and demographic parameters (Blakesley et al. 2001, Seamans et al. 2001, Franklin et al. 2004) and descriptive habitat associations (Noon and Franklin 2002).
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The population status of CSOs in the Sierra Nevada is uncertain at the present time.
CSOs are distributed throughout their historic range in the Sierra Nevada and do not appear to have experienced any detectable range contractions at broad scales (Gutierrez and Harrison 1996). Recent demographic analyses are somewhat ambiguous suggesting populations are stable or slightly declining in the Sierra Nevada (Franklin et al. 2004).
Blakesley (2003) reported evidence for declining trends in site occupancy. Uncertainty regarding population trends and status indicates that management agencies should proceed with caution in CSO conservation and management efforts (Franklin et al. 2004).
Much research has been directed at understanding CSO habitat associations. This work has been extensively summarized in Verner et al. (1992), and USDA Forest Service
(2001, 2004). Most work to date has been observational and focused on describing CSO habitat associations at nest sites, foraging sites, and within home ranges.
Recent work has attempted to associate variation in demographic parameters with variation in habitat, with most results to date reporting that habitat did not explain much of the observed variation in reproduction or survival. Variation in reproduction has been associated with variation in habitat at nest site (North et al. 2000) and core area/home range scales (Hunsaker et al. 2002, Blakesley 2003, Lee and Irwin, In Press). Blakesley
(2003) related survival to variation in habitat at core area/home range scales.
Zimmerman et al. (2003) reported spatial variation in potential fitness and occupancy among CSO territories in the San Bernardino Mountains of southern California, but that variation in survival and reproduction was not correlated with variation in habitat.
Despite a strong base of descriptive information on the vegetation classes selected or used for nesting and roosting, and to a lesser degree, foraging, a significant degree of uncertainty exists regarding the amounts and spatial configuration of habitat associated with high quality CSO habitat in the Sierra Nevada. Little is known regarding fine resolution forest structure and prey resource selection at foraging sites used by CSOs.
Work to date on foraging habitat use has focused on the use of vegetation types classified into broad structural classes. The amounts and spatial configuration of habitat within home ranges that provide for high probability of occupancy by a pair of CSOs is unknown. Further, the amounts and spatial configuration of habitat within home ranges that is associated with high fitness, as measured by CSO survival and reproduction, is unknown. Finally, no attention has been focused on the landscape scale to explore how habitat patterns and territorial spacing behavior of CSOs are associated with CSO density and distribution at larger spatial scales.
Recent studies on northern spotted owls and other species have reported findings with important implications for future analyses of CSO habitat associations and the development of predictive habitat models. First, Franklin et al. (2000) reported that survival and reproduction were associated with different suites of habitat and weather covariates. Of greatest significance, optimal habitat fitness was observed under habitat conditions that represented a balance between conditions associated with both high survival and reproduction. Considering only habitat patterns associated separately with either maximum survival or reproduction resulted in lower overall habitat fitness
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7 September 2004 potential. The implication from this study is that it is necessary to jointly consider both survival and reproduction for identifying the suite of habitat factors necessary to provide for high quality CSO territories.
Second, recent work on spotted owl habitat associations have indicated that non-linear relationships between owls and their habitat may be important for understanding owl habitat associations, and subsequently for developing predictive habitat models (Franklin et al. 2000, Zabel et al. 2003, Lee and Irwin, In press). Previous analyses have focused exclusively on linear relationships between CSOs and their habitat. Results of recent work suggest that non-linear relationships may be important. Of particular significance could be the existence of pseudo-thresholds that may indicate management thresholds where, for example, change in habitat conditions could reduce the probability of occupancy and shift a site from suitable to non-suitable. The potential implication of non-linear relationships between CSOs and their habitat is significant and requires focused attention in future habitat modeling exercises.
Finally, the role of territorial behavior in determining the distribution and abundance of
CSOs across larger spatial scales has not been addressed to date. Newton (1979) identified the importance of territorial behavior in regulating the distribution and abundance of territorial raptor species such as CSOs. Understanding the role of territorial behavior in structuring the spatial distribution of CSOs on a landscape is of critical importance for two reasons. First, apparently suitable habitat may not appear to be occupied because neighboring pairs of CSOs may exclude additional pairs from colonizing suitable habitat located within defended territories. Reich et al. (2004) reported that territorial behavior may limit the number of goshawk pairs on the Kaibab
Plateau in Arizona despite the presence of widely distributed suitable nesting habitat.
Second, should a site become unoccupied, two possibilities exist regarding whether there is an overall decline in the number of territorial CSO pairs at larger spatial scales of watersheds and landscapes. The pair of CSOs from a formerly occupied site may have died or emigrated from the study area, both scenarios resulting in a reduction of one pair of birds at the local population level. Alternatively, a pair may be displaced from their original site and have dispersed to another site within the local population, resulting in no net loss of the number of pairs but rather a redistribution of birds within the local population.
Monitoring CSO sites within the larger context of local populations distribution and trend is an important component of assessing potential effects at both the population and individual owl level. The role of territorial behavior has significant implications for understanding the role of habitat and other factors on the distribution and abundance of
CSOs in the project area, and how silviculture and fuels treatments, and restoration efforts, can be optimized within the spatial population structure of CSOs across a landscape.
Two additional recent stressors may have significant impacts on CSOs. Barred owls
( Strix varia ) have been expanding their range into California over the past ten years
(Dark et al. 1998, Peterson and Robins 2003). The first record of a barred owl in the southern Sierra Nevada was recorded in Sequoia-Kings Canyon National Park in 2004
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(G. Steger and T. Munton, Unpublished data). Hybridization occurs between barred and spotted owls when barred owls first colonize an area. However, it appears that the greater long-term risk is that as barred owl numbers increase they will displace spotted owls through direct competition for food and territories (Kelly et al. 2003, Kelly et al.
2004).
West Nile Virus (WNV) has the potential to significantly affect CSOs. Although the potential effects of WNV on CSOs are unknown, owls in general are susceptible to WNV
(Fitzgerald et al. 2003), including species in the genus Strix (e.g., Great Gray Owl, Strix nebulosa ). WNV was introduced to North America in 1999 and first detected in southern
California in 2003 (Komar 2003, Marra et al. 2004, Boyce et al. 2004). WNV has been detected in the KRP study region (CDHS 2004).
Monitoring the effects of barred owls and WNV on CSOs is critical to determine if these factors are significant threats to CSO population viability. Further, monitoring these potential stressors is necessary in order to be able to assess their contribution relative to habitat, territorial behavior and other factors affecting CSO populations in the study area.
Given the conservation concern and political controversy surrounding CSO management it is imperative to have reliable information on the full suite of factors that may impact
CSOs.
In summary, landscape fuels treatments can affect CSOs and their habitat at multiple spatial and temporal scales and at population and individual owl levels of organization.
Key uncertainties exist regarding CSO habitat associations at both levels of organization and how treatments will affect CSOs and their habitat. This module is designed to address key research needs and treatment effects at both levels of organization, to advance scientific understanding of CSO habitat associations, reduce the uncertainty surrounding treatment effects, address implications from recent work on spotted owls, and assess the role of Barred Owls and West Nile Virus on CSOs. Our research addresses
5 specific questions as described in detail in the following sections.
Study Objectives
The CSO module is designed to provide as much information as possible on treatment effects on CSO habitat quality. Within a passive adaptive management framework, our objective is to develop predictive models that project the effects of proposed treatments on CSOs and their habitat, directly monitor CSO response, and subsequently evaluate model predictions and reiterate model development or refinement as necessary. We focus on CSO response variables and habitat associations at two levels of biological organization: the population and individual owl levels. Current information on vegetation and CSO demography and distribution is available from ongoing research and recent inventory work in the study area. Photo-interpreted vegetation maps have been recently completed and updated for the KRP area and Sequoia and Kings Canyon
National Parks and will form the base vegetation information for the development of predictive habitat models (Hunsaker et al. 2001, R. Rojas, Unpublished data, G. Steger,
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Unpublished data). Vegetation polygons have a minimum mapping unit of less than 5 acres and are attributed by vegetation type, tree size class, and cover class. Information on CSO stage-specific survival, fecundity, and population trends is available from ongoing demographic studies started in 1990 being conducted on the Sierra National
Forest within the KRP area and on the Sequoia and Kings Canyon National Parks
(Franklin et al. 2004). These data sources provide an extensive baseline of information and provide an opportunity to develop predictive habitat models, monitor future change in CSOs in response to treatments, and advance scientific knowledge of CSO habitat associations.
Five specific research questions will be addressed. The first three questions address associations among treatments and CSO response at the population and individual owl levels. These three questions address each of the primary information needs proposed in the previous section required to understand the range and types of possible CSO responses. The fourth and fifth questions address important additional stressors that may have significant negative impacts on CSO population trends and population viability in the Sierra Nevada. It is imperative to have this information in order to assess the relative contribution of potential causative factors that may affect CSO populations. The five questions are as follows:
1) What are the associations among timber harvest and fuels treatments and CSO density, distribution, population trends and habitat suitability at the landscape-scale?
2) What are the associations among timber harvest and fuels treatments and CSO reproduction, survival, and habitat fitness potential at the PAC/home range scales?
3) What are the associations among timber harvest and fuels treatments and CSO habitat use and home range configuration at the PAC/home range scale?
4) Are barred owls increasing in the northern Sierra Nevada, what factors are associated with their distribution and abundance, and are they associated with reduced CSO territory occupancy?
5) Does West Nile Virus affect the survival, distribution and abundance of California spotted owls in the study area?
Research Questions
Question 1: What are the associations among timber harvest and fuels treatments and CSO density, distribution, population trends, and habitat suitability at the landscape-scale?
Habitat Association Model: Development
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The objective of this question is to develop predictive habitat models to assess habitat suitability and then predict the probability of site/territory occupancy, and the distribution and number of CSOs in the project area (Boyce and McDonald 1999, Zabel et al. 2003).
The basic approach uses statistical techniques to develop models for estimating CSO habitat suitability using vegetation and environmental covariates collected at CSO sites and non-CSO sites. We will use logistic regression (Hosmer and Lemeshow 1989) and an information-theoretic approach to model development and selection to generate the most parsimonious model or small set of competing models for quantitatively assessing habitat suitability across the project area (Burnham and Anderson 1998, Zabel et al.
2003). A priori candidate sets of potential models consisting of combinations of vegetation and environmental covariates will be developed based on prior scientific knowledge of habitat associations reported in the literature plus additional models based on expert opinion that have not been previously evaluated regarding the structure of the relationship between habitat and CSO occurrence.
Vegetation covariates can be summarized in a large number of ways given that each vegetation polygon is classified by vegetation type, average tree size class, density/cover class, and presence/absence of large trees. Additionally, a large number of landscape pattern metrics can be estimated, such as patch size, amounts of edge or interior, and total amount of each type. Given the large number of classes, previous investigators have generally collapsed the vegetation classification into structural classes based on average tree size and/or cover class depending on the goal of each study and available vegetation information (e.g., Franklin et al. 2000, Hunsaker et al. 2002, Blakesley 2003, Zabel et al.
2003, Lee and Irwin, In press). Franklin et al. (2000) used a dichotomous classification of vegetation into either owl habitat or non-habitat in conjunction with vegetation pattern covariates. Hunsaker et al. (2002) and Lee and Irwin (In press) classified vegetation solely by density/cover classes. Blakesley (2003) created 10 vegetation classes based on size class, density/cover class, presence/absence of large remnant trees and whether they were considered SELECT or OTHER CSO strata by Verner et al. (1992). Zabel et al.
(2003) compared model fit across 6 different vegetation map layers and found that a classification of vegetation polygons into 2 classes (Nesting/Roosting and Foraging classes) based on expert opinion produced the best model compared to existing habitat descriptions used to date in assessing habitat for the northern spotted owl.
We will compare model results between two base vegetation maps using: (1) the photointerpreted coverage; and (2) the corporate Region 5 Landsat-derived coverage.
Vegetation structural classes from both map sources will be aggregated into biologically meaningful categories based on existing literature and expert opinion. One candidate model set will use an expert derived classification of CSO Nesting/Roosting, Foraging, and Other categories, following Zabel et al. (2003). We will include variables related to amount of specific vegetation classes and pattern metrics (e.g., amount of edge).
Candidate environmental covariates will include aspect, slope, slope position, and distance to roads and streams. We will evaluate linear, quadratic and pseudo-threshold forms of each covariate to assess linear and non-linear habitat associations given recent findings that non-linear functional relationships may be important (Franklin et al. 2000,
Zabel et al. 2003, Lee and Irwin, In press). Of particular significance would be
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7 September 2004 identification of pseudo-thresholds as indicated by recent work that suggests thresholds at which a site may become suitable or unsuitable.
Candidate models will be developed at multiple spatial scales to assess habitat associations across spatial scales and to identify the spatial scale associated with the best predictive model. We will initially include 3 spatial scales around each CSO and non-
CSO site. We will use a smallest scale of a 200ha circle (approximate core home range area (Bingham and Noon 1997)) and estimate the largest scale based on ½ of the mean nearest-neighbor distance in the study area to be estimated following completion of 2004 field inventory surveys. The mid-scale will be the mid-point between the smallest and largest scales.
The best model generated from the modeling exercise described in the preceding paragraph will be used to produce a continuous map of habitat suitability across the project area and to calculate a probability of occupancy for each CSO site. Using the best model we will explore the ability of the model to estimate the number of CSOs expected across watersheds given the habitat suitability of the watershed (Boyce and MacDonald
1992, Zabel et al. 2003). Zabel et al (2003) reported a significant, high correlation between estimated numbers of northern spotted owls based on model projections versus actual numbers counted on independent study areas. Models will be evaluated by splitting the data to build the model with one subset and assessing the classification accuracy of the model using the second subset (Johnson 2001). Model evaluation is a necessary step to evaluate the utility of a model for a particular application (Johnson
2001).
Habitat Association Model: Exploratory Issues
Several issues can be explored that may advance understanding of CSO habitat associations and factors controlling CSO distribution and abundance. Although territorial behavior is a primary factor known to determine the distribution, spacing, and abundance of territorial raptor species (Newton 1979), the concept has not been fully incorporated into modeling of habitat for CSOs. The implications of territorial behavior are that there are behavioral constraints that ultimately limit the number and distribution of CSO pairs that may occur across a landscape. Where CSO nest sites are located depends both on the local vegetation and topographic conditions and also the distribution of neighboring CSO territories. Several alternate methods exist for incorporating spatial autocorrelation or structuring (Augustin et al. 1996, Meyer et al. 1998, Knapp et al. 2003, Reich et al.
2004). We will explore the implications of incorporating territorial behavior and the role of spatial autocorrelation and territory spacing for developing habitat models for CSOs.
Additionally, while we will initially use logistic regression to develop predictive habitat models for CSOs there will be opportunities to explore alternative modeling approaches
(e.g., Bayesian Belief networks, Lee and Irwin, In Press) and statistical models (e.g., classification and regression trees, De’ath and Fabricius 2000) for developing habitat models.
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Habitat Association Model: Application and Utility
The habitat model will function as a tool to project estimated effects of KRP implementation on CSO populations and habitat. The best model will be used to generate quantitative estimates of: (1) habitat suitability across the HFQLG project area: (2) the probability of occupancy at CSO sites; and (3) the number of CSOs at the watershed scale. The proposed KRP projects can then be overlain on the base vegetation map, the expected resulting changes to the base vegetation can be determined, and then the habitat model can be used to estimate the expected changes to habitat suitability, the probability of occupancy at CSO sites, and potential changes in CSO numbers. Model results will then be evaluated against results from monitoring of direct effects on CSOs to assess the utility of the model and suggest factors to improve the next generation of predictive habitat models.
The habitat modeling exercise should also advance basic knowledge of scientific understanding of CSO habitat associations, CSO distribution and territory spacing, and the potential role of habitat thresholds in the southern Sierra Nevada. While the model will be used specifically to project anticipated effects of KRP implementation, it will also function more generally as a tool to compare the projected effects of alternative management scenarios to assess trade-offs among differing management hypotheses.
Further, incorporating the role of territorial spacing into our knowledge about CSO density and distribution across a project area will likely lead to advances in forest restoration strategies and optimization modeling exercises that search for optimal solutions for the placement of fuels treatments and habitat conservation in a spatial context.
CSO Population Monitoring:
The objective is to monitor CSO occupancy, distribution and population trend across the
KRP. A strong baseline data set exists from the ongoing Sierra demography study as previously described. The Sierra demographic study area encompasses the entire KRP project area. CSOs have been annually monitored and band for the past 15 years. Our sampling is structured to utilize the extensive baseline of information on CSO density and demographics that we have available from this ongoing effort and to maximize the information that can be garnered from our investment in this project. We will continue to monitor CSOs across the entire KRP project area. Primary sampling units (PSUs) within the project area consist of approximately 1000-acre polygons have been established for surveys to determine CSO occupancy.
CSO surveys will be conducted annually following standardized spotted owl research protocols (Franklin et al. 1996). Briefly, all PSUs are surveyed a minimum of three times per year for CSO using extensive surveys at set survey point locations distributed to provide 100% survey coverage. Status visits are conducted in PSUs with known CSO pairs or where CSOs are detected during extensive surveys to determine pair status, reproductive status and output, to color-band all territorial birds, and band juveniles.
Mark-recapture techniques and reverse-time models will be used to estimate population
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7 September 2004 growth rates across the study area based on color-banded territorial CSOs (Nichols 1992,
Pradel 1996, Nichols et al. 2000, Franklin et al. 2004). This metric provides an estimate of whether the CSO population in the study area is stable or changing over time as the
KRP is implemented.
Occupancy will be estimated using methods to account for detectability (MacKenzie et al. 2003). We will model change in occupancy status relative to potential covariates and combinations of covariates to identify factors associated with change in occupancy status and the best model that fits the data using an information theoretic approach. Covariates will include habitat, change in habitat resulting from treatment, environmental factors
(e.g., elevation), weather factors (precipitation, temperature), presence of barred owls or spotted-barred owl hybrids, and presence of West Nile Virus. Many potential factors, as well as interactions among factors, could be associated with change in CSO occupancy.
Given lack of control over the spatial and temporal implementation of treatments, some
CSO sites will not receive treatments within their home ranges while other will receive varying degree of treatment dependent on where they are located relative to proposed
DFPZs and WUIs (example Figure 2). Therefore, we will attempt to model how change in occupancy may be associated with a suite of potential covariates, including treatment effects. Our estimated sample size of CSO territories should be in range of 40-45.
Together, estimating population trend and assessing factors associated with change in occupancy status provide complementary insights into the overall trend of the population.
Should a change in population trend occur, we would evaluate which factors are associated with observed change in site occupancy. Further, as discussed previously under CSO research needs, changes in site occupancy must be considered in the context of the larger CSO population across larger spatial scales.
Question 2: What are the associations among timber harvest and fuels treatments with CSO reproduction, survival, and habitat fitness potential at the PAC/home range scale?
Habitat Association Model: Development
The modeling and monitoring proposed under Question #1 addresses issues regarding
CSO habitat suitability, numbers and distribution at the landscape scale. Further,
Question #1 investigates whether there are potential quasi-thresholds at which a site becomes either suitable or unsuitable for CSO occupancy, which vegetation and environmental variables, in what form and at what spatial scale, are associated with potential thresholds, and what role territorial behavior and spacing play in determining the distribution and number of CSO pairs across a landscape. The research objective under Question #2 complements the research conducted under Question #1 by investigating whether there are differences in reproduction, survival, and habitat fitness potential at the scale of individual CSO territories that are associated with variation in vegetation and environmental factors (Franklin et al. 2000, Blakesley 2003, Lee and
Irwin, In press). That is, within the range of conditions across home ranges established
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7 September 2004 under Question #1 are there suites of vegetation and environmental factors associated with relatively higher reproduction, survival, and habitat fitness potential. As Franklin et al. (2000) have demonstrated, variation in reproduction and survival may be associated with different sets of covariates and it may be necessary to consider a summary metric such as habitat fitness potential to evaluate an optimal suite of factors associated with increased fitness.
We will extend the modeling work conducted by Hunsaker et al. (2001) and Lee and
Irwin (In press) to associate reproduction, survival and habitat fitness potential to variation in vegetation, environmental, and weather variables to develop the best model for each demographic metric. Sets of a priori candidate models based on relationships suggested in the literature and expert opinion will be developed and evaluated using an information theoretic approach for identifying the best model (Burnham and Anderson
1998). Similar to the modeling exercise described under question #1 to estimate habitat suitability, we will develop and compare models using the same 2 base vegetation coverages (see above) at the same 3 spatial scales around CSO territories, include both amounts and pattern metrics for select vegetation classes, and assess potential linear, quadratic, and pseudo-threshold functional relationships. Data collected between 1990-
2004 on the Sierra National Forest and Sequoia and Kings Canyon National Park demographic studies (Franklin et al. 2004) will provide the estimates of CSO survival, reproduction, and habitat fitness potential for model development.
Habitat Association Model: Application and Utility
Similar to the predictive habitat models developed under Question #1, the goal of research under Question #2 is to generate predictive habitat models that will function as a tool to project estimated effects of KRP implementation on CSOs and habitat. The difference is that these models will focus on projecting the potential effects of KRP on the quality of individual CSO territories as measured by survival, fecundity, and habitat fitness potential. The best model will be used to generate quantitative estimates of habitat quality across all CSO territories in the study area. The proposed KRP projects can then be overlain on the base vegetation map, the expected resulting changes to the base vegetation can be determined, and then the habitat model can be used to estimate the expected changes to habitat quality as measured by projected changes in survival, reproduction and habitat fitness potential. Model results will then be evaluated with respect to results from monitoring of direct effects on CSOs to evaluate the utility of the model and suggest factors to improve the next generation of predictive habitat models.
Results will also advance basic scientific knowledge of CSO habitat associations and the degree to which variation in demographic parameters is associated with variation in vegetation, environmental, and climatic covariates. This type of information is necessary for addressing current uncertainty regarding management effects, identifying factors or sets of interacting factors associated with habitat quality and CSO fitness, and improving scientific ability to project the possible effects of alternative management strategies. If strong patterns emerge, results may generate explicit hypotheses regarding habitat patterns associated with increased fitness that can be tested in future treatments as part of the adaptive management process (Noon and Franklin 2002).
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7 September 2004
CSO Monitoring:
CSO survival and reproduction will be monitored annually across all territories surveyed as described in the CSO Population Monitoring section under Question #1. All nonjuvenile CSOs will be uniquely color-banded and juveniles will be banded with standard
USFWS aluminum leg bands (Franklin et al. 1996). Pair status, reproductive status, and reproductive output of each pair will be estimated annually following protocols presented in Appendix A. Mark-recapture models will be used to estimate survival with program
MARK (White and Burnham 1999). Data will provide trend estimates of survival and reproduction over time, as well as estimates of territory specific survival, reproduction, and habitat fitness potential.
Question 3: What are the associations among timber harvest and fuels treatments, and CSO habitat use and home range configuration at the home range scale?
In addition to effects at the population level-landscape scale (Question #1) and individual level-territory quality scale (Question #2), timber harvest and fuels treatments may also have acute effects on home range configuration and habitat use by CSOs within their home ranges during and immediately following project implementation. Acute responses may range from no effect, to shifts in use of prey species, habitat or space within home ranges, to territory abandonment, or to reproductive failure or death during periods or seasons of treatment implementation. Question #3 addresses pre- and post-treatment behavioral responses and home range configuration, habitat use, and prey use patterns of a subset of owl pairs to treatments within core areas of home ranges. Radio-telemetry will be used to quantify habitat use, home range configuration, and habitat suitability pre- and post-treatment on a subset of CSO pairs that occur in areas that will be treated.
Pending adequate funding, environmental covariates (e.g., elevation, slope, slope position, distance to stream) and plot-based vegetation measurements will be collected at
CSO nesting, roosting, and foraging locations to provide greater resolution into forest structural characteristics associated with CSO habitat use. A modified Forest Inventory and Assessment plot will be used to measure vegetation characteristics at CSO use sites and random sites within home ranges. Data will be used to develop models describing the fine-scale stand attributes associated with CSO habitat use (North and Reynolds
1996).
Sampling will occur within the 2-3 occupied CSO home ranges per year that are scheduled for treatment between 2006-2008 (Figure 2, Appendix A) following the protocol for assessing treatment effects proposed by McDonald and McDonald (2002).
The degree to which the research objectives can be met is dependent on management ability or willingness to control the timing of select treatment within the core areas of target CSO home ranges to allow research to gather pre-treatment information for one year prior to treatment followed by implementation of treatment, followed by collection of post-treatment data for a minimum of one year. If treatments cannot be controlled in time then it will be difficult if not impossible to address the research objectives given
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7 September 2004 limitations with battery life for telemetry units, and turnover and movement of CSOs within home ranges. In this case, telemetry would improve basic scientific understanding of CSO habitat use and home range configurations in the northern Sierra Nevada, and the specific forest structure and composition factors and environmental factors associated with habitat use.
4) Are barred owls increasing in the southern Sierra Nevada, what factors are associated with their distribution and abundance, and are they associated with reduced CSO territory occupancy?
Barred owls have recently expanded their geographic range into the Sierra Nevada and pose a potentially significant threat to CSO population viability. Although barred owls have only recently been detected in the southern Sierra Nevada, it is likely they will increase in distribution and abundance in the coming years. The objective of this question is to monitor the distribution and abundance of barred owls across the study area. Because barred owls may displace or replace spotted owls in a landscape it is necessary to monitor their potential effect on CSOs in the study area and investigate if there are vegetation, environmental, or treatment factors associated with barred owl occurrence. Barred owls will respond to CSO broadcast surveys and their distribution and abundance will be monitored during field surveys for CSOs.
At the present time there is one record of a barred owl within the project area over the past 15 years. Should barred owls increase in numbers these data on their occurrence will be used to develop a predictive habitat model to assess which vegetation, environmental, or treatment factors are associated with barred owl occurrence relative to CSO occurrence. Data on barred owl distribution would also be used as covariates in models of CSO occupancy (Question #1), and perhaps reproduction and survival (Question #2), to investigate the association of barred owl occurrence on CSO response variables.
Information on barred owl distribution and abundance is necessary to determine which factors, or set of interacting factors, is associated with changes in CSOs across the study area and the relative contribution of barred owls to the observed change. Further, understanding habitat associations and other factors associated with CSO and barred/spotted-barred hybrid owl distribution and abundance will be necessary for developing conservation strategies should barred owls increase in the Sierra Nevada and pose a significant risk factor to CSO populations viability
5) Does West Nile Virus affect the survival, distribution and abundance of
California spotted owls in the study area?
The effects of West Nile Virus on CSO population viability are unknown. However,
WNV is known to result in high mortality in numerous owl species. Hence, WNV may have a significant impact on CSO population viability in the Sierra Nevada. Monitoring the potential effects of WNV on CSOs in the study area is critical for determining potential causative factors if CSO population declines occur relative to other potential
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7 September 2004 contributing factors. The objective of this question is to determine if West Nile Virus
(WNV) has a significant effect on CSO populations in the study area and whether there are vegetation and environmental factors associated with risk exposure to the disease. To investigate potential WNV effects we will monitor the distribution of WNV over time across the study area, exposure of CSOs to WNV, and changes in survival and reproductive success.
Blood samples will be collected from all CSOs captured as part of normal banding operations to identify WNV antibodies to determine exposure to WNV. This work is being conducted in collaboration with researchers from the School of Veterinary
Medicine at the University of California, Davis. Blood samples will be analyzed at the
Davis Arbovirus Research Unit, Center for Vector Borne Disease Research, School of
Veterinary Medicine at the University of California, Davis. This unit analyzes all WNV samples for the State of California and has the equipment and expertise for assessing
WNV exposure. WNV may result in high, acute mortality for exposed individuals and mortality may differ for different life stages (e.g., adults versus nestling/fledglings).
Non-juvenile CSOs have high survival rates that exhibit low variation over time (Franklin et al. 2004). Given low temporal variation, changes in survival rates should be apparent if WNV has a significant effect detected through monitoring of survival rates of territorial birds (Question #2).
Pending additional funding, annual monitoring of mosquitoes will be conducted to determine the distribution and prevalence of WNV across the project area. Mosquitoes will be sampled at both CSO sites and across the study area using a probability based sampling frame to assess risk of exposure at CSO nest/roost sites and to develop a model to predict the distribution and abundance of WNV in the study area. Understanding the distribution and dynamics of WNV across space and time will have important ramifications for CSOs, as well as, be of broad significance to humans and a multitude of wildlife species susceptible to the disease.
Quality Assurance and Control
All field personnel attend standardized training at the start of each field season and are trained on data collection methods, record keeping, daily data filing. Field project leaders review all data sheets for completeness before data sheets are electronically entered and filed. Field project leaders immediately consult with data collectors to resolve any discrepancies located on data sheets.
Data Management and Archiving
Data are electronically entered into an Access database by a subset of trained field personnel. The electronic database and hard copies are reviewed by our data manager to check for accuracy and completeness. Thus, field sheets receive 1 review and the electronic database receives 1 review following data collection and data entry. Hard copies of original data sheets are archived in 2 locations: (1) the Sierra Nevada Research
Center in Davis California; and (2) the Sierra Nevada Research Center field office in
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7 September 2004
Quincy, California. Electronic copies of the database are archived on: (1) a computer and server at the Fresno office; (2) a computer at the Dinkey Creek Field Station; and (3) on CDs at the homes of two principal investigators.
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Table 1. Comparison of silviculture prescriptions proposed for treating Protected Activity
Centers (PACs) of California spotted owls in the King’s River Project and under the current (2001) and supplemental (2004) Records of Decision for the Sierra Nevada
Framework Plan Amendment.
Current ROD 2001
PAC’s treated – No more than 5% in the first year and
10% per decade.
DEFENSE ZONE
PAC’s in defense zone
Mechanical treatment
In all CWHR classes:
20 inch diameter limit
Retain 50% canopy cover
(CC)
No reduction of CC in stand with 40 to 50 % CC except for tree < 6” DBH.
Max. of 20% reduction in CC
Leave 25% of each stand untreated
500 ft. buffer around roost/nest hand piling fuels.
Underburn PAC
Supplemental ROD 2004 King’s River Project
PAC’s treated – No more Treat all PAC’s within than 5% of the Area of the
PAC’s treated in the first year and 10% of the Area
KRP the same to have a standardized treatment for future analysis. (sample size). of PAC’s in a decade
DEFENSE ZONE
PAC’s in defense zone
Use the Current ROD
Defense zone prescription.
Mechanical treatment
Modified CASPO
CWHR classes 4M, 5M,
5D, & 6
30 inch diameter limit
Retain 40% CC – Goal @
50% CC
Mechanical treatment in all CWHR classes:
20 inch diameter limit
Retain 50% canopy cover
Max of 30% reduction in
CC
Retain 40% of Basel Area
(BA) in larger trees
Retain 5% to 20% CC in lower layer of trees 6 to 24 inch DBH
Other CWHR classes
30 inch diameter limit
Retain 40% of BA in larger trees
Gaps, all CWHR classed
30 inch diameter limit
5% of stand per decade size ½ to 2 acres
Underburn PAC
(CC)
No reduction of CC in stand with 40 to 50 % CC except for tree < 6” DBH.
Max. of 20% reduction in
CC
Leave 25% of each stand mechanically untreated
500 ft. buffer around roost/nest hand piling fuels.
Underburn PAC
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7 September 2004
Figure 1. King’s River Project area on the Sierra National Forest, California Project area encompasses approximately 131,500 acres in the Dinkey and Big Creek watersheds.
Approximately 72,000 acres is scheduled for treatment between 2004-2033. Examples of
California spotted owl territory locations and 1000-acre home range polygons are illustrated. See text for further details.
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7 September 2004
Figure 2. Distribution of California spotted owl territories in the King’s River Project
Area through 2002. Dates indicate the last year a site was known to be occupied by
California spotted owls.
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7 September 2004
Figure 3. Distribution of California spotted owl territories relative to the proposed treatment units scheduled for implementation in 2004-2008 in the King’s River Project,
Sierra National Forest, California.
25