Vandenberg Air Force Base: Final Version - Point Blue · distribution and population trends on...

Vandenberg Air Force Base: Recommendations for an Upland Bird Monitoring Program

Final Version December 17, 2012

L. Jay Roberts and Geoffrey R Geupel

PRBO Conservation Science 3820 Cypress Drive #11, Petaluma, CA 94954

www.prbo.org

PRBO Contribution #1910

http://www.prbo.org/

Upland Bird Monitoring at VAFB

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Table of Contents Acknowledgements ......................................................................................................................... 3 I. Introduction ................................................................................................................................. 4

Background ................................................................................................................................. 4 Monitoring Objective .................................................................................................................. 4

II. Sample Design and Analyses ..................................................................................................... 5 Influence of Sample Design on Analyses: Occupancy and Detectability ................................... 9 Influence of Sample Design on Analyses: Scale ......................................................................... 9 Sample Size and Effort .............................................................................................................. 10

III. Field Methods ......................................................................................................................... 10 Details of Point Count Methods ................................................................................................ 10 Field Technician Training ........................................................................................................ 11 Vegetation Surveys .................................................................................................................... 11 Data Management ..................................................................................................................... 11

IV. Data Analyses and Reporting ................................................................................................. 13 Occupancy Analyses ................................................................................................................. 13

Literature Cited ............................................................................................................................. 15 Tables and Figures ........................................................................................................................ 20

Table 1 – distribution of habitats across VAFB........................................................................ 20 Table 2 – sample transect locations.......................................................................................... 22 Figure 1 – Point count spatial arrangement............................................................................. 24 Figure 2 – Transect locations on VAFB ................................................................................... 25

Appendix A: Point count survey – Standard Operating Procedure .............................................. 26 Appendix B: Point count location vegetation survey – Standard Operating Procedure ............... 31 Appendix C: List of sensitive species targeted by this monitoring program ................................ 37


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Acknowledgements We thank Lauren Wilson and the Vandenberg AFB Natural Resource staff for extensive guidance and feedback during the production of this report. We also thank Nat Seavy, Morgan Ball, and Dan Robinette for reviewing earlier versions and for providing numerous helpful insights.


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I. Introduction Background

This report presents a plan for long-term monitoring and evaluation of upland bird distribution and population trends on Vandenberg Air Force Base (VAFB) in Santa Barbara County, CA. This plan draws on the 30 years of experience PRBO Conservation Science has in monitoring and analysis approaches for landbirds (e.g. Geupel et al 2012, Roberts et al 2011, Meese et al 2009, Nur et al 1999, Ralph et al 1993). Key components of this monitoring plan, reflecting the state of the science in wildlife monitoring, are spatially balanced random distribution of survey locations, sampling habitats at equal probability so that the measurements are representative of the study area, explicit incorporation of analysis methods into monitoring program design, flexible spatial representation of sampling units to account for differences in species territory size, and a balance between efficiency of data collection, sample size and spatial representation.

Monitoring Objective

Using recommendations on setting objectives form the US North American Bird Conservation Monitoring Subcommittee (2007), PRBO and VAFB Natural Resource staff identified the following objectives for a base-wide upland bird monitoring program:

1. Identify yearly locations and distribution of Nature Serve S3-ranked species of concern

that use scrubland, grassland, or woodland vegetation – for a list of these species see Appendix C;

2. Provide a baseline of detections for evaluating and selecting new species of special concern that occur on upland habitat on Vandenberg Air Force Base, especially those that may be declining elsewhere or that may acquire regulatory protection in the future;

3. Assess the health of upland bird populations and habitats by tracking trends in abundance and occupancy of upland bird species, including California Partners in Flight Coastal Scrub (CalPIF 2004) and Grassland (CalPIF 2000) Bird Conservation Plan species and the U.S. Fish and Wildlife Service surrogate species;

4. Determine the effects of management activities on upland birds and habitats, specifically invasive species control projects (Pampas Grass Removal), habitat restoration projects (Eucalyptus removal, endangered butterfly management), fuels management (Ten Year Burn Plan and fire history relationships), and outdoor recreation (hunting);

5. Evaluate covariates and threats or causes that may require more intensive monitoring or management actions such as development or military mission activities, grazing and agricultural land use, climate change, and invasive plant species encroachment; and

6. Compare VAFB point count data with data from other local, regional, and state-wide bird monitoring programs to assist with assessment of the status of VAFB populations.

The monitoring program described below, in concert with other VAFB targeted monitoring efforts, will aid VAFB Natural Resource staff in meeting these objectives. This program is designed using standardized and repeatable protocols that track the abundance and


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distribution of multiple species, including targeted species of special concern, focal species (Chase and Geupel 2005), and surrogate or multi-species indices (Wiens et al. 2008) that may inform and advance the management of wildlife habitat across VAFB and allow comparisons with other sites throughout Western North America. Fulfillment of many of the objectives listed above depends largely on establishing a baseline for current conditions and tracking trends and changes over time to evaluate future conditions and inform management practices that may influence species presence and abundance. In the sections that follow, necessary data and potential analyses are laid out that can inform the adaptive management of wildlife habitat on VAFB.

The mechanism by which to achieve the objectives listed above is to monitor the distribution and trends or changes in species presence or abundance over both time and space for as many species as possible and then evaluate these with respect to changes in habitat conditions and populations of other avian species occupying the same survey locations. We believe a multiple species approach as outlined by California Partner’s in Flight Coastal Scrub (CalPIF 2004) and Grassland (CalPIF 2000) Bird Conservation Plans, and more recently by the United States Fish and Wildlife Service surrogate species program (USFWS 2012), will provide greater insight into the effects of management actions and other disturbances (Alexander et al. 2007, Stephens et al. 2011). A carefully designed sampling strategy using repeated surveys at consistent locations can be used to maximize the sample size and minimize variability and spatial bias (Theobald et al. 2007). With this approach it will be possible to infer likely causes of bird population and community changes over time through correlations with natural disturbances, habitat succession and alteration, climate change, interspecific interactions, and management or other human activities. II. Sample Design and Analyses Monitoring Overview and Objectives

The VAFB upland bird monitoring program is targeted towards two primary goals. First, a baseline sample that can be used to detect long-term population trends in abundance, species richness, and occupancy at the base-wide scale should be established. The target for these data will be to help determine the direction and magnitude of population change for as many avian species as possible, specifically those species that are likely to be detected through standardized passive auditory and visual variable circular-radius point counts (Ballard et al. 2003; Ralph et al. 1995). On point counts, a single observer estimates the distance to the location of each individual bird they detect within a five minute time span from a fixed location. Species that are not reliably sampled with this method include those species that are secretive, nocturnal, and/or non-territorial; species with very large territories are also not reliably detected during point count surveys even when they do inhabit the surveyed area.

The second goal will be to adapt the monitoring program to inform specific management targets such as effects of cantonment area and infrastructure development, military rocket and missile launches and other military-mission related activities, vegetation management (including


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invasive plant species control), prescribed burning and fire history relationships, grazing and agricultural practices, and habitat restoration projects focused towards populations of listed species (e.g. endangered El Segundo blue butterfly or Lompoc yerba santa and threatened vernal pool fairy shrimp). This component of the monitoring program will entail a set of survey locations targeted at locations of particular interest due either to the presence of species of concern or to management activities. The selection of these monitoring locations can take place on an ‘as needed’ basis, and can be completed at the expense of visiting the static locations.

Data on avian population trends at VAFB can be used in comparison to local (e.g. county), regional (e.g. Central Coast BCR, California LCC) and continental scale patterns to provide context and assist in identifying causes of change and appropriate management solutions. We feel it is important to take a multiple species approach rather than focus on individual species of special concern for two main reasons. First, species of special concern tend to be rare or sparsely distributed and therefore difficult and costly to monitor. Second, monitoring metrics that incorporate multiple species may be more robust to inter-annual variation and representative of particular aspects of ecosystem health than are metrics based on single species (Wiens et al. 2008).

Target analyses linked to the first goal (building a baseline dataset of species occurrences to examine long-term population trends) will include estimates of abundance, species richness, and distribution while accounting for variable detectability. The class of statistical models that incorporate detectability as an explicit component are referred to as occupancy models (MacKenzie et al. 2006). Occupancy is a particularly appropriate metric for tracking species distributions in multiple species monitoring programs since detectability varies widely between species, and explicitly accounting for these sampling errors makes it possible to directly compare measurements between species (Kery and Schmidt 2008).

The primary analyses of this project will be to establish population abundance, distribution (using occupancy methods), and habitat associations for a variety of species of concern, specifically species that have a Heritage Status Rank of G1-G3, N1-N3, and S1-S3 (www.natureserve.org). Air Force Instruction 32-7064 allows ecosystem and sensitive species management based on these ranking criteria. Primary targets include a portion of those listed on Species List (INRMP 2011, Tab D Appendix A) such as Mountain plover (N3S2, Charadrius montanus), Burrowing Owl (N4S2, Athene cunicularia hypugea), Vaux’s swift (N5S3, Chaetura vauxi), Costa’s hummingbird (N4S3, Calypte costae), Allen’s hummingbird (N5S?, Selasphorus sasin), Nuttall’s woodpecker (N5S?, Picoides nuttallii), Olive-sided flycatcher (N4S4, Contopus cooperi), Loggerhead Shrike (N4S4, Lanius ludovicianus), Oak titmouse (N5S3, Baeolophus inornatus), Grasshopper Sparrow (N5S2, Ammodramus savannarum), Bell’s Sage Sparrow (N2S2, Amphispiza belli belli), Belding’s savannah sparrow (N3S3, Passerculus sandwichensis beldingi), Black-chinned sparrow (N5S3, Spizella atrogularis), and Tricolored blackbird (N2S2, Agelaius tricolor), as well as more than 50 other more common species including California Quail, a game species. See Appendix C for complete list of Vandenberg’s Special-Status species and reason for not including other species as primary monitoring targets. Some Endangered

http://www.natureserve.org/


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Species Act listed species or otherwise high profile species (e.g. Southwestern Willow Flycatcher, Western Snowy Plover, Ashy Storm Petrel, and California Least Tern) that require targeted species-specific protocols will not be adequately covered by the implementation of this monitoring program, but are extensively managed by other long-term recurring VAFB projects (i.e. XUMUOS1009xx, XUMUOS1023xx, XUMUOS1008xx, XUMUOS1018xx, XUMUOS1013xx, XUMUOS1017xx).

Sample Design

There are a number of logistical considerations that influence the sampling design of this monitoring project, the most evident of which is the need to maximize statistical precision and spatial representation, while at the same time minimizing the cost to conduct the samples (including the number of sites, the amount of time required to navigate to each location, and the time and effort required to complete each survey). A good design attempts to maximize the ability to make inferences for an entire area while minimizing sampling effort. A design that includes too few sample locations will not give adequate power to identify an important change in a population whereas too many samples results in inefficient use of time and money.

The preferred field method is to conduct variable circular radius point counts at locations selected with a spatially balanced Generalized Random-tessellation Stratification (GRTS) procedure to ensure a balanced random distribution of survey locations (MacDonald 2003, Stevens and Olsen 2003, 2004). The tesselation grid is the Military Grid Reference System (MGRS) at 1km2 (100 hectare, 247 acre) resolution; all GIS analyses were conducted with ArcGIS 9 (ESRI 2006). The field sites are arranged in transects of five point count locations (Figure 1), with one point at the center and four others at 300m intervals in the cardinal directions. All five points correspond to MGRS 100m resolution points and follow those naming conventions to allow easy referencing of locations. This points-within-transects arrangement allows for flexible (two level hierarchical) definition of sampling units to account for differences in species territory sizes. For example, species with small territories <1ha (sparrows and songbirds) will be analyzed at the point scale, while other species with large territories (e.g. quail, some corvids, and raptors) may be more appropriately addressed using transects (representing 1km MRGS grid cells) as sample units. GRTS is an efficient design for monitoring programs aimed at identifying trends of species with widely differing population size and distribution ( Carlson & Schmiegelow 2002, Theobald et al. 2007, White et al. 2012).

When it is not possible to navigate to designated field point count locations due to access restrictions or impassable barriers such as impenetrable shrub fields, steep slopes, deep rivers, or any other dangerous landscape features, then alternate locations can be chosen manually using the following criteria: 1) new point must be within the same 1km MGRS grid cell, 2) new point should be at least 200m from any adjacent point count locations, 3) new point should be within similar habitat as the original point. Utilizing trails and roadsides is encouraged in logistically difficult areas if it significantly reduces the total time to navigate to all five points in the transect. If all five points within a transect are relocated, care should be take to ensure that the entire 1km2


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grid cell is represented and thus the five point locations are not clustered in one portion of the cell. If any entire transects listed in Table 2 are not logistically feasible or occur on restricted areas, they should be replaced with a manually-selected adjacent location that minimizes the difficulty in accessing the transect, for example the adjacent MGRS grid cell that is closest to a road or trail (this process should only be used if absolutely necessary).

Across the approximately 100,000 acre VAFB boundary the sampling frame includes coastal scrub, grassland, and other upland habitats that are known to be important wildlife habitat in coastal California (Bunn et al. 2007), while avoiding dunes, lowland, barren, and developed lands (Table 1). Riparian and beach/dune habitats were not included as they are the focus of other bird monitoring programs (DiGaudio et al. 2011). The sampling universe for this project was defined based on an overlay of MGRS 1km grid cells on the VAFB boundary keeping only those cells with a minimum of 0.75km2 of area within the base, and only those cells with at least 75% cover of target habitats. This area is represented by approximately 319 km2 depicted as crosshatched cells in Figure 2. The GRTS algorithm was used to select survey locations with equal weight across the entire target area, resulting in the placement of survey locations in a spatially balanced and random distribution proportional to the amount and spatial distribution of suitable area for sampling. The selection of points in proportion to the distribution of target habitats across the study area is necessary to build a sample of the avian community that is representative of the entire VAFB without need to adjust count numbers based on the amount of area that each point represents (Nur et al. 1999).

Assuming approximately 18 person-weeks of total field effort (2 observers counting simultaneously for 9 weeks at 5 point count days per week), and each observer can complete two transects per morning, up to 60 transects can be visited three times per field season. Transects consist of 5 point-count locations within a 1km2 grid cell (Figure 1). Sixty transects represents a sampling rate of roughly 19% at the 1km scale. Most avian species have an effective detection radius of less than 100m (Alldredge et al. 2008), so if each point represents a sample of a 100m radius area (7.8 acres) then at the point scale sample rate is roughly 3.1% of the total area.

At any point following the establishment of this project, it may be desirable to allocate some monitoring effort to additional targeted areas to fulfill other monitoring objectives. At that time it will be necessary to develop a strategy for altering effort based on yearly funding levels and the identified additional monitoring needs. GRTS samples are flexible in that different portions of the survey locations can be surveyed in different years, as long as ordered lists of locations are maintained (Table 2). VAFB Resources Staff can therefore determine the proportion of the VAFB-wide sample to repeat each year, or the number of revisits to each transect, while diverting effort to targeted sampling locations and still maintain the overall balance of the sample (Stevens & Olsen 2003) and represent the whole community while maintaining an adequate sample of as many species as possible (MacKenzie and Royle 2005). The details regarding best approach to panelizing the site visit history can be finalized following the first two to three field seasons and will depend largely on target metrics and amount of variability in the sample dataset (MacKenzie and Royle 2005). We suggest one method of


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conducting a panelized sample by using three groups of 20 transects, the first group surveyed only in odd years, the second group in all years, and the third group in even years (Table 2). Field efforts can be further reduced by conducting only two repeat samples at each transect. Influence of Sample Design on Analyses: Occupancy and Detectability

The detectability (probability of recording the presence of a species when it is in fact present at a location) of targeted species is a key measure for converting occurrence records into indices of abundance, occupancy, distribution, habitat associations, and other metrics used to glean information from monitoring data (Nichols et al. 2008). For species with low detectability, it is difficult to establish whether a non-detection at a survey location is because the bird is actually absent or just not detected (Kery & Schmidt 2008). High confidence is possible, therefore, in population and community indices only for species with high and uniform (across space and time) detectability.

Occupancy analysis relies on repeated surveys over time at the same locations to establish more reliable estimates of presence vs. absence and to calculate detectability. For species with low detectability a large number of repeated surveys within a season may be required to establish its presence at a survey location (Kery & Schmidt 2008; Royle et al. 2005). However, the total sample size is reduced with each repeat. A balance can be met between the need to cover more area with the need for many repeat surveys (MacKenzie and Royle 2005); we recommend that each point count location be visited three times per season. Robust occupancy estimates can be calculated with less than three visits per year for species with relatively high detectability, and thus the decision whether to reduce the number of visits to allocate field effort towards other goals can be determined after two to three years of data are generated and the amount of variability and probability of detection can be assessed for species of interest.

Influence of Sample Design on Analyses: Scale

Species’ home range size influences the interpretation of occurrence estimates (regardless of whether abundance, occupancy, or another state variable is used) calculated from point count records (Royle et al. 2007). This transect design (Figure 1) was purposely built to be flexible for analyzing species occurrence records of many species where both detectability and home range size may vary widely. Many small passerine territories are approximately 1 ha (about a 57 m radius circular area) and thus adjacent point counts are unlikely to survey the same territory, but individual points have the potential to survey multiple territories. Larger species including woodpeckers, corvids, and quail have territories up to 50 ha or larger and thus adjacent points have the potential to survey a single territory. This fact influenced the transect design, where each point within the transect samples about 3-30 ha (or a 100-300 m radius) depending on the effective detection distances of the species recorded, while aggregated counts from all five points within a transect are representative of a much larger area (up to 1 km2, or 100 ha). Flexibility, therefore, exists to consider each point count an individual sampling unit for species with smaller


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territories, while the entire transect can be treated as a sampling unit (by lumping all the point count records together) for species with large territories such as California Quail. Spatial dependency factors should be included in any models assuming independence of sampling units to account for spatial autocorrelation.

Sample Size and Effort

In choosing the number of survey locations to visit, tradeoffs between power (probability of detecting a real trend), spatial coverage, sample design issues (e.g. randomization of survey plots, minimizing bias, maximizing detection probability), effort, number of revisit surveys, and travel time between survey locations and other logistical constraints need to be assessed. Power is often of secondary importance compared to adequate spatial coverage and careful sample design to maximize the precision of parameter estimates (Seavy & Reynolds 2007), nevertheless it is desirable to have high confidence in trend estimates. With the full sample of 60 transects (300 points), a minimum of 90% power to detect a 3% annual trend in occupancy over 5 years should be achieved for all species with more than approximately 10 detections per year (assuming α = 0.2, 2-tailed test, exponential growth model, and two counts per point per year – see Figure 3 in USGS Patuxent Wildlife Research Center Manager’s Monitoring Manual [http://www.pwrc.usgs.gov/monmanual/samplesize.htm]). For subsets of 12 transects (60 points), for example, only relatively common species with a 50% coefficient of variation will achieve a 90% power to detect a 3% yearly trend over five years; whereas most species will have higher variation and thus reveal lower power (Purcell et al. 2005). These numbers are coarse estimates and only after 3-5 years of survey data are available will it be possible to calculate coefficient of variation values for each species of interest.

III. Field Methods Details of Point Count Methods

A standardized point count method should be employed to survey the diurnal upland bird community. A complete description of the point count methodology is provided in Appendix A. Point counts should consist of (at least) five minute variable circular radius plot (VCP) surveys (Ballard et al. 2003; Ralph et al. 1995), estimating the exact distance (to nearest 1m) to each detection. Five minute surveys are common across Western avian monitoring projects (Siegel et al. 2007; Stine et al. 2005) and provide a compromise between the need to detect species that vocalize less frequently (rationale for a longer count, Barker et al. 1993) with the need to survey more sites (Ralph et al. 1993). Estimating exact distance to each individual bird detected allows for estimation of detection probability functions (Buckland 2001) while also allowing for the greatest data processing and analysis options including binning of data for comparison with other monitoring programs.

The appropriate window for point count surveys in central California coastal areas extends from April through June. This period coincides with the arrival of the latest arriving Neotropical migrants and the time when numerous species reduce their singing rates in mid-

http://www.pwrc.usgs.gov/monmanual/samplesize.htm


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summer. Point count surveys begin after sunrise and are to be completed within four hours. Point counts are not to be conducted in adverse weather conditions including high winds, fog, snow, and rain when bird activity levels and detection probabilities are substantially reduced (Ralph et al. 1993). Weather data should be recorded throughout the morning and if conditions are questionable observers are advised to stop surveys.

Transects should be visited in a manner that randomizes the dates and times at which each point is surveyed. For example, do not conduct all three visits to one set of transects in the early spring and all visits to another set of transects in late spring. Transects may be paired, or aggregated in any other way that eases the logistics, and visited on the same day as long as the repeated visits to those locations are spread out across the survey season. The order at which points within transects are counted is not important, but should again be randomized or rotated on a per visit basis – e.g. if the first visit the points are counted in the order N, E, S, C, W, then the next visit could be S, C, W, N, E. Field Technician Training

Observer variability can greatly influence the integrity of point count data and success of monitoring programs (Sauer et al. 1994), thus it is critical to hire experienced technicians and then put them through a rigorous training program (Walker 2005). Field technicians who conduct point counts should have previous experience identifying western landbirds and/or experience using variable circular plot point count methods. All new field technicians will need to be trained and calibrated in distance estimation by qualified personnel.

Vegetation Surveys Vegetation surveys using a relevé-based protocol should be conducted during the field season. Measurements and estimates of vegetation cover should be collected within a 50m radius circle (1.94 acres, 0.785 hectares) centered on each point count station. Most relevé vegetation measurements rely on visual estimates of cover and rapid acquisition of diameter, height, and basal area measurements using forestry tools. Useful measurements include plant cover in multiple vertical layers; height, diameter, and cover of tree species within the overstory layer; ground cover; basal area; habitat type; snag counts; coarse woody debris assessment; and other measurements (see Sawyer et al. 2009, CNPS 2011). A suggested vegetation survey protocol and field data form is listed in Appendix B. Project leaders should examine the protocol and adjust as necessary to ensure that features of interest (e.g. prescribed fire rotations, grazing, invasive plant management) receive appropriate focus and additional variables to account for these could be added to the survey form. Vegetation surveys should be repeated every 3-5 years at each point count location, or immediately following a major change in vegetation structure due to management or other disturbance. These surveys will also be useful in ground-truthing the GIS habitat classification at each survey location. Data Management


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All data must be entered in a timely manner by the observers (typically within a few days of collection). We recommend uploading point count and vegetation survey data multiple times per week into an online (free) data entry tool through the California Avian Data Center (CADC) as part of the Avian Knowledge Network (AKN) where databases are backed up frequently and reliably (Ballard et al. 2008). Before uploading data, technicians are required to proof their data by comparing raw data to what was entered in the database. At the end of each field season technicians extensively re-proof all field surveys and correct mistakes. All raw data sheets should also be scanned and digital copies stored on regularly backed up servers accessibly to VAFB Natural Resource staff.


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IV. Data Analyses and Reporting Occupancy Analyses It is valuable in a monitoring project such as this one to adjust species occurrence records to remove biases inherent in point count data (Seoane et al. 2005). Without adjusting for detectability and other factors, occurrence data are still useful as indices of abundance and density, but must be considered in the proper context or they can be misleading (Johnson 2008). In particular, ignoring inter-specific differences in detectability can lead to underestimating abundance for inconspicuous species and makes comparisons across species difficult (Kery & Schmid 2004).

There are a number of methods for adjusting point count data to reveal more realistic estimates of occurrence, and these indices are generally based on corrections using distance to recorded individuals (Buckland 2001), multiple observers (Alldredge et al. 2006), or repeated samples at the same locations (Royle et al. 2005). Occupancy analysis (MacKenzie et al. 2006) is a robust analytical method for monitoring data that relies on repeated sampling at the same locations. Standard occupancy (MacKenzie et al. 2002) minimizes the reliance on accurate distance estimates which can be difficult to collect in densely vegetated habitats (Alldredge et al. 2007). Distance estimates can be used to select a cutoff for each species at maximum effective detection distance where it is probable that other individuals are not being detected. Occupancy estimates should be relied on to account for variable detectability, but both standard occupancy and abundance (Royle and Nichols 2003) estimates should be reported in comparison to prevalence and abundance indices that are not corrected for detectability (Johnson 2008).

Occupancy modeling is a method for estimating the probability of a species’ presence at a site when the probability of detection of that species is less than 1 (which it nearly always is). Covariates including site conditions (habitat type, slope, elevation, aspect) and survey variables (observer, weather conditions, time of day, day of year) can be included in occupancy models as these factors can influence both detectability and occupancy. There are a variety of occupancy model formulations available, and the variety is expanding (Nichols et al. 2008). Initial analyses should rely on single-season standard occupancy models to create yearly occupancy estimates that can then be analyzed using standard trend regression techniques (Nur et al. 1999). Additional occupancy formulations such as multi-season models to examine site-level colonization and extinction, or site-level abundance estimates rather than probability of presence (traditional occupancy) can be utilized to approach specific management questions as necessary.

The interpretation of occupancy parameter values depends on the scale at which point count records are aggregated. The biological meaning of occupancy at the point count scale (treating each point count location as an independent sample unit) is similar to an individual-level population parameter, in other words the probability of encountering an individual at that location. Given certain restrictions, e.g. when the effective area surveyed is approximately the same as that of an average individual territory, occupancy will very closely track changes in abundance (MacKenzie & Nichols 2004). Alternatively point count records can be lumped at the transect level, in which case the biological meaning of the occupancy parameter is akin to a


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population-level distribution measure, or the proportion of the sampled area occupied. And when abundance is incorporated explicitly into the occupancy estimate, the occupancy model can be extrapolated across the entire study area to generate base-wide population size estimates.


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McDonald, T. L. 2003. GRTS for the Average Joe: A GRTS Sampler for Windows. www.west-inc.com/reports/grts.pdf. WEST, Inc., Cheyenne, WY.

Meese, R.J., G.R. Geupel, K. Reeves, and J. Weigand. 2009. Scientific Peer Review and Assessment of the California Department of Parks and Recreation Off-highway Motor Vehicle Recreation Division Habitat Monitoring System. Final Report. Submitted to California Department of Parks and Recreation, Off-highway Motor Vehicle Recreation Division, Sacramento, CA. 46 pp. (http://carnegiegeneralplan.com/document-library).

Nichols, J. D., L. Thomas, and P. B. Conn. 2008. Inferences About Landbird Abundance from Count Data: Recent Advances and Future Directions. Pages 201-235 in D. L. Thompson, editor. Modeling Demographic Processes in Marked Populations. Springer Science.

Nur N., S. Jones, and G. R. Geupel. 1999. A statistical guide to data analysis of avian monitoring programs. US Department of the Interior, Fish and Wildlife Service, Biological Technical Publication BTP-R6001-1999, Washington, D.C.

Purcell, K. L., S. R. Mori, and M. K. Chase. 2005. Design considerations for examining trends in avian abundance using point counts: Examples from oak woodlands. Condor, 107, 305-320.

Ralph, C. J., G. R. Geupel, P. Pyle, T. E. Martin, and D. F. DeSante 1993. Handbook of field methods for monitoring landbirds. USDA Forest Service Publication, PSW-GTR 144, Albany, CA. 41p.

Ralph, C. J., J. R. Sauer, and S. Droege, editors. 1995. Monitoring bird populations by point counts. PSW-GTR-149. USDA Forest Service, Pacific Southwest Research Station, Albany, CA.

Roberts, L. J., R. D. Burnett, A. M. Fogg and, G. R. Geupel. 2011. PRBO MIS Final Study Plan and Sampling Protocols for Mountain Quail, Hairy Woodpecker, Fox Sparrow and Yellow Warbler. January 2011. PRBO Contribution #1714.

Royle, J. A., M. Kery, R. Gautier, and H. Schmid. 2007. Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecological Monographs 77:465-481.

Royle, J. A., J. D. Nichols, and M. Kery. 2005. Modelling occurrence and abundance of species when detection is imperfect. Oikos 110:353-359.

Royle, J. A., and J. D. Nichols. 2003. Estimating abundance from repeated presence-absence data or point counts. Ecology, 84, 777-790.

http://www.west-inc.com/reports/grts.pdf

http://www.west-inc.com/reports/grts.pdf

http://carnegiegeneralplan.com/document-library


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Kery, M. and B. R. Schmidt. 2008. Imperfect detection and its consequences for monitoring for conservation. Community Ecology, 9, 207-216.

Sawyer, J. O., T. Keeler-Wolf, J. Evens. 2009. A manual of California vegetation. Edition 2. California Native Plant Society Press. University of Michigan.

Seavy, N. E., and M. H. Reynolds. 2007. Is statistical power to detect trends a good assessment of population monitoring? Biological Conservation 140:187-191.

Seoane, J., L. M. Carrascal, C. L. Alonso, and D. Palomino. 2005. Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecological Modelling 185:299-308.

Siegel, R. B., R. L. Wilkerson, K. J. Jenkins, R. C. I. Kuntz, J. R. Boetch, J. P. Shaberl, and P. J. Happe. 2007. Landbird Monitoring Protocol for National Parks in the North Coast and Cascades Network. Chapter 6 of Section A, Biological Science. Book 2, Collection of Environmental Data. Pages Techniques and Methods 2–A6, 200 p. U.S. Department of the Interior, U.S. Geological Survey.

Stephens, J. L., K. Kreitinger, C. J. Ralph, and M. T. Green, eds. 2011. Informing Ecosystem Management: Science and Process for Landbird Conservation in the Western United States. Biological Technical Publication FWS/BTP-R1014-2011. Portland, Oregon: U.S. Department of Interior, Fish and Wildlife Service.

Stevens, D. L., and A. R. Olsen. 2003. Variance estimation for spatially balanced samples of environmental resources. Environmetrics 14:593-610.

Theobald, D. M., D. L. Stevens, D. White, N. S. Urquhart, A. R. Olsen, and J. B. Norman. 2007. Using GIS to generate spatially balanced random survey designs for natural resource applications. Environmental Management 40:134-146.

USFWS. 2012. DRAFT Technical Guidance on Selecting Species for Design of Landscape-scale Conservation. U.S. Fish and Wildlife Service Online Resource. Access date: 09-15-2012. http://www.fws.gov/landscape-conservation/pdf/DraftTechnicalGuidanceJuly2012.pdf

U.S. North American Bird Conservation Initiative Monitoring Subcommittee. 2007. Opportunities for Improving Avian Monitoring. U.S. North American Bird Conservation Initiative Report. 50 pp. (http://www.nabci-us.org/).Walker, T. 2005. Recommendations for conducting a training program in passerine identification and distance estimation. National Park Service, Central Area Network.

White, C. M., N. J. Van Lanen, D.C. Pavlacky Jr., J. A. Blakesley, R. A. Sparks, J. A. Fogg, M. F. McLaren, J. J. Birek and D. J. Hanni. 2012. Integrated Monitoring in Bird


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Conservation Regions (IMBCR): 2011 Annual Report. Rocky Mountain Bird Observatory. Brighton, Colorado, USA. http://rmbo.org/v3/Portals/0/Documents/Science/Reports/2011_IMBCR_report.pdf

Wiens, J. A., G. D. Hayward, R. S. Holthausen, and M. J. Wisdom. 2008. Using surrogate species and groups for conservation planning and management. Bioscience 58:241-252.


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Tables and Figures Table 1 – distribution of habitats across VAFB The total area of each habitat type on VAFB. Targeted habitat types are in bold. All upland habitat types that are or could transition (given proper management) into dry forest, shrub, grass, or scrub habitats were included in the target habitat list. Habitat Type Total Acres Habitat Group Acacia 229.1 other tree Agriculture 1150.4 ag Badlands 36.3 scrub Bishop Pine Forest 1039.0 pine Central Coastal Scrub 16884.1 scrub Central Coastal Scrub - Central Dune Scrub 585.0 scrub Central Coastal Scrub - Maritime Chaparral 3227.1 scrub Central Coastal Scrub / Herb 13113.8 scrub Central Coastal Scrub / Iceplant 2675.7 scrub Central Dune Scrub 6361.0 scrub Central Dune Scrub / Iceplant 953.8 scrub Coast Live Oak Savannah 629.4 oak Coast Live Oak Woodland 5316.5 oak Hollyleaf Cherry Woodland 24.1 other tree Iceplant 905.0 scrub Iceplant - Herb 695.0 scrub Maritime Chaparral 8271.8 chaparral Maritime Chaparral - Pampas Grass 461.3 chaparral Maritime Chaparral - Venturan Coastal Sage Scrub 497.3 chaparral Maritime Chaparral / Herb 536.0 chaparral Maritime Coast Live Oak Chaparral 3426.9 chaparral Mesic Central Coastal Scrub 1084.4 scrub Native and Non-Native Herb 259.5 grass Non-Native Grasses and Forbs 14182.6 grass Non-Native Tree 2049.9 other tree Pampas Grass 140.9 grass Tan-Oak Forest 167.6 oak Veldtgrass 41.9 grass Venturan Coastal Sage Scrub 2101.1 scrub Venturan Coastal Sage Scrub / Herb 1247.0 scrub Central Coast Arroyo Willow Riparian Forest and Scrub 3896.6 riparian Coastal and Valley Freshwater Marsh 126.5 wet Coastal Bluff 216.6 dune


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Coastal Brackish Marsh 40.0 wet Coastal Salt Marsh 134.4 wet Coastal Strand 413.0 dune Cottonwood - Willow Riparian Forest 51.5 riparian Developed 3070.3 barren Disturbed / Cleared 1673.0 barren Erosional Wash 9.9 barren Foredune 673.1 dune Open Water 236.8 wet Rocky Coastline 2.3 barren Rocky Outcrop 124.1 barren Sand Dune 628.9 dune

total 99590.4

target area 88293.4

non-target area 11297.0

grass 14624.9

scrub 48269.3

chaparral 13193.3

tree 9455.6


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Table 2 – sample transect locations The transect locations with MGRS ID and center point coordinates in UTM zone 10S (WGS84 datum) are listed, along with percentage of cell area consisting of target habitat types. Each of the transects listed below consist of five point count locations (see Figure 1). A suggested plan for panelizing the transects is listed below which allows for reducing total field efforts by 1/3 each year. Alternative panelization methods can take a variety of formats, while still maintaining the overall spatial balance of the sample, as long as an ordered list of sites (using GRTS_ID) is visited each year (e.g. transects 1-30 one year and 31-60 the following year, but never 1-20 and 41-60 in the same year).

GRTS_ID MGRS1km_ID X-

coordinate Y-

coordinate % cell area in

target habitats total area

(sq. meters) yearly panel

groups 1 10SGD2534 725500 3834500 97.6 1000000 odd years 2 10SGD2152 721500 3852500 91.8 1000000 odd years 3 10SGD2131 721500 3831500 96.3 1000000 odd years 4 10SGD3050 730500 3850500 82.8 1000000 odd years 5 10SGD2827 728500 3827500 97.6 999400 odd years 6 10SGD2244 722500 3844500 76.4 1000000 odd years 7 10SGD1958 719500 3858500 82.7 1000000 odd years 8 10SGD2133 721500 3833500 93.2 1000000 odd years 9 10SGD3248 732500 3848500 100 856600 odd years

10 10SGD2654 726500 3854500 99.6 1000000 odd years 11 10SGD2234 722500 3834500 95.7 1000000 odd years 12 10SGD2057 720500 3857500 91.0 1000000 odd years 13 10SGD2345 723500 3845500 89.8 1000000 odd years 14 10SGD2750 727500 3850500 93.8 1000000 odd years 15 10SGD1847 718500 3847500 96.3 1000000 odd years 16 10SGD2353 723500 3853500 97.9 1000000 odd years 17 10SGD2130 721500 3830500 97.6 1000000 odd years 18 10SGD2856 728500 3856500 88.6 1000000 odd years 19 10SGD2251 722500 3851500 89.0 1000000 odd years 20 10SGD1930 719500 3830500 91.8 1000000 odd years 21 10SGD2137 721500 3837500 100 1000000 all years 22 10SGD2441 724500 3841500 100 1000000 all years 23 10SGD3351 733500 3851500 100 1000000 all years 24 10SGD2724 727500 3824500 95.8 1000000 all years 25 10SGD1948 719500 3848500 76.3 1000000 all years 26 10SGD1946 719500 3846500 98.3 1000000 all years 27 10SGD2061 720500 3861500 100 990400 all years 28 10SGD3251 732500 3851500 98.3 1000000 all years


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29 10SGD3055 730500 3855500 99.4 1000000 all years 30 10SGD2243 722500 3843500 89.1 1000000 all years 31 10SGD2454 724500 3854500 95.7 1000000 all years 32 10SGD2849 728500 3849500 100 1000000 all years 33 10SGD2254 722500 3854500 81.9 1000000 all years 34 10SGD2232 722500 3832500 100 1000000 all years 35 10SGD2428 724500 3828500 100 1000000 all years 36 10SGD2144 721500 3844500 91.0 1000000 all years 37 10SGD2925 729500 3825500 100 1000000 all years 38 10SGD2342 723500 3842500 93.4 1000000 all years 39 10SGD1944 719500 3844500 99.9 968700 all years 40 10SGD2355 723500 3855500 96.4 1000000 all years 41 10SGD2431 724500 3831500 90.4 1000000 even years 42 10SGD2945 729500 3845500 97.1 1000000 even years 43 10SGD2656 726500 3856500 98.1 1000000 even years 44 10SGD1933 719500 3833500 95.2 1000000 even years 45 10SGD2448 724500 3848500 77.6 1000000 even years 46 10SGD2753 727500 3853500 84.9 1000000 even years 47 10SGD1835 718500 3835500 93.4 1000000 even years 48 10SGD2641 726500 3841500 97.4 1000000 even years 49 10SGD1733 717500 3833500 96.8 944900 even years 50 10SGD2449 724500 3849500 92.5 1000000 even years 51 10SGD2744 727500 3844500 98.3 1000000 even years 52 10SGD2542 725500 3842500 85.1 1000000 even years 53 10SGD2427 724500 3827500 92.3 1000000 even years 54 10SGD2336 723500 3836500 98.4 1000000 even years 55 10SGD2136 721500 3836500 92.8 1000000 even years 56 10SGD2923 729500 3823500 100 1000000 even years 57 10SGD1932 719500 3832500 100 1000000 even years 58 10SGD2129 721500 3829500 100 1000000 even years 59 10SGD2450 724500 3850500 86.5 1000000 even years 60 10SGD1629 716500 3829500 87.0 969800 even years


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Figure 1 – Point count spatial arrangement The spatial arrangement of point count transects is shown below. Transects consist of a center point, and four perimeter points spaced at 300m intervals from the center point. Center points are at the center of the MGRS grid cell, while outer points are 200m from the edge. Point ID names correspond to the cardinal direction in relation to the center point (“C”). In some cases it may be necessary to adjust the location of points to avoid logistically unfeasible terrain; adjusted points should be located as close as possible to those they replace and in all cases should fall within the original 1km MGRS grid cell. 5-minute passive point counts are conducted at all five locations.

“W”

“N”

“C” “E”

“S”


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Figure 2 – Transect locations on VAFB The point locations are depicted by green dots, and potential sampling locations (MGRS 1km grid cells) are shown as crosshatched squares overlaying the VAFB boundary. Target habitats are shown as light gray and non-target areas (largely developed, riparian, and unvegetated sand habitats) as black.

20km


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Appendix A: Point count survey – Standard Operating Procedure Part 1: Schedules and point count field procedures Daily Schedule At least two transects should be visited per field day. Field techs should begin their first survey near local sunrise and complete it within 3-4 hours, generally by 0930 and never later than 1000. GPS units can be used to check on the local sunrise time. For subsequent visits to a transect, reverse the order in which point counts were conducted by changing the direction or switching which transect is done first. Observers should alternate visits so that each transect is not visited by only a single observer each year. Adverse weather conditions Do not conduct surveys during weather conditions that reduce detectability (e.g., high winds or rain). Bird detections may even diminish in fog. If it is not possible to see clearly (due to fog) 100 m away and up to the top of trees then the survey should not be done. The consistency of data collection is of first priority. If conditions change for the worse while doing a count, remaining points can be completed within 3 days. What to Bring Field techs are responsible for the following:

• binoculars • watch with countdown timer and beeping noise when time is up • bird field guide • food and water for a 5 hour survey + hike • drab and earth-toned clothing (no red clothing)

Other field equipment provided for techs: • sufficient blank data forms • clipboard • at least two black ink pens • directions and maps • GPS unit & extra batteries • Laser range finder • digital game caller • walkie-talkie or radio

Approaching the point count station Approach the point count location with as little disturbance to the birds as possible and allow the birds to settle down for a period of 1-2 minutes, during which time you can delineate distances to


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visual landmarks using laser rangefinders. Attracting devices, recordings, or “pishing” are never used BEFORE or DURING a count. Pishing while chasing unknowns after a count is ok. Duration of counts Point counts are 5 minutes duration at each point (though this may change if at monitoring coordinator’s discretion). Use countdown timers on a watch set to 5 minutes, set to beep once that time has elapsed. If something interferes with the ability to detect birds during the 5-minute count (e.g. train, plane, or chainsaws), stop the count until the disturbance has passed and start over. Part 2: Collecting bird survey data Point Count Data Form: Header and point information Use a new point count datasheet for each transect. Complete the header information on the first page of each datasheet and take care to note how many pages are included in the transect survey (e.g., “1 of 2”). Header information also includes the month, day, year and visit number (01, 02 or 03). Weather Data At the first point count location record weather data at the bottom of the datasheet. Estimate the temperature, percent cloud cover, and wind speed. Use the Beaufort scale to estimate wind speed. Any significant changes in weather (such as increasing winds, cloud cover, rain or snow) are recorded in the margins or in the “Notes” section of the datasheet. Point information and bird species codes At each point, write down the point ID name, the time in 24-hour format (eg, 0723) and for each field record, species are recorded using a 4-letter code (eg, SOSP for Song Sparrow). Codes are based on the AOU checklist of North American bird species. Techs will be provided with a copy to carry with into the field. A wavy line should be used to separate each point’s data from the next. Point Count Data Form: Bird data Every species detected at a point is recorded, regardless of distance from the observer. It is very important that all species are identified (especially those within 50 m). Field technicians should focus on identifying birds farther than 100 m once birds within 100 m are identified and counted. Closer birds should be given greater attention than distant ones as these observations are more valuable. Technicians can chase down unknown birds after the five minute count if time is available. Technicians are strictly instructed to only record bird detections with the 5-minute period and to not go “birding” after the count to include species they may have missed. These are


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set time-period point count surveys, not ‘censuses’. Technicians are encouraged to keep track of all species observed throughout the morning in their field notebooks. Unknown bird detections For unknown species heard during the count, record “XXXX.” For unknown members of various families, use “XX” plus two letters to signify the family – “XXHU” for unidentified hummingbird, for example. Technicians must take notes on identifying features of unknown bird detections. No detections If no birds are detected at a point, “No birds detected” should be written on the form, across from the time in the data fields. Detection codes Record the behavioral cue that alerted you to the presence of the individual bird:

S = song C = call V = visual D = drumming woodpecker H = humming hummingbird J = juvenile birds (birds born that year – see note below)

If a bird sings or a woodpecker drums after it has been detected via a different cue, circle the original detection (“C” or “V”). For behaviors such as wing flaps (eg, Band-tailed Pigeons), bill snaps (eg, Black Phoebe), or bark foraging (woodpeckers and nuthatches), simply use the default “C” for call. Juvenile birds Juvenile birds are recorded regardless of their behavior but are not normally included in analyses. This includes birds in the nest, fledglings following parents and birds in juvenal plumage like a spotty robin. If you see or hear a juvenile bird always record it as “J” – not by its detection cue. Distance Estimation We use a variable circular plot (VCP) and exact distance estimation up to 300 m, in which the distance to each individual detection is recorded. Record the distance from the point to the first location an individual was observed, regardless of its behavior. If the bird subsequently moves, do not change the original distance recorded. If a bird is flying (but not “flying over” – see below), or perched high in a tree, the distance recorded is to the point at which a plumb line would hit the ground if hung from the point at which the bird was first observed. Thus, there is no vertical distance included in the measurement, only horizontal. Distances beyond 300 m are


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recorded as “B300”. Distances are to be horizontal, not angular incorporating topography and tree canopy height. Writing bird data on the datasheet Bird species are written down in the order they are observed. Only one species can be listed per row but include multiple individuals of that species are fine. Record the data elements by first writing number of individuals (blank assumes only 1) followed by the type of detection, followed by the exact distance up to 300 m. For example, in a row where “SOSP” is written in the species column, the code “2V35, C110” means that there were two individuals detected visually at 35 m and another individual called at 110 m (three total individuals). Fly-overs Birds that are flying over but not using the habitat on the study area (such as Mallards) are recorded using the FLO code. Remember to put a detection cue, such as C or V, before the FLO code. Birds flying below canopy level, flying from one perch to another, or actively foraging on or above the study area (e.g., swallows) are recorded as described above with distance estimation. Birds flushed at the point A bird flushed from within 10m of the point when the technician arrives should be included in the count. Birds that are flushed from farther away should be noted in the margins of the form if they are species that didn't occur during the count. Double counting Make every effort to avoid double counting individuals detected at a single point. Always keep track of individuals during a point count. However, if an individual is known or thought to have been counted at a previous point (e.g. Red-tailed Hawk), it should still be recorded at the next point but note in the margin that it is believed to be the same bird from the previous point. Behavioral Observations Record any potential indications of breeding in the “Notes” section for each species as follows: CO-copulation FS-fecal sac DI-territorial display MC-material carry DD-distraction display NF-nest found FC-food carry PA-pair FL-fledglings CS –counter singing


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Appendix B: Point count location vegetation survey – Standard Operating Procedure Vegetation Survey Protocol (modified and copied here from the PRBO protocol which can be viewed at: http://data.prbo.org/cadc2/index.php?page=prbo-point-count-veggie-protocol). See also the CNPS/DFG rapid assessment protocol for additional instructions (http://www.cnps.org/cnps/vegetation/pdf/protocol-combined.pdf). History: This method is based on the "Estimation of Stand Characteristics" method described on page 37 of the "Handbook of Field Methods for Monitoring Landbirds" by C.J. Ralph, Geoff Geupel, Peter Pyle, Thomas Martin, and Dave DeSante (USDA Forest Service General Technical Report PSW-GTR-144). Objectives:

1. To classify each point into broad habitat types and to gather vegetation and limited landscape data that can be related to bird numbers.

2. To document habitat changes in response to disturbance (flood, fire, grazing), and to

provide data for examining how these changes relate to bird numbers.

3. To gather these data in a consistent, efficient, and useful way. These vegetation surveys are designed to take 15-30 minutes (assuming observers have good knowledge of trees, shrubs, and the common herb species).

How to use these instructions: This protocol should be reviewed by all project leaders at the beginning of each season, and discussed as necessary. All field biologists conducting vegetation surveys should go over this protocol with their project leader prior to attempting actual vegetation surveys. Several (at least 3) vegetation surveys should be conducted as a group by all vegetation data collectors on a project, particularly for calibration of cover estimation. Project leaders should discuss this protocol with each other and with all personnel conducting vegetation surveys prior to data collection. It is very important that there be consistency in these methods. In some cases slight modifications to the method are warranted, but these should be thoroughly discussed with all involved, and documented prior to implementation. When to do Vegetation surveys:

http://data.prbo.org/cadc2/index.php?page=prbo-point-count-veggie-protocol

http://www.cnps.org/cnps/vegetation/pdf/protocol-combined.pdf


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Vegetation surveys should be done during the breeding season of the first year of any point count project. In stable habitat types it will be necessary to repeat vegetation surveys every few years, while in relatively frequently disturbed habitats it may be necessary to do them each season. If new sites are added to a project, they should be surveyed the first year, and then on the same schedule as the other stations. Fill out one form for each point. Two sheets may be necessary if the plot covers more than one habitat type, clearly note at top of sheet when more than one form are used for a single point. Filling in the form, header section at top: Project: The project name, such as "VAFB Upland" or "VAFV Riparian" Transect: Unique MGRS 1km grid cell transect code (e.g. “10SGD2029”). Point ID: Name of the point count site (usually N, S, E, W, C). Habitat1: The dominant (i.e., most abundant) habitat type and Sawyer/Keeler-Wolf series (consult list at: http://www.cnps.org/cnps/vegetation/pdf/mcv2_veglist_sn_200911.pdf). Hab1%: Percent of the plot that is Habitat1. Habitat2: Secondary habitat type and Sawyer/Keeler-Wolf series. Hab2%: Percent of the plot that is Habitat2. Habitat percentages added together should not exceed 100%. Adjacent land use and habitat: dominant management practice of adjacent lands within 100 meters of the point and habitat (Sawyer/Keeler-Wolf series) if applicable. Recent disturbance: Yes or no, with note about what type (e.g. fire, flooding, vegetation removal). Patch length, width: Note the approximate size of consistently structured vegetation or to nearest patch edges in four cardinal directions and not approximate distance. Plot Radius: Usually 50 meters. Please do not vary without discussing with project leader. Water: The presence of water, both running and standing. Answer Y (yes) or N (no) to each.


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Snags>10cm: record the number of snags with dbh >10 cm on the plot. Snags<10cm: record the number of snags with dbh < 10 cm on the plot. Logs: record the number of logs (diameter > 10cm) on the plot. Aspect: The direction of slope given in degrees (can be thought of as the direction a drop of water or a marble would roll if it was placed in the plot). Averaged over entire 50 meter plot. Slope: The slope of the plot in angular degrees, use compass with clinometers to measure and estimate average over entire 50 meter radius plot. Filling in the form, 2nd Section, Total cover table: Total cover: Estimate the cover that each of the following vegetative layers provides over the 50 meter radius plot area. The layers are strictly defined by height categories, and should be thought of as: 1 or "tree" layer - the layer dominated by trees. This layer may contain vegetation that is not strictly a tree, such as vines hanging from trees, so long as its within the height range (5 meters to highest tree height). If there are two sublayers, add "T2" to layers box and record % cover, low and high species information. 2 or "shrub" layer - the layer dominated by shrubs. This layer may contain non-woody plants within the height range (.5 meters to less than 5 meters). If there are two sublayers, add "S2" to layers box and record % cover, low and high species information. 3 or "herb" layer - the layer dominated by herbs (0 to less than .5 meters). This layer may contain small shrubs and other woody plants less than .5 meters high. 4 or "totalwoody" layer - absolute cover of all woody vegetation combined across height categories. Also include additional ground cover layers that are relevant to the particular study plot or region, such as water (differentiate running versus standing, salt versus fresh, as needed), road, trail, litter, or moss cover. Project leaders should ensure these site-specific layers are included for all points within a project. The tree and the shrub layers can each be divided into 2 sublayers, if appropriate. If two distinct layers within a layer (by height) can be identified, estimate the cover of each separately. If both


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are 20% or more, they qualify as separate sublayers. Otherwise, lump them. The herb layer should never have more than one sublayer. If only one layer is present, leave the second layer blank. The second tree layer should be labeled "T2" and the second shrub layer should be labeled "S2". T1 and S1 should always be the taller than T2 and S2, respectively. Note that the total cover for each vegetation sublayer could theoretically reach 100%. If heights other than those specified here are used to define the different layers, those heights must be recorded and should be consistent across an entire project. Please do not change the heights without talking to the project leader. High: Estimate to the nearest 1 meter the average height of the upper bounds of the vegetation sublayers (tree, shrub, use 0.1 meter increments for herb). This is not usually the height of the tallest plant: if a single tree, which takes up a very small area, is much higher than the average high layer, this is NOT the height that is recorded. Another way to think of this is the height above which only 10% of individuals of the dominant species in a layer reach. Low: Estimate to the nearest 0.1 meter the average height of the lower bounds of the tree and shrub sublayers. This should be the average low living branches for each sublayer, NOT THE HEIGHT OF LOW TREES AND SHRUBS, so a LoHeight for a tree sublayer can be less than 5 meters! Lower and Upper Species: Record the dominant plant species that make up the upper and lower bounds for the tree and shrub sublayers. DBH: Estimate the minimum and maximum diameter at breast height to the nearest 1 centimeters, for the tree layer and, if applicable (if shrubs reach or exceed dbh level) the shrub layer. Minimum and maximum species: Record the species of tree or shrub used for minimum and maximum dbh. Filling in the form, 3rd section: Species cover table: Refer to USDA NRCS Complete Plants Checklist (http://plants.usda.gov/dl_all.html) for list of 4-6 digit codes for each species. Sublayer: Record the sublayers as "T1" for tree sublayer 1, "T2" for tree sublayer 2 (if present), "S1" for shrub sublayer 1, "S2" for shrub sublayer 2 (if present), and "H1" for the herb sublayer. Species: Record in the appropriate place the 4-6 digit code for most species that occur in each sublayer. A species that occurs in multiple sublayers should be recorded separately for each. Do your best to list most species in each layer without spending more than about 15 minutes at a site

http://plants.usda.gov/dl_all.html


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searching for plants. For the total woody layer, we recommend listing the top 5 most common woody species. Be sure to include any species of interest to the study, such as invasive exotics. Cover: Record the relative cover of that sublayer made up by that species. The sum of all the species' covers within a sublayer should equal 100%. Glossary of terms cover - the percent of ground obscured from above. For total cover table, this is the absolute cover (pretending that the other layers do not exist). For species relative cover table it is relative to the other species in the layer. DBH – diameter at breast height. Breast height is defined as 1.3 meters above ground. layer - a height category for describing habitat. A tree or shrub layer can be comprised of 2 "sublayers."" sublayer - a layer that falls within a larger layer.


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Appendix C: List of sensitive species targeted by this monitoring program This table was reproduced from Tab D Appendix A of the 2011-2015 Vandenberg AFB Integrated Natural Resources Management Plan. Monitoring targets show which species are likely to be adequately surveyed with this study design and field protocol, and the reason (not associated with a target habitat, or inappropriate survey methods) for which some species are not adequately surveyed. California quail is an additional game species of special concern that will be adequately monitored with these methods. Common name Scientific name Monitoring target Mountain plover Charadrius montanus YES Western burrowing owl Athene cunicularia hypugea YES Vaux’s swift (nesting) Chaetura vauxi YES Costa’s hummingbird (nesting) Calypte costae YES Allen’s hummingbird (nesting) Selasphorus sasin YES Nuttall’s woodpecker (nesting) Picoides nuttallii YES Olive-sided flycatcher (nesting) Contopus cooperi YES Loggerhead shrike Lanius ludovicianus YES Oak titmouse (nesting) Baeolophus inornatus YES Grasshoper sparrow (nesting) Ammodramus savannarum YES Bell’s sage sparrow Amphispiza belli belli YES Belding’s savannah sparrow Passerculus sandwichensis beldingi YES Black-chinned sparrow (nesting) Spizella atrogularis YES Tricolored blackbird Agelaius tricolor YES Ferruginous hawk (wintering) Buteo regalis NO (survey method) Northern harrier (nesting) Circus cyaneus NO (survey method) Golden eagle Aquila chrysaetos NO (survey method) Bald eagle Haliaeetus leucocephalus NO (survey method) American peregrine falcon Falco peregrinus anatum NO (survey method) White-tailed kite Elanus leucurus NO (survey method) Long-eared owl (nesting) Asio otus NO (survey method) Short-eared owl (nesting) Asio flammeus NO (survey method) Purple martin (nesting) Progne subis NO (survey method) Common loon (nesting) Gavia immer NO (habitat) Ashy storm-petrel (rookery site) Oceanodroma homochroa NO (habitat) California brown pelican Pelecanus occidentalis californicus NO (habitat) Least bittern (nesting) Ixobrychus exilis NO (habitat) Western snowy plover Charadrius nivosus NO (habitat) Black oystercatcher (wintering) Haematopus bachmani NO (habitat) Long-billed curlew Numenius americanus NO (habitat) California least tern Sterna antillarum browni NO (habitat) Black skimmer Rynchops niger NO (habitat) Marbled murrelet Brachyramphus marmoratus NO (habitat)


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Little willow flycatcher (nesting) Empidonax trailii brewsteri NO (habitat) Southwestern willow flycatcher Empidonax trailii extimus NO (habitat) Bank swallow (nesting) Riparia riparia NO (habitat) Yellow warbler (nesting) Dendroica petechia brewsteri NO (habitat) Yellow brested chat (nesting) Icteria virens NO (habitat)

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