2019-20 Baseline Demographic Survey of the Bishop Pine (Pinus muricata) on Santa Rosa Island, California

Environmental Science and Resource Management Capstone Project

Andrew Tegley, Eric Schwarz, Victoria Joyce, and Grant Hassinger

Submitted in partial fulfillment of the requirements for a Bachelors of Science degree in Environmental Science and Resource Management from California State University Channel Islands

1 Table of Contents:

I. A b stract 3

II. Study A rea 6

III. H y p o th eses 7

IV. Materials and Methods 8

V. R esu lts 18

VI. D iscu ssio n 37

VII. C onclusion 43

VIII. Acknowledgements 45

IX. Literature Cited 45

X. A p p en d ix 51

2 Abstract

Environmental factors, including drought, ungulate grazers, bark beetle infestation, and competition for resources have affected the abundance, distribution, and health of the Bishop Pine population on Santa Rosa Island, California. Santa Rosa Island, one of the northern Channel Islands, is the second-largest island in California, with an area of 217 km2. Since the 1980s, the island has been managed by the National Park Service, which brought an end to the era of ranching operations that had caused significant damage to the island’s native vegetation via ungulate grazing. The complete removal of ungulate grazers by 2011 provided a novel opportunity to monitor the regeneration of the island’s native vegetation. In 2016, a preliminary survey of the Bishop Pine population was conducted, which resulted in the establishment of thirty-five circular (6.3 m diameter) plots in the vicinity of Black Mountain. In 2019, we completed a thorough demography survey of the Bishop Pine population, thereby providing an assessment of the Bishop Pines’ status following the extraordinary drought that California experienced from 2012-2016 while simultaneously establishing a baseline set of data that future researchers can reference and build-upon. In our study, we discovered that there were five times more seedlings in 2019 as compared to 2016, and that most seedlings were found on slopes that ranged from 40-65% with lesser amounts of crust/moss. We also discovered that seedling and sapling growth did not occur in plots with levels of vegetation species richness higher than 10. 48% of Bishop Pine plots showed evidence of bark beetle infestation in 2019. Trees that had larger diameters, as well as trees that were unhealthy or dead prior to infestation in 2016, were associated with higher levels of infestation in 2019. Beetle samples were collected from underneath the bark of Bishop Pines throughout the study area. The samples were identified as weevils of the genusStenoscelis , i.e. inquilines, which take advantage of the damage wrought by the initial invasion of another species, and are often associated with a more-aggressive, primary invader. As such, the bark beetle species causing the initial damage and, potentially, contributing to the mortality of Bishop Pines on Santa Rosa Island is yet to be identified.

Keywords: Santa Rosa Island, California, Bishop Pine, Pinus muricata, Channel Islands National Park, Bark Beetle, Stenoscelis, Demography Survey, DBH, Drought, Recruitment, Species Richness

3 Introduction

Drought is a significant driver of forest mortality across the world. Anthropogenic activities have resulted in a trend of increasing global temperatures, which is causing droughts to increase in frequency and severity (Allen et al. 2010). Given that drought has such a pronounced impact on forest health and structure, it is important to understand the impacts of past and current droughts in order to better-predict, and mitigate, the effects of future droughts. The California drought of 2012-2016, a product of low precipitation and record-high temperatures, was the worst drought that California has experienced in the past 1,200 years (Griffin and Anchukaitis 2014). Since 2010, an estimated 147 million trees have died in California, primarily due to drought and drought-driven impacts, e.g. bark beetle epidemics (USDA 2019). These extreme events have raised concerns among conservationists and land managers regarding the future viability of native trees, particularly those with a limited distribution and small population size, such as the Bishop Pine (Pinus muricata D. Don) (Fischer 2009). The Bishop Pine is a drought-sensitive, sparsely distributed species of closed-cone pine that is endemic to the coast of California, including the northern Channel Islands, and Baja California, Mexico (Williams et al. 2008).

Approximately 12,000 years ago, a warming climate reduced the once-extensive Bishop Pine forests of the Channel Islands to a small number of isolated stands that are spottily distributed across Santa Cruz and Santa Rosa Islands (Rick et al. 2014). Several efforts have been made to study the characteristics and structure of Bishop Pine communities on Santa Cruz Island, but little knowledge exists with regards to the structure and population dynamics of the Santa Rosa population, particularly following the extreme drought of 2012-2016 (Baguskas et al. 2016). These stands are primarily distributed on precipitous, north-facing slopes that are inundated by the coastal fog belt, which provides a critical source of moisture during the dry summer months (Rastogi et al. 2016, Buckley 2016, Pritchard 2016). On the Channel Islands, the Bishop Pine fulfills an ecological role as a keystone species by making water available to the surrounding flora and fauna during the dry summer months. The pine’s needles sequester and condense the moisture provided by the fog belt, thereby providing water to the surrounding environment via the process of “fog drip.” This mechanism is critical for the survival of other endemic species that have evolved in isolation on the islands, reliant on the availability of water during the summer (Williams et al. 2008, Fischer et al. 2009, Baguskas et al. 2016).

Drought is a primary influence on the mortality of Bishop Pines, evidenced by large-scale diebacks on Santa Cruz Island during the drought years of 1976-1977, 1988-1989, and most-recently in 2012-2016 (Fischer et al. 2009, Taylor et al. 2019). Severe drought influences the population dynamics and structure of pine communities in several ways: carbon starvation;

4 hydraulic failure; competition over resources; bark beetle infestations; and soil desiccation (Allen et al. 2010, Grogan et al. 2000). In response to drought, trees close their stomata in order to prevent the excess loss of water, thereby halting the process of photosynthesis and, subsequently, energy production. If the drought outlasts the tree’s stored provisions then the tree will die as a result of “carbon starvation” (Allen et al. 2010). In addition, the rapid desiccation of a tree may result in the formation of emboli within the xylem, the tissues that transport water throughout the tree, thereby blocking the flow of water (Choat et al. 2012). Amongst these internal changes to the Bishop Pine, drought also leads to competition between the pine and its neighboring vegetation. During times of drought, pines compete with neighboring species for underground resources, especially water. Studies have shown that this competition for water, particularly between pines and oaks, has led to a decline in pine survival and growth rate (Jucker et al. 2014). Areas with high species richness are more likely to encounter such competition due to limited space and finite resources available.

The physiological stressors that forest communities experience during periods of severe drought, e.g. carbon starvation and hydraulic failure, also make trees vulnerable to exploitation by insects, particularly bark beetles (Kolb et al. 2013). As temperatures and, subsequently, drought severity and frequency have increased on a global level, the range and prevalence of bark beetle populations has greatly increased (Raffa et al. 2008). These environmental conditions result in the impairment of metabolic processes that provide a conifer with the materials necessary to defend against foreign invaders. In order to complete their life cycle, bark beetles tunnel their way through the bark of a conifer, into the phloem or vascular tissues. Within the phloem, the insects carve galleries, which are laden with eggs. After hatching, the larvae feed on the tree’s tissues until they develop into adults and emerge from the bark in search of a host. Drought-stressed conifers have fewer nutrients and water resources to allocate to the production of resin, a viscous substance that is utilized as a primary defense mechanism, serving as both a physical barrier, and as a chemical repellent that expels invasive insects (Raffa et al. 2008). Drought can also threaten the health and recruitment of Bishop Pines via the desiccation of local soils. Pine growth is dependent upon the ability to take up water for cell expansion and growth (Hsiao 2000). This trait can be threatened in the case of extreme drought, like the one that occured in 2012-2016. Another stressor for Bishop Pine recruitment on the Channel Islands is ungulate grazers which would consume seedlings and saplings before they were big enough to not be a food source for these animals. Now post-drought and post-removal of grazers (McEachern et al. 2009), Bishop Pines have an opportunity for recruitment without these barriers that they have faced for more than a century of ranching land use. In this study, we provide insight into the overall status of the Bishop Pine community on Santa Rosa Island via an analysis of several biotic and abiotic factors, including current regeneration rates, bark beetle infestation, species richness levels, and resource competition measured in 36 plots that were previously established and sampled in 2016 (Buckley 2016, Pritchard 2016).

5 Study Area

Santa Rosa Island is one of the northern Channel Islands, located 43 km offshore of mainland California within Channel Islands National Park. Managed by the National Park Service, Santa Rosa is the second-largest island in California with an area of 217 km2 (McEachern et al. 2016). Ungulate grazing and ranching activities have taken place on Santa Rosa Island since the late 1800s and has led to erosion and thus native species decline as a result. Recently, the island experienced a tremendous drought through 2012-2016 that did not allow for optimal vegetation recovery on Santa Rosa Island.

Figure 1: Maps showing the location of the study area, including sub-populations assessed and individual plots, in relation to Santa Rosa Island, the northern Channel Islands, and mainland California.

6 Hypotheses

In 2019, we collected and summarized plot data as a team. In 2020, we each investigated certain aspects of Bishop Pine ecology in more detail, framing and testing our individual questions as hypotheses. The hypotheses are as follows:

Eric:

Hj: Recruitment has increased without the impediment of drought and ungulates.

H,: The number of seedlings varies with different ground cover types

Hj: Slope steepness has an effect on the amount of seedlings that germinate.

Andrew:

Hj: Bark beetles are present throughout the Bishop Pine study area on Santa Rosa Island, CA in 2019.

H,: Throughout the study area, Bishop Pines with larger diameters are more susceptible to bark beetle infestation.

H,: Throughout the study area, healthier Bishop Pines are less susceptible to bark beetle infestation.

Victoria:

H,: In the study area, plots with higher levels of vegetation species richness experience a decrease in average Bishop Pine health class scores.

H,: Higher levels of vegetation species richness leads to more needle dieback amongst the Bishop Pines in the study area.

Hx: Higher levels of vegetation species richness leads to a decrease in Bishop Pine seedling production within the study area.

Hx: Higher levels of vegetation species richness leads to a decrease in Bishop Pine sapling production within the study area.

7 Grant:

Hx: There has been a decline in the health of Bishop Pines on Santa Rosa Island from 2016 to 2019.

H,: Some environmental factors can be positively associated with tree decline.

Materials and Methods

Field Methods

Plot Distribution Bishop Pine distribution on Santa Rosa Island was mapped using aerial photographs and then verified in the field during the 2015-2016 population survey (Buckley 2016, Pritchard 2016). Bishop Pines occur in six distinct stands, or “subpopulations”, on the island. Four stands are located in and around the upper Windmill and Cherry Canyon Drainages on the north-face of Black Mountain (Figure 1). The last two stands each consist of only a few trees and are located near Radar Peak and in a small unnamed drainage east of the Smith Highway.

In 2016, thirty-five points were chosen at random in ArcMap (vl0.3) within the four Black Mountain subpopulations. The number of points assigned to each subpopulation was in proportion to the mapped subpopulation area. Fifteen points were located in subpopulations 1 and 2, two points were located in subpopulation 3, and three points were located in subpopulation 4 (Buckley 2016, Pritchard 2016). These points became the locations of the permanent, fixed plots that were initially sampled in 2016 and then sampled again in the 2019-2020 demography survey that established a baseline set of data from which future surveys will reference and build-upon. During the 2019-2020 survey, a Bishop Pine (PIMU) plot marker was discovered in subpopulation 1 that did not have a GPS waypoint associated with it. In order to eliminate any future confusion, we included the plot in our survey, bringing the total number of surveyed plots to thirty-six.

Data Collection

1) Record plot attributes

8 a. Find the plot’s center: Locate the plot, using a GPS as a navigational aid. An aluminum stake, embossed with “PIMU” and the plot number, marks the center of the circular plot which has a radius of 6.3 m. b. Record the stand #: Consult the map in order to determine which stand, i.e. subpopulation/polygon, the plot is located within. c. Record the plot #: The plot # is embossed on the plot’s stake (refer to la.) d. Record the Brand, Model, Datum (set to NAD83), and number of your GPS unit: There should be an arbitrary number associated with your GPS unit if you borrowed it from the Santa Rosa Island Research Station (SRIRS). Record this number and use the same GPS each time you work in the field. e. Record the UTM Zone, Northing, and Easting:Standing at the center of your plot, use a GPS unit to create a waypoint. Record the UTM Zone, Northing, Easting, and the corresponding waypoint number. f. Record the Accuracy (m) of the GPS:When creating a waypoint, record the GPS’s accuracy (in meters). Accuracy may differ between locations due to satellite geometry, signal blockage, atmospheric conditions, etc. g. Record the Elevation (m) of the Plot’s Center:Standing at the center of a plot, use a GPS to determine the elevation (in meters). h. Record the Slope’s Aspect (degrees):Standing at the center of a plot, use a compass to determine the slope aspect (in degrees). Make sure that the metal stake does not affect the accuracy of your reading. i. Record the Slope’s Steepness (%):Use a clinometer or clinometer app (e.g. STANLEY® Level) to determine the slope’s steepness (%). j. Mark the Plot’s Cardinal Directions: Using a compass and transect tape, place a candy cane 6.3m from the central stake, at North (0°), East (90°), South (180°), and West (270°). k. Establish the Plot’s Perimeter: Use a fiberglass transect tape, anchored at the aluminum center stake, to establish the plot’s perimeter. Extend 6.3m of tape and rotate around the central stake to establish a circular plot. Temporarily mark the perimeter with pin-flags and/or colored tape. Find North, East, South, and West, and then mark those spots along the plot’s perimeter using candy canes. The candy canes will help you visualize the plot’s quadrants. l. Divide the Circular Plot into Quadrants:Quad 1 = N to E (0° - 90°), Quad 2= E to S (90° - 180°), Quad 3 = S to W (180° - 270°), and Quad 4 = W to N (270° - 0°). m. Record any Plot Notes:Take note of any relevant information, e.g. hydrologic features, recent disturbances, etc.

9 n. Photograph the Plot:Stand at the plot’s northern perimeter and take a picture of the plot (looking towards the Southern perimeter). o. Record the Camera # and Photo #: Similar to the GPS, if you borrowed a camera from the SRIRS, there should be an arbitrary number associated with it. The photo number refers to the number that the camera automatically assigns to the picture (do not confuse this with the number associated with the picture’s position on the memory card). The photo number should look something like “DSC 111-0520.”

2) Data Collection (within plot)

a. Tree: On SRI, a “tree” is defined as an individual taller than 160cm or with a dbh >4cm. A tree is considered to be within a plot if more than half of the trunk diameter lies within the plot’s 6.3m radius. Keep track of which trees are in which quadrant so that it is easier to (a) keep track of them during the survey, and, (b) find them in the future. b. Record the Tree Tag #: Check the north side of each tree to determine whether they are already tagged. If the tree is not tagged, mark the tree with an aluminum tree tag on the north side of the tree so that it can be resampled in the future. Nail tag to the trunk of the tree (leaving ~2cm of space between the trunk and tag so that the tree has room to grow). Tag trees in locations that are not immediately obvious to the casual observer. If the tree is too small to mark with a nail and tag, nail the tag to the ground at the base of the tree on the north side. Tree tag numbers should be sequential from the beginning of the plot (i.e. Quad 1 = NE Quad). Record the tree tag number. c. Distance (m): Using the transect tape, record the distance from the plot’s center stake to the center of the tree trunk (to the nearest centimeter). d. Azimuth (degrees): Standing at the center stake, record the azimuth (compass bearing) to the center of the tree (in degrees). Again, make sure that your compass bearing is not being adjusted by the metal stake. e. Diameter at Breast Height (DBH): Measure the tree’s diameter at breast height (1.3m above the ground) using diameter tape. If the tree is leaning, measure the trunk where 1.3m would be if the tree were standing upright. If the tree has multiple trunks, measure the trunk with the largest diameter. f. Number of Trunks: Record the number of trunks/stems that are distinctly separated within 30cm of ground-level. g. Tree Height Class (m):Estimate height in the following classes: (1) = <30cm; (2) = 30-160cm; (3) = 161-300cm; (4) = 310-600cm; (5) = >600cm.

10 h. Tree Health Class: Estimate the overall health of each tree based on the proportion of dead canopy. Use the following health classes: (0) = dead; (1) = <50% dead; (2) = >50% dead. i. Cone Production:Estimate the number of OPEN and CLOSED cones for each tree using the following classes: (0) = no cones; (1) = 1-10; (2) = 10-100; (3) = >100 cones. j. Needle Dieback: Estimate needle dieback as a percentage of the live-crown area, including the dieback area. Assume the perimeter of the crown is a two-dimensional image outline from branch-tip to branch-tip, excluding snag branches and large holes/gaps in the crown. Estimate in bins of 10% as follows: (0) = 0%; 10 = 1-10%; 20 = 11-20%, etc. Avoid counting old dead branches at the bottom of the canopy. k. Bark Beetle Attack: (0 = No; 1 = Yes) This covers notable and visible attacks from bark beetles that occurred within the last five years. Each Bishop Pine within the study area was examined for evidence of bark beetle attack. The examination was focused on the trees’ trunk area and any accessible branches. Tiny pin-hole exit holes in the bark or furrows, bluish or greyish fungal hyphae under the outside bark layers, red sap plugs, and fine sawdust are all evidence of bark beetle attack. l. Collection of Bark Beetle Specimens:Live arthropod samples were collected by using a pocket knife to peel back the outer layer(s) of bark (bearing evidence of bark beetle damage) from (1) the trunk of live Bishop Pines between ground-level and ~18-inches above the ground; and (2) dead, fallen trees. Specimens were transferred to plastic 5 ml vials that had filled with ethanol prior to collection.

3) Data Collection (site data collected within plot; estimations/counts for the entire plot)

a. Canopy Cover Class (%):Estimate Bishop Pine cover of both Live and Dead canopy as a percent of the ground surface of the entire plot covered by projecting the perimeter of Bishop Pine canopy cover onto the plot surface, in cover classes of 0-5: (0) = none; (1) = trace; (2) = 1-25%; (3) = 25-50%; (4) = 50-90%; (5) = >90%. b. Seed Bank: Estimate the number of OPEN and CLOSED Bishop Pine cones lying on the ground within the entire plot using the following classes: (0) = no cones; (1) = 1-10; (2) = 10-100; (3) = >100. c. Understory Shrub Species Canopy Cover (%):Estimate as a percent of the ground-surface of the entire plot covered by understory shrubs (collectively, not species-by-species) by projecting the perimeter of their canopy cover onto the plot

11 surface. Use the following classes: (0) = none; (1) = trace; (2) = 1-25%; (3) = 25-50%; (4) = 50-90%; (5) = >90%. d. Substrate Cover (%):Estimate percent-cover ofBare Ground (BG = bedrock and soil), soil Crust, Lichen, and Moss (CST), and Litter (LIT) for the whole plot. Make a visual estimate of cover of each cover type in the plot by imagining a line drawn around the perimeter of each parameter, projected down horizontally onto the land surface, as if they totaled up to 100%. Estimate the percent-cover of each cover-type using the following classes: (0) = none; (1) = trace, (2) = 1-25%; (3) = 25-50%; (4) = 50-90%; (5) = >90%. e. Record Plant Species: Record all plant species rooted in the plot, e.g. shrubs, sub-shrubs, and trees, in addition to Bishop Pines. For grasses, ferns, and herbs, record species (if known) or Unknown grass, Unknown fern, or Unknown herb. f. Seedlings and Saplings: Seedlings (SRI) = Bishop Pines <30cm tall. Saplings (SRI) = Bishop Pines 30-160cm tall. Count and record the number of all seedlings and saplings in the plot as live or dead. Record counts separately for each plot quadrant.

4) Re-establishing Plots

a. We replaced all thirty-six of the Bishop Pine plots’ central markers that had been established in 2016 with new rebar and aluminum stakes. The numbering system that was previously used to distinguish between Bishop Pine plots in 2016 was inconsistent, inaccurate, and overall confusing. As such, we developed a new numbering system that eliminates confusion and will enable future researchers to work more efficiently. The rebar will enable future researchers to find plots using a metal detector if the plot has become overgrown with vegetation. b. Our numbering system consists of the designation “PIMU” followed by the subpopulation and plot numbers. Example: “PIMU 1-12” designates that you are within the Bishop Pine study area’s subpopulation #1- plot #12. c. We stamped each of the aluminum stakes using a stamp kit and mini sledge. d. In the field, each Bishop Pine plot’s central marker was located and then replaced with a newly stamped aluminum stake as well as a piece of rebar. After each stake was replaced, we collected highly-accurate GPS coordinates using an EOS Arrow GPS unit in order to further ease the process of future fieldwork. e. The aforementioned process was completed for twenty-eight Bishop Pine plots that contained central plot markers. We also established plot markers for eight Bishop Pine plots that did not contain central plot markers, yet were stored as waypoints in the previous study (2016).

12 Data Analysis Methods

Eric:

Ht: Recruitment has increased without the impediment of drought and ungulate grazing.

How many seedlings are there per unit area in 2016 versus 2019?

Since the last of the ungulate grazers were removed in 2011, this grazing cessation has removed a stressor that has prevented recruitment in the past. Seedling density was calculated by counting up the number of plots surveyed each year (2016 and 2019), calculating the area of each plot (Area=3.14(6.3A2)), and then calculating the total area sampled each year by adding the total area of all the plots surveyed in each year. The number of seedlings counted for that given year was divided by the total area of the sampling year to create the density (e.g. total seedling count 2016/total area 2016 = seedling density 2016 in numbers per square meter). Differences between years were summarized in tabular format.

Is the growing season precipitation related to seedling establishment in plots?

Now removed from the drought, Bishop Pines have the opportunity to recruit without major stressors. It is known that drought is the primary influence upon Bishop Pines on Santa Cruz Island (Fischer et al. 2009, Taylor et al. 2019) so now that we are a few years out of the drought it is likely that this population is recruiting at a higher rate. To examine if recruitment of seedlings has increased over time I explored the influence of annual precipitation on seedling survivorship, I surveyed saplings in thirty-six plots in 2019 and counted a total of twenty-two saplings. For each of the twenty-two saplings, I estimated the age by counting the whorls, which represent the growth from year to year for a Bishop Pine tree. Whorl count ranged from one to six and this information was used to approximate the germinating year of these pines (e.g. a sapling that had six whorls would have approximately germinanted in 2013). Precipitation data was acquired from the weather station that is positioned on top of Black Mountain on Santa Rosa Island t https://wrcc.dri.edu/channel_isl/ accessed 2/17/2020). Precipitation was counted for the “water year” in a Mediterranean climate which is October 1- September 30 and totaled for that given year (e.g. water year 2013 was the precipitation sum from October 2013-September 2014).

Ht: Different ground cover types can change the amount of seedlings that germinate.

13 I tested for the effects of a variety of different types of ground cover on the number of seedlings occurring in plots in 2019. These factors included crust/moss, litter, and bare ground. The class of crust/moss is the amount of biotic crust in the entire plot, including non-vasculars lichen, moss, cyanobacteria, liverworts, etc. The class of litter includes downed branches, sticks, leaves, needles, and pine cones. The bare ground class is composed of soil, bare rock, sand, and gravel. Total seedling count for each plot was also used to test against these different environmental factors. A Kruskal-Wallis test, a nonparametric test, was utilized to analyze this data because the total seedling count for all thirty-six plots did not follow a normal distribution; this was determined using a QQ plot. I used a Kruskal-Wallis H test to test whether the number of seedlings per plot differed based on an environmental variable score (0-5). All of this was done within SPSS Statistics 26.

Ht: Slope steepness has an effect on the amount of seedlings that germinate.

The final environmental factor that was looked at was slope steepness. I examined whether there was a relationship between slope and the amount of seedlings that had germinated in 2019. Slope information was gathered for all thirty-six plots along with seedling count. I compared the slopes of plots that contained seedlings (n=13) with the slopes of plots that did not contain seedlings (n=23). I used a two-tail t-test, assuming unequal variances to test if there was a significant difference in slope between plots that contained seedlings and those that did not.

Andrew:

Ht: Bark beetles are present throughout the Bishop Pine study area on Santa Rosa Island, CA in 2019.

Pinhole entrance/exit holes in the bark, furrowed galleries underneath the bark, bluish or greyish fungal hyphae underneath the bark, red sap plugs, and fine sawdust are all considered to be evidence of bark beetle infestation (USDA 2015). Each individual Bishop Pine tree within the study area was thoroughly examined and the presence or absence of bark beetle damage was recorded. Using this information, in conjunction with information collected in 2016, I was able to determine the proportion of plots with bark beetles each sample year, and which Bishop Pine plots had become infested by bark beetles since 2016.

The beetle samples were sent to Dr. Tom Dudley at the University of California, Santa Barbara, in order to obtain an identification.

Ht: Throughout the study area, Bishop Pines with larger diameters are more susceptible to bark beetle infestation.

14 In order to determine whether a correlation existed between Bishop Pine diameter (DBH) and bark beetle infestation, I used IBM SPSS Statistics (v26) to run a Kolmogorov-Smirnov Test (KS-Test) to test the null hypothesis that there is no significant difference in the distribution of the diameters of infested pines (N = 38) versus the distribution of the diameters of uninfested pines (N = 65). I limited the test to include only the DBH-values of pines that were located within plots that contained at least one infested pine, in order to account for the possibility that bark beetles may have never visited the uninfested plots.

Ht: Throughout the study area, healthier Bishop Pines are less susceptible to bark beetle infestation.

My sample (N = 53) consisted of trees that I could compare between 2016 and 2019 based on the existence of tree tags as well as health class and bark beetle infestation data. In order to compare the rates of infestation between 2019 infested trees (N = 25) and uninfested trees (N = 28), based on their prior health in 2016, I ran a Chi-Square Test (socscistatistics.com) which tested the null hypothesis that there is not a significant difference in the 2019 frequency of bark beetle infestation among trees that were relatively healthy (< 50% dead) versus relatively unhealthy (> 50% dead) versus dead (100% dead) in 2016 within the study area.

Victoria:

Hx: In the study area, plots with higher levels of vegetation species richness experience a decrease in average Bishop Pine health class scores.

In order to see if there is a correlation between the average health of the Bishop Pines and the level of species richness per plot, I used a linear regression test with the average health class of each plot as the dependent variable and vegetation species richness as the independent variable. While the “health” of a tree can refer to many different aspects, I used the health class as an indicator to represent health within my testing. For each individual Bishop Pine within a plot, the average health class was determined based on what percentage of the pine appeared to be dead and what percentage appeared to be alive (0 = dead, 1 = > 50% dead, 2 = < 50% dead). To determine the average health class for an entire plot, I added together all of the health classes for every tree within the plot and divided by the number of individual trees (sum of every Bishop Pines’ health class per plot/number of Bishop Pines per plot). Vegetation species richness was calculated by counting the number of different plant species, including the Bishop Pine, within the plot’s perimeter. The different plant species that were identified varied from shrubs, bushes, ferns, trees, grasses, sedges, succulents, moss, and lichens. Photos of each plant species were taken and used for identification. The linear regression test consisted of using the average health

15 class per plot and level of vegetation species richness per plot for the twenty-five plots that had Bishop Pines present. The other eleven plots did not have Bishop Pines within their perimeter so they were not included in this testing

Ht: Higher levels of vegetation species richness leads to more needle dieback amongst the Bishop Pines in the study area.

While “health” is a broad term, I focused on the Bishop Pines’ percentage of needle dieback and used it as one of many indicators of health. I used a linear regression test to test the correlation between needle dieback and the level of vegetation species richness for each plot, using needle dieback as the dependent variable and species richness as the independent variable. For each individual Bishop Pine within a plot, the percent of needle dieback was measured from the appearance of the branches and needles (measured to the nearest 10%; 0% = no needle dieback, 100% = dead, brown needles). The average percentage of needle dieback per plot was calculated by adding together the percentages of every pine within the plot and dividing it by the number of pines present (sum of every Bishop Pines’ needle dieback percentage per plot/number of Bishop Pines per plot). Vegetation species richness was calculated by counting the number of different plant species within the plot which included the Bishop Pine. The different plant species that were identified varied from shrubs, bushes, ferns, trees, grasses, sedges, succulents, moss, and lichens. Photos of each plant species were taken and used for identification. The linear regression test consisted of the average needle dieback percentage per plot and vegetation species richness per plot for twenty-five plots that had Bishop Pines present. The remaining eleven plots were not included due to no pines growing within their perimeter and in order to maintain accuracy.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine seedling production within the study area.

The total number of Bishop Pine seedlings present within each plot was used as one of many indicators of the plot’s “health”. I used a linear regression test to determine if there was a correlation between the total of seedlings and the level of vegetation species richness for each of the thirty-six plots, using seedling count as the dependent variable and the species richness as the independent variable. To calculate the total number of Bishop Pine seedlings present within each plot’s perimeter, every pine that was less than 30 cm tall was considered a seedling and was included in the count (seedling = < 30 cm tall, sapling = 30-160 cm tall, tree = an individual with > 4 cm DBH). Vegetation species richness was calculated by counting the number of different plant species, including the Bishop Pine, within the plot’s perimeter. The different plant species that were identified varied from shrubs, herbs, trees, grasses, moss, and lichens. Photos of each plant species were taken and used for identification. The linear regression test consisted of the

16 total number of seedlings present per plot and the vegetation species richness level per plot for all thirty-six of the plots.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine sapling production within the study area.

The total number of Bishop Pine saplings present within each plot was used as one of many indicators of the plot’s “health”. I used a linear regression test to determine if there was a correlation between the total of saplings and the level of vegetation species richness for each of the thirty-six plots, using seedling count as the dependent variable and the species richness as the independent variable. To calculate the total number of Bishop Pine saplings present within each plot’s’ perimeter, every pine that was between 30 cm and 160 cm tall was considered a sapling and was included in the count (seedling = < 30 cm tall, sapling = 30-160 cm tall, tree = an individual with > 4 cm DBH). Vegetation species richness was calculated by counting the number of different plant species, including the Bishop Pine, within the plot’s perimeter. The different plant species that were identified varied from shrubs, herbs, trees, grasses, moss, and lichens. Photos of each plant species were taken and used for identification. The linear regression test consisted of the total number of saplings present per plot and the vegetation species richness level per plot for all thirty-six of the plots.

Grant:

Calculated Values: Basal Area: Basal Area is a measurement used to describe the density (m2)of a forest stand. This factor takes into account the diameter of trees in the plot as means to quantify competition for 2 water and space (Kerhoulas et. al 2013). It is calculated by: Basal Area = 71 x

Northness and Eastness: To remove the circular data present in the aspect reading, northness was calculated by Northness = Cosine(Aspect) xSine(Slope ) (Calaway and Davis 1993). This factor summarizes the topography present at each plot based on how north facing the slope was. It is matched by eastness, calculated by Eastness = Sine(Aspect) x Sine(Slope) (Amatulli et. al. 2018) Changes in tree health were determined by comparing trees with the same tree tags between 2016 and 2019. The test for changes in average health class and dieback were done using Student's t-test.

17 Correlations between environmental plot factors(Basal area, understory Shrub Coverage, Bark Beetle Presence, Total Canopy, Northness of the Plot, Eastness of the Plot, DBH, Slope of Plot, Elevation, and the Bare Ground measurement) and health (both Health Class and Needle Dieback) were calculated using Spearman’s correlation using RStudio( version 1.2.5033) and the library Hmisc. The R library corrplot was used to generate the matrix chart.

Results Thirty-six Bishop Pine plots were sampled throughout the study area within the Black Mountain region on Santa Rosa Island, CA. Twenty-eight of the sampled plots had previously been sampled in 2016. The remaining eight plots had been listed as Bishop Pine plot locations during the 2016 survey, but had not been established or surveyed. In 2019, we established and surveyed the remaining eight plots using the same methodology that we had used to survey the previous twenty-eight plots. In total, one hundred twenty-two Bishop Pine trees, twenty-four saplings, and one hundred fifty-seven seedlings were distributed throughout twenty-five of the plots within the study area. The remaining eleven plots did not contain any Bishop Pine trees, saplings, or seedlings. Plots ranged in elevation from one hundred sixty-four to eight hundred fifty meters, with the majority having a northwestern aspect. Slopes generally had an average slope of approximately 52%. Of the four cover classes that we considered during the study, the average rank for understory shrub and litter was three; bare ground had an average rank of two; and crust/moss had an average rank of three.

Eric:

Ht: Recruitment has increased without the impediment of drought and ungulates

How many seedlings are there per unit area in 2016 versus 2019?

Total Plot Area 2016 (m2) Total Plot Area 2019 (m2)

3988.16 4486.56

Total Seedling Count 2016 Total Seedling Count 2019

24 157

Density of Seedlings 2016 (m2) Density of Seedlings 2019 (m2)

18 .006 .035

Table 1: Densities of Bishop Pine seedlings (m2) sampled in plots on Santa Rosa Island, California in 2016 and 2019.

H2: sI the growing season precipitation related to seedling establishment in plots?

Figure 2: In this graphic, there was variable rainfall from year to year along with variable seedlings that germinated in a given year that lived to become saplings. Graph is using logarithmic to allow reader to see the shape of the precipitation line along with the high number of seedlings counted in 2019 (94)

Hp Different cover types can change the amount of seedlings that germinate.

Number of Seedlings Across Increasing Amounts of Litter Cover

Total N 36

Test Statistic 2.831

19 Degree of Freedom 2

Asymptotic Sig. (2-sided test) .243

Table 2: The Kruskal-Wallis H test showed that there was not a statistically significant difference in the number of Bishop Pine seedlings on Santa Rosa Island, California in 2019 per plot between different levels of litter within the plot. We failed to reject the null hypothesis and accept that there is no difference between the levels of litter and seedlings per plot.

Number of Seedlings Across Increasing Amounts of Bare Ground Cover

Total N 36

Test Statistic 7.311

Degree of Freedom 3

Asymptotic Sig. (2-sided test) .063

Table 3: The Kruskal-Wallis H test showed that there was not a statistically significant difference in the number of Bishop Pine seedlings on Santa Rosa Island, California in 2019 per plot between different levels of bare ground within the plot. I failed to reject the null hypothesis and accept that there is no difference between the levels of bare ground and seedlings per plot.

Number of Seedlings Across Increasing Amounts of Crust/Moss Cover

Total N 36

20 Test Statistic 13.193

Degree of Freedom 4

Asymptotic Sig. (2-sided test) .010

Table 4: The Kruskal-Wallis H test showed that there was a statistically significant difference in the number of Bishop Pine seedlings on Santa Rosa Island, California in 2019 per plot with different levels of crust/moss within the plot. I reject the null hypothesis and accept that there is a relationship between the levels of crust/moss and numbers of seedlings per plot.

Figure 3: Graphing these results, we see that it is a negative influence of crust/moss on Bishop Pine seedlings.

Crust/Moss Cover Rank Plots with Seedlings Plots without Seedlings Present Present

Rank 0 (none) 1 0

21 Rank 1 (trace) 2 0

Rank 2 (1-25%) 5 6

Rank 3 (25-50%) 2 5

Rank 4 (50-90%) 2 11

Rank 5 (>90%) N/A N/A

Table 5: Crust/Moss cover rank with number of plots that contain and did not contain Bishop Pine Seedlings. Note, there were no plots in the entire study area that had a rank of five for the cover class of crust/moss

Hp Slope has an effect on the amount of seedlings that germinate.

Figure 4: I found there was no significant difference in plot slope for plots that had seedlings and those without (t= 0.19, d.f. = 34, p=0.84).

22 Andrew:

Ht: Bark beetles are present throughout the Bishop Pine study area on Santa Rosa Island, CA in 2019.

Overall Infestation In 2016, none of the plots showed evidence of bark beetle infestation. Of the thirty-six plots surveyed in 2019, eleven plots, including the newly established plot, did not contain Bishop Pines. Of the twenty-five remaining plots, twelve plots (48%) contained at least one Bishop Pine that showed evidence of bark beetle infestation.

23 Figure 5: Map showing the distribution of Bishop Pine plots that are infested with bark beetles vs. uninfested plots vs. plots that contain no pines throughout the study area in relation to Santa Rosa Island, the northern Channel Islands, and mainland California.

Infestation Status of Bishop Pine Plots on Santa Rosa Island between 2016 and 2019

Subpopulation - Plot Bark Beetle Damage Bark Beetle Damage (2016) (2019)

1 - 1 No pines No pines

1 -2 NO YES

1 -3 NO NO

1 -4 NO YES

1 - 5 NO YES

1 -6 No pines No pines

1 -7 NO YES

1 - 8 NO YES

1 -9 NO YES

1 - 10 NO YES

1 - 11 NO YES

1 - 12 NO NO

1 - 13 NO NO

1 - 14 NO YES

1 - 15 No pines No pines

1 - 16 Not surveyed No pines

2 - 1 NO NO

2 - 2 NO NO

24 2 - 3 NO YES

2 - 4 NO NO

2 - 5 NO NO

2 - 6 No pines No pines

2 - 7 NO YES

2 - 8 NO YES

2 - 9 No pines No pines

2 - 10 No pines No pines

2 - 11 NO YES

2 - 12 No pines No pines

2 - 13 NO NO

2 - 14 No pines No pines

2 - 15 No pines No pines

3 - 1 NO NO

3 -2 NO NO

4 - 1 NO NO

4 - 2 NO NO

4 - 3 No pines No pines

Table 6: Delineates the presence or absence of bark beetle damage within Bishop Pine communities on Santa Rosa Island across the study area between 2016 and 2019 by study plot.

Beetle Identification The beetle’s physical characteristics, identified via microscopic examination, were used to tentatively identify the beetle’s genus as being that ofStenoscelis by Dr. Tom Dudley, pending official review.

25 Figure 6: Profile photographs of the weevil(Stenoscelis spp.) collected from Bishop Pines within the study area on Santa Rosa Island, CA.

Beetle samples, collected from underneath the bark of Bishop Pines within the study area, were tentatively identified as being members of the genus Stenoscelis by Dr. Tom Dudley, pending official review.

26 Ht: Throughout the study area, Bishop Pines with larger diameters are more susceptible to bark beetle infestation.

There is a significant difference in the size (i.e. the diameter at breast height) distribution of Bishop Pines that show evidence of bark beetle infestation compared to the size distribution of uninfested trees (N = 103, D(102) = 2.748, P = .007). Infested trees (N = 38) tend to have larger diameters (x = 15.4 cm, o = 8.9) (Figure 7), whereas uninfested trees (N = 65) tend to have smaller diameters, (x = 5.8 cm, o = 5.2) (Figure 8).

Figure 7: Distribution of the diameters of Bishop Pines in survey plots on Santa Rosa Island that showed evidence of bark beetle attack in 2019 (N = 38, x = 15.4 cm, a = 8.9).

27 Figure 8: Distribution of the diameters of Bishop Pines in survey plots on Santa Rosa Island that did not show evidence of bark beetle damage, yet were located within plots that contained at least one infested tree (N = 65, x = 5.8 cm, o = 5.2).

28 Ht: Throughout the study area, healthier Bishop Pines are less susceptible to bark beetle infestation.

Figure 9: Bark beetles disproportionally attacked trees that were unhealthy (> 50% dead) and dead (100% dead) Bishop Pines in study plots on Santa Rosa Island between 2016 and 2019. Within plots that contained at least one tree exhibiting evidence of bark beetle damage in 2019: bark beetles had infested 83.3% of the dead trees, 61.5% of the unhealthy trees, and 35.3% of the healthy trees.

Of the fifty-three trees that I could compare between 2016 and 2019, twenty-five showed evidence of bark beetle infestation in 2019. Of the twenty-five infested trees, twelve trees (48%) were <50% dead, eight trees (32%) were >50% dead, and five trees (20%) were 100% dead, prior to infestation, in 2016. Of the twenty-eight uninfested trees, twenty-two trees (79%) were <50% dead, five trees (18%) were >50% dead, and one tree (4%) was 100% dead in 2016.

The results of the chi-square test(X2 = 6.15, P = .046) indicate that there is a significant difference in the frequency of bark beetle infestation among healthy, unhealthy, and dead trees. As a result, I rejected the null hypothesis that there is not a significant difference in the frequency of bark beetle infestation of healthy (< 50% dead) vs. unhealthy (> 50% dead) vs. dead (100% dead) Bishop Pines. Unhealthy (>50% dead) and dead (100% dead) trees were predominantly associated with bark beetles throughout the study area. Within plots that contained at least one tree exhibiting evidence of bark beetle damage in 2019: bark beetles had infested 83.3% of the dead trees, 61.5% of the unhealthy trees, and 35.3% of the healthy trees.

Victoria:

Ht: In the study area, plots with higher levels of vegetation species richness experience a decrease in average Bishop Pine health class scores.

29 Figure 10: The level of species richness compared to the average tree health for twenty-five of the thirty-six plots established on Black Mountain. The other eleven plots were not included in the graph due to no Bishop Pines being found within them. Every data point represents one of the established plots.

30 Table 7: Linear regression test results between plot average health class and the level of vegetation species. Only the twenty-five plots that have Bishop Pines present were included within the test while the eleven plots without Bishop Pines were excluded.

To test the statistical significance of the plots’ average health class and vegetation species richness level, I used a linear regression test with the average health class as the dependent variable and the species richness as the independent variable. In order to make the testing more accurate, plots that had no Bishop Pines within their perimeter were excluded. The eleven plots that had no pines would make the regression test results inaccurate due to them increasing the p-values and decreasing the R Square. The regression test calculated a p-value of 0.74 and an R Square of 0.00, determining that there is no statistical significance between the average health class of Bishop Pines and the level of vegetation species richness per plot.

Ht: Higher levels of vegetation species richness leads to more needle dieback amongst the Bishop Pines in the study area.

Figure 11: The level of species richness compared to the average needle dieback percentage for twenty-five of the thirty-six plots established on Black Mountain. The other eleven plots were not

31 included in the graph due to no Bishop Pines being found within them. Every data point represents one of the established plots.

Table 8: Linear regression test results between the average needle dieback percentage and the level of vegetation species per plot. Only the twenty-five plots that have Bishop Pines present were included within the test while the eleven plots without Bishop Pines were excluded.

To test the statistical significance of the plots’ average needle dieback percentage and vegetation species richness level, a linear regression test was used with needle dieback as the dependent variable and the species richness as the independent variable. In order to make the testing more accurate, plots that had no Bishop Pines within their perimeter were excluded. The eleven plots that had no pines would make the regression test results inaccurate due to them increasing the p-values and decreasing the R Square. The regression test calculated a p-value of 0.67 and an R Square of 0.01, determining that there is no statistical significance between the average needle dieback of Bishop Pines and the level of vegetation species richness per plot.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine seedling production within the study area.

32 Figure 12: The level of species richness compared to the number of seedlings found within the perimeter of each of the thirty-six plots established on Black Mountain. All thirty-six plots were included within the graph to show if levels of species richness affects the growth of seedlings or not. Every data point represents one of the established plots.

33 Table 9: Linear regression test results between the total seedling count and the level of vegetation species per plot. All thirty-six plots were included in the test.

To test the statistical significance of the plots’ total Bishop Pine seedling count and vegetation species richness level, a linear regression test was used with seedling total as the dependent variable and the species richness as the independent variable. All thirty-six of the plots were included in the testing to compare species richness across all plots, including those without pines. The regression test calculated a p-value of 0.69 and an R Square of 0.00, determining that there is no statistical significance between the total Bishop Pine count and the level of vegetation species richness per plot.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine sapling production within the study area.

Figure 13: The level of species richness compared to the number of saplings found within the perimeter of each of the thirty-six plots established on Black Mountain. All thirty-six plots were included within the graph to show if levels of species richness affects the growth of seedlings or not. Every data point represents one of the established plots.

34 Table 10: Linear regression test results between the total sapling count and the level of vegetation species per plot. All thirty-six plots were included in the test.

To test the statistical significance of the plots’ total Bishop Pine sapling count and vegetation species richness level, a linear regression test was used with sapling total as the dependent variable and the species richness as the independent variable. All thirty-six of the plots were included in the testing to compare species richness with plots without pines. Perhaps there is a significance of plots with high species richness and no Bishop Pines. The regression test calculated a p-value of 0.35 and an R Square of 0.03, determining that there is no statistical significance between the total Bishop Pine count and the level of vegetation species richness per plot.

Grant:

Ht: There has been a decline in the health of Bishop Pines on Santa Rosa Island.

In examining 105 trees that could be verified as the same tree via tree tag between 2016 to 2019, 20 trees (19.0%) showed an improvement in health class score while 10 trees (9.5%) showed a decline in health, while 75 (71.4%) remained unchanged. Needle dieback rates declined in 38 trees and increased in 27, and remained the same in 39 with the average change being an improvement of 6.30%. The average dieback rate of all trees recorded between the years decreased from 36.8% in 2016 25.9% in 2019 showing an overall improvement in Bishop Pine Health (p=0.0030).

Ht: Some environmental factors can be positively associated with tree decline.

35 Figure 14: Correlation values (Spearman's) of various environmental conditions potentially pertaining to Bishop Pine health where p < 0.05. Correlation values shown as circles in blue where R magnitude is represented both in size, color and saturation.

Statistically significant negative correlations(p <= 0.05 ) associated with a decline in health were found with an increase in needle dieback rates (r= -0.213 p=0.033) and with diameter of the tree (r= -0.227 p=0.0244) Trees with an increase in dieback were positively correlated with Northern (r =0.220 p=0.0270) and Eastern plots(r =0.204 p=0.04l). Negatively correlated with the higher elevated plots (r =-0.259 p= 0.009). And correlated with plots with more bare ground (r=0.248 p=0.012).

36 Discussion

Eric:

Ht: Recruitment has increased without the impediment of drought and ungulates.

Previous studies on the Bishop Pine have not had the opportunity to study the Santa Rosa Island population relatively soon after a drought event has ended. The 2012-2016 drought saw low precipitation coupled with record-high temperatures that had not been seen in 1,200 years (Griffin and Anchukaitis 2014). The first sampling of this Bishop Pine population was done in 2016 and helped build a foundation of what the population was doing right about the time the drought was ending. Now, in 2019, approximately three years removed from that drought event, this sampling effort is creating a timeline of how Bishop Pines are recovering. Along with the removal of grazers in 2011 Bishop Pines now have an ability to recruit without these stressors that they have faced in the past (McEachern et al. 2009). In Table 1 we can see that following the large amount of rainfall in the “water year” for 2018 the density of seedlings in 2019 per square meter is now five times greater than it was in 2016. Being that the primary influence on the mortality of Bishop Pines is drought (Fischer et al. 2009, Taylor et al. 2019) we see that when there is sufficient precipitation there is a chance that more seedlings could germinate compared to when there is low precipitation. In Figure 2, we see variable rainfall along with variable sapling establishment. It is important to note there was a lack of data for the majority of the plots on seedlings and saplings, simply because there were none within the plots, because of this the data was not following a normal distribution. Some of this can be related to normal die off of seedling along with other environmental factors. The null hypothesis that recruitment has increased without impediment of drought and ungulates cannot be accepted because of the lack of data. With future research projects, a strong database of counts will be created that will allow for statistical tests to be run, but for now we can only look at correlated trends in the data and speculate what this population might be doing.

Ht: Different ground cover types can change the amount of seedlings that germinate.

Ground cover types can help land managers make decisions on how to plan their conservation efforts as not every plot is going to provide the optimal growing conditions for a Bishop Pine. Three ground cover types were tested to see the distribution of seedling germination across different levels of each cover class: litter, bare ground, and crust/moss. The first two cover classes, litter and bare ground, came back with insignificant results meaning that the varying levels of each of these cover types does not play a role in the amount of seedlings that germinate.

37 The varying levels of the cover class of crust/moss was significantly related to the distribution of seedlings that would germinate within a plot. When the number of seedlings was graphed against the varying levels of crust/moss (Figure 3) there is a negative trend of seedlings in relation to the increasing level of crust/moss present. The crust/moss class was mostly found on plots that consisted of rocky and and crumbly soil that did not favor the germination of a Bishop Pine seed. Some of these areas could have been areas that were heavily grazed during the ranching era that led to erosion (Anderson et al 2010). This could be the main reason as to why there is a negative trend between the number of seedlings and the level of crust/moss there is in a plot. A shortcoming in this study is the cover class ranges that were used in the study could be too large to detect an environmental difference. An example would be a cover class of four ranges from 50-90%, A large gap like that creates room for variability within the plot that the data sheet could not represent. A rank of four could be assigned to a plot with 50% or 90% cover, when in reality these create two very different conditions for the overall plot. For land managers that are going to plant Bishop Pine seeds, an area that has high levels of crust/moss should be the last choice, with more sampling efforts needed to see if litter or bare ground cover types can help with seedling germination.

Ht: Slope steepness has an effect on the amount of seedlings that germinate.

It has been shown that woodland areas on Santa Rosa Island have a greater percent change on moderate slopes compared to gentle slopes after the removal of grazers (Summers et al. 2019). The data that was collected during this study confirms that Bishop Pines prefer slopes that have a steepness in the range of 40-65%, a slope that is not too gentle nor too steep. This can be seen from Figure 4, where plots that did not contain seedlings had a full range of slopes where the majority of the plots that did have seedlings were clustered around the 40-65% range. However the mean slope between plots that did not have seedlings and those that did were not significant (p=0.84). It is important to look at the number of seedlings per plot relative to slope because the seedlings are the future of the Bishop Pine cover. If land managers know the prefered slope that the Bishop Pine likes to recruit on then they can allocate their resources to that area and be more site specific in their efforts.

Andrew:

Ht: Bark beetles are present throughout the Bishop Pine study area on Santa Rosa Island, CA in 2019.

While it is evident that bark beetles have infested the Bishop Pine community on Santa Rosa Island, as evidenced by the presence of bark beetle damage on live trees, the culprit species is yet to be identified. The beetle samples that were collected from underneath the bark of Bishop

38 Pines throughout the study area were identified as being weevils from the genusStenoscelis. Stenoscelis spp. are a group of beetles that are generally referred to as inquilines, or secondary attackers, as they are opportunistic in nature, taking advantage of the damage already wrought by another species’ initial invasion. As such,Stenoscelis spp. tend to infest dead or dying trees that have already been invaded by a more aggressive species, and are not directly associated with tree mortality (Dudley, T. 04/28/2020). Future investigation will be necessary to determine the identity of the primary invader.

Ht: Throughout the study area, Bishop Pines with larger diameters are more susceptible to bark beetle infestation.

The results indicate that Bishop Pines with larger diameters have higher levels of bark beetle infestation. These findings support the determinations of previous studies, which indicated that, in general, bark beetles are more likely to infest trees with larger diameters as they are more conducive to reproductive efforts. More specifically, trees with smaller diameters are less-likely to be infested because: (1) the infestation of smaller trees for breeding purposes may result in a decline in the population if the number of beetles required to overwhelm the trees’ defenses are in excess of the number of offspring that are produced post-infestation; (2) smaller trees have less bark separating them from the outside environment and may therefore contain higher beetle parasite densities than trees with larger diameters; and (3) smaller trees contain less phloem, i.e. fewer nutrients and less space for bark beetles to reproduce (Cameron and Billings 1988, Gargiullo and Berisford 1981, Cole and Amman 1969, Reid 1962).

Ht: Throughout the study area, healthier Bishop Pines are less susceptible to bark beetle infestation.

The results indicate that bark beetles on Santa Rosa Island predominantly attacked unhealthy and dead trees. Previous studies have discovered that bark beetles, at endemic population levels, predominantly favor unhealthy and dying trees, as their defenses are more-readily overwhelmed (Reay 2000). Infestation of healthy trees can occur, however, if conditions, e.g. drought, enable the bark beetle population to reach epidemic levels, thereby enabling the population to overwhelm the defenses of healthy trees via sheer numbers (Bentz et al. 2009). These issues may be exacerbated when the beetles are present in a Mediterranean environment, where the climatic factors that would otherwise hinder the growth of the beetle population, e.g. freezing temperatures, are absent. Given that drought severity is expected to increase in the future, land managers should consider the management of the island’s bark beetle population, as their impacts may become even more pervasive and pronounced as the Bishop Pine population becomes increasingly stressed.

39 Victoria:

Ht: In the study area, plots with higher levels of vegetation species richness experience a decrease in average Bishop Pine health.

The results of the linear regression test indicated that there is no statistical significance between the level of vegetation species richness and the average health class of each plot within our study area. Most research regarding species richness refers to its positive effects on biodiversity and productivity within ecosystems; very little research has been conducted with the negatives of species richness as its focal point. With that, this result supports the idea that species richness provides benefits to ecosystems. While the statistical test was conducted, I still question whether or not there is no correlation between the average health class of a plot and its level of vegetation species richness. When looking at Figure 10, there is an indication that species richness levels that reach beyond 10 may have a factor in Bishop Pine health and even production. The graph shows that the plot with the lowest average health class of 0.5 also has a species richness level of 10, the highest level within this particular graph. While this particular graph only included plots with Bishop Pines within their perimeters, there were a total of eight plots not included which all had species richness levels higher than 10 and each of them had no signs of Bishop Pine growth. It is worth noting that all eight of those plots reached species richness levels of nearly 20 and no pines were found within them. Perhaps having high numbers of various plant species made it difficult for the Bishop Pines to find space to grow. Due to this fact, negative effects of species richness should not be ruled out when discussing what could be a factor in Bishop Pine production on Santa Rosa Island.

Ht: Higher levels of vegetation species richness leads to more needle dieback amongst the Bishop Pines in the study area.

After conducting the linear regression test on the average needle dieback percentage and vegetation species richness per plot, no statistical significance was found between the two variables. When looking at the graph, all of the plots appear to be scattered with no linear line showing a drastic increase in needle dieback as species richness increases. While the plots do not appear to be linear, there appears to be a cluster of plots toward the bottom of the graph, between the species richness levels of 4 and 10. When testing to see any negatives of species richness, the focus was on finding patterns with the data collected. This clustered appearance of the plots might seem insignificant yet it could also be a small indicator that species richness might have an affect on the health of the Bishop Pines on Santa Rosa Island.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine seedling production within the study area.

40 After conducting the linear regression test on the total number of seedlings and the level of vegetation species richness per plot, no statistical significance was found. The R Square value was calculated to be 0.00, showing that there were no plots close enough to resemble a line or be fitted along a regression line to be considered significant. However, as stated before, this research that is focused on finding the negatives of high species richness is based on finding patterns amongst the data collected. While the regression test calculated there to be no significance, the graph appears to say the opposite. In the graph, there are a few plots that appear to make a positive incline. However, the highest point of the incline was a plot that had sixty-three seedlings and a species richness level of 10. Plots that have species richness levels higher than 10 have less than five seedlings growing within their perimeters. The graph appears to show a decline in Bishop Pine production once the species richness levels exceeds 10. Perhaps coincidentally, when looking at the average heath class of the pines data, all eight of the plots without pines in them had species richness levels higher than 10. While the regression tests showed no statistical significance between species richness and seedling production, there is a significant change in plot “health” as the species richness expands beyond 10.

Ht: Higher levels of vegetation species richness leads to a decrease in Bishop Pine sapling production within the study area.

The linear regression test that was used showed no statistical significance between a plot’s presence of saplings and its level of vegetation species richness. With the R Square value measuring 0.03, there were no plots close enough to resemble a line, let alone be in close proximity to a fitted regression line. When graphed, the plots are mostly scattered. Although it was proven that there was no statistical significance between the sapling total and species richness, the graph does have noteworthy characteristics. Similar to the graph with seedlings as the dependent variable (Figure 12), Figure 13 shows a significance to the species richness level of 10. Once a plot’s species richness expands beyond 10, there is an immediate plummet in sapling production. Every plot with a species richness level higher than 10 has no sapling present. As stated previously, the graphs representing the average health class (Figure 10) and the seedling totals (Figure 12) per plot had similar findings. The species richness level of 10 appears to be a significant number such as a turning point. Once the species richness expands beyond 10, the dependent variables that were used as “health” indicators (i.e. average health class, seedling total, and sapling tota) experience a sudden decrease.

General Species Richness Discussion: Overall, the linear regression tests conducted on each of the four “health” indicators I used (i.e. average health class, needle dieback percentage, seedling total, and sapling total) showed no statistical significance with the plots’ vegetation species richness levels. However,

41 while statistics failed to find any correlation between poor Bishop Pine health and high species richness levels, the visual representations of the data in each of the four graphs showed very distinct similarities between one another. Visually, the graphed data showed a decline in Bishop Pine health once the species richness level expanded beyond 10. Whether it be a coincidence or not, it is important to take note of this pattern and continue more research in the future. Over years of species richness research, the percentage of studies that discuss its negative effects is almost always significantly lower than the number of positive studies (Waide et al 1999). More research on the negatives of species richness needs to be conducted in order to gain better knowledge on the subject and to better understand all aspects of this ecological measurement.

Grant:

Ht: There has been a decline in the health of Bishop Pines on Santa Rosa Island. Considering that the more trees showed an increase in health and the average dieback percentage decreased between 2016 and 2019, Bishop Pine health on Santa Rosa Island had increased from 2016 to 2019. The end of the drought may have brought enough novel growth to the trees to lower the apparent percentage of dead needles seen on trees. Showing that dieback percentage may be more responsive to change and a better tool for monitoring health in the continuation of this project. Health class did not change significantly between study years; this parameter may not provide adequate granularity to identify trends. Additional parameters of tree health could be used such as needle moisture content, to better monitor changes in tree health.

Ht: Some environmental factors can be positively associated with tree decline. Three factors were associated with the increase of needle dieback: Easternness and Northemness both positively correlated with the decline in health, bare ground also positively correlated. The summarizations of topography correlating with a decline in tree health indicates that trees on the opposite side of the mountain from the coastal fog may have a higher susceptibility to environmental variation. Factors not pertaining to health that show strong correlations were the relationship between elevation and the density of the plot. The higher dense plot higher on the mountain should be explained by the availability of water caused by fog drip being more prevalent on the upper parts of the mountain. Eastness also correlated negatively with elevation as most of the lower level plots were on the eastern part of the mountain and thus down the hill from the rest of the plots. Fog drip is known as a large component in Bishop Pine water intake (Baguskas et. al. 2016) and factors that influence water intake should have a large effect on tree health. This is also represented in higher elevation being negatively correlated with needle dieback. Possible further study of the amount of fog drip these plots receive could further confirm this. Bare ground also positively correlated with an increase in needle dieback.

42 Conclusion Eric: Suggestions for Future Research

Precipitation, ground cover types, and slope steepness all play a role in how the dynamics of the bishop pine population is fluctuating. Although statistical tests could not be run and questions be answered for the sapling establishment data, this information will help land managers down the line as more data is collected they can begin to see what really is happening. For ground cover types it was observed that there was a negative relationship between seedlings that would germinate and the amount of crust/moss; most of the plots containing Bishop Pines were in the 40-65% crust/moss range. For future research I recommend that researchers address why Bishop Pine seedlings do not favor a high level of crust/moss. Could it be that these are eroded steep slopes that have not had the opportunity to regenerate or could it be something else? I also recommend that future researchers continue seedling and sapling counts to continue a timeline of recruitment as this will be the most helpful information when trying to understand if the population is rebounding. All this information can give insight to land managers to make more informed decisions on what to do on Santa Rosa Island for the Bishop Pine Population. From my research, the most suitable habitat to reintroduce Bishop Pine seedlings would be on areas that had slopes between 40-65%. As well as areas that have low levels of crust/moss should be considered as suitable habitat for Bishop Pine seedlings.

Andrew: Suggestions for Future Research

While it is evident that bark beetles have infested the Bishop Pine community on Santa Rosa Island, further investigation is necessary to identify the culprit species so that land managers can better-predict the potential impacts that the beetle population may have on the Bishop Pine population and, subsequently, decide whether it is necessary to manage the beetle population. Beetle samples should be collected from healthy trees along with unhealthy and dead trees on Santa Rosa Island using proven methods, e.g. pheromone traps. Additionally, surveys for bark beetle infestation should be included in both the long term Bishop Pine and Torrey Pine demography surveys in order to monitor for change in the geographic extent of the island’s bark beetle population. Some trees on the island may be serving as reservoirs for the beetle population, particularly those that are unhealthy or dead, with larger diameters. In the event that land managers consider it necessary to manage the beetle population, they may want to consider the removal of trees that meet the above criteria, as they could facilitate a resurgence of the beetle population in the future. Management of the beetle population could also be accomplished by more-direct methods, e.g. establishment and treatment of trap trees, or pheromone traps. Pheromone traps, in particular, have been used, with great success, to reduce

43 the level of infestation throughout Torrey Pines State Natural Reserve in San Diego (USDA 1995).

Victoria: Suggestions for Future Research

While “health” is a broad term that could fluctuate based on various factors, the average health class, needle dieback percentage, seedling count, and sapling count were all used as “health” indicators and were proven to have no negative correlation with high species richness. The tests resulted in p-values no less than 0.35 when p-values need to be less than 0.05 in order to be considered significant. When graphed, the data of each of the four tests showed a possible significance with the species richness level 10. However, more research needs to be conducted to better understand why this is. This research was conducted solely by looking with a naked eye at what different plant species were found within the plots’ perimeters. In order to delve deeper into the topic of negative effects of species richness, future research should be conducted at the microscopic and chemical level, including looking at mycorrhizae of the pines’ roots and local fungi. Past studies have found both positive and negative effects that species richness have on the carbon cycle and how the biomass of plants is affected due to these changes (Chen et al 2017, Carbone et al 2012). Looking at the carbon abundance in soil and the species richness levels per plot could bring interesting results about how species richness affects the Bishop Pines’ health. Along with this, conducting the same 2019-2020 research in future years might prove to be beneficial. The research conducted in 2016 did not focus on species richness and its negative effects on the Bishop Pines, therefore, no comparisons could be made between the 2016 and 2019 data. In order to see if any changes occur throughout the years, as well as with any future drought events, the same procedures and research should be conducted. The effects that species richness can have on plant productivity can change based on the year as well as the weather (Yuan et al 2015). By continuing this research in future years, as well as adding new procedures and factors to it, the negative effects of high species richness on the Bishop Pines may present itself and prove to be more statistically significant throughout the years.

Grant: Suggestions for Future Research

Now that it has been established that the overall tree health has improved between 2016 and 2019, and that there are distinct environmental characteristics correlated with the changes in health, additional factors should be examined. In the continuation of these demography studies additional parameters could be measured such as plot contour and hill slope position (Amatulli et. al. 2018). Tree health class was not a sensitive enough measurement to reflect the same trend found in needle dieback, and another measurement of tree health can be used to provide better quantifiable changes in tree health. To examine whether fog moisture varies between plots and whether the intake of water by Bishop Pines are affected by plot characteristics, a study during

44 the summertime when these fog events happen would need to be conducted. For management implications of these results, additional attention during Bishop Pine expansion planning should be given to the hill slope and azimuth of proposed projects.

Acknowledgements Special thanks to Russell Bradley, Joseph Forrest, Aspen Coty, and Robyn Shea with the CSU Channel Islands Santa Rosa Island Research Station, Kathryn McEachem with the U.S. Geological Survey, Stephen Bednar with the Mountains Restoration Trust, and Dr. Tom Dudley with UC Santa Barbara for their contributions and guidance during this project. We would also like to thank the Channel Islands National Park, Havasi Wilderness Foundation, Judith Staplemann Foundation, Matthew Hillman Fisher Foundation, Southern California Edison, and Keith Wescott for assisting in the provision of transportation, lodging, field supplies, equipment, and other relevant services that enabled us to successfully conduct research on Santa Rosa Island. Finally, we would like to thank Dr. Clare Steele, Dr. Dan Reineman, and the Environmental Science and Resource Management (ESRM) Department for supporting our research, as well as Zachary Buckley and Brice Pritchard for their 2016 survey data. This research was conducted under research permit CHIS-2020-SCI-0005 from the Channel Islands National Park.

Literature Cited

Anderson, S. R., Starratt, S. Brunner Jass, R. M., Pinter, N. 2010. Fire and vegetation history on Santa Rosa Island, Channel Islands, and long-term environmental change in southern California. Journal o f Quaternary Science. 25:782-797

Allen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, E. H. (Ted. Hogg, P. Gonzalez, R. Fensham, Z. Zhang, J. Castro, N. Demidova, J. H. Lim, G. Allard, S. W. Running, A. Semerci, and N. Cobb. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660-684.

Amatulli, G., S. Domisch, M.-N. Tuanmu, B. Parmentier, A. Ranipeta, J. Malczyk, and W. Jetz. 2018. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data 5.

45 Baguskas, S., C. Still, D. Fischer, C. D’Antonio, and J. King. 2016. Coastal fog during summer drought improves the water status of sapling trees more than adult trees in a California pine forest. Oecologia 181:137-148.

Bentz, B., J. Logan, J. MacMahon, C. D. Allen, M. Ayres, E. Berg, A. Carroll, M. Hansen, J. Hicke, L. Joyce, W. Macfarlane, S. Munson, J. Negron, T. Paine, J. Powell, K. Raffa, J. Regniere, M. Reid, B. Romme, S. J. Seybold, D. Six, D. Tomback, J. Vandygriff, T. Veblen, M. White, J. Witcosky, and D. Wood. 2009. Bark beetle outbreaks in western North America: Causes and consequences.Bark Beetle Symposium A2.

Buckley, Z. W. 2016. Comparing germination rates between Island and Mainland populations of Pinus muricata: A Fire Ecology Study. CSUCI Capstone thesis.

Callaway, R., M. and Davis, F., W., 1993. Vegetation Dynamics, Fire, and the Physical Environment in Coastal Central CA. Ecology 74:1567-1578.

Cameron, R. S., and R. F. Billings. 1988. Southern Pine Beetle: Factors Associated with Spot Occurrence and Spread in Young Plantations. Southern Journal of Applied Forestry 12:208-214.

Chen, H., Mommer, L., Ruijven, J., Kroon, H., Fischer, C., Gessler, A., Hildebrandt, A., Scherer-Lorenzen, M., Wirth, C., Weigelt, A., and Nina Wurzburger. 2017. Plant species richness negatively affects root decomposition in grasslands.Journal of Ecology 105:209-218.

Choat, B., S. Jansen, T. J. Brodribb, H. Cochard, S. Delzon, R. Bhaskar, S. J. Bucci, T. S. Feild, S. M. Gleason, El. G. Hacke, A. L. Jacobsen, F. Lens, H. Maherali, J. Martinez-Vilalta, S. Mayr, M. Mencuccini, P. J. Mitchell, A. Nardini, J. Pittermann, R. B. Pratt, J. S. Sperry, M. Westoby, I. J. Wright, and A. E. Zanne. 2012. Global convergence in the vulnerability of forests to drought.Nature 491:752-755.

Cole, W.E.; Amman, G.D. 1969. Mountain pine beetle infestations in relation to lodgepole pine diameters. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research paper INT-96.

Fischer, D. T., C. J. Still, and A. P. Williams. 2009. Significance of summer fog and overcast for drought stress and ecological functioning of coastal California endemic plant species. Journal of Biogeography 36:783-799.

46 Gargiullo, P. M., and C. W. Berisford. 1981. Effects of Host Density and Bark Thickness on the Densities of Parasites of the Southern Pine Beetle. Environmental Entomology 10:392-399.

Griffin, D. and K. J. Anchukaitis. 2014. How unusual is the 2012-2014 California drought? Geophysical Research Letters 41:9017-9023.

Grogan, P., J. Baar, and T. D. Bruns. 2000. Below-ground ectomycorrhizal community structure in a recently burned bishop pine forest.Journal of Ecology 88:1051.

Grogen. P., T. D. Bruns, and F. S. Chapin III. 2000. Fire Effects on Ecosystem Nitrogen Cycling in a Californian Bishop Pine Forest.International Association for Ecology 122:537-544

Hsiao, T.C., and L. K. Xu. 2000. Sensitivity of growth of roots versus leaves to water stress: biophysical analysis and relation to water transport.Journal of Experimental Botany 51:1595-1616.

Jucker, T., O. Bouriaud, D. Avacaritei, I. Danila, G. Duduman, F. Valladares, D. A. Coomes, and M. Turnbull. 2014. Competition for light and water play contrasting roles in driving diversity-productivity relationships in Iberian forests.Journal of Ecology 102:1202-1213.

Kerhoulas, P. L., Kolb T. E., Koch, W. G. 2013. Tree size, stand density, and the source of water used across seasons by ponderosa pine in northern Arizona.Forest Ecology and Management 289:425-433

Kolb, T. E., M. L. Gaylord, W. T. Pockman, J. A. Plaut, E. A. Yepez, A. K. Macalady, R. E. Pangle, and N. G. McDowell. 2013. Drought predisposes pinon-juniper woodlands to insect attacks and mortality. New Phytologist 198:567-578.

McEachem, A. K., D. M. Thomson, K. A. Chess, A. K. Mceachern, D. M. Thomson, and K. A. Chess. 2009. Climate Alters Response of an Endemic Island Plant to Removal of Invasive Herbivores.Ecological Applications 19:1574-1584.

McEachem, K., T. Atwater, P.W. Collins, K. Faulkner, and D. V. Richards. 2016. Chapter 34 Managed Island Ecosystems. Pages 755-778. H. A. Mooney and E. Zavaleta, editors. Ecosystems o f California. University of California Press, Oakland, California.

Neumann, F. G. 1987. Introduced bark beetles on exotic trees in Australia with special reference to infestations ofIps grandicollis in pine plantations. Australian Forestry 50:166-178.

47 Paine, T. D., and F. Lieutier. 2016. Insects and Diseases of Mediterranean Forest Systems. Page (T. D. Paine and F. Lieutier, Eds.) Insects and Diseases of Mediterranean Forest Systems. Springer International Publishing, Cham.

Pritchard, B. 2016. Current Weather Patterns and Removal of Non-native Ungulates Dictates Survivability of the Bishop Pines Population on Santa Rosa Island. CSUCI Capstone thesis.

Raffa, K. F., B. H. Aukema, B. J. Bentz, A. L. Carroll, J. A. Hicke, M. G. Turner, and W. H. Romme. 2008. Cross-scale Drivers of Natural Disturbances Prone to Anthropogenic Amplification: The Dynamics of Bark Beetle Eruptions.BioScience 58:501-517.

Rastogi, B., A. P. Williams, D. T. Fischer, S. F. Iacobellis, K. McEachem, L. Carvalho, C. Jones, S. A. Baguskas, and C. J. Still. 2016. Spatial and Temporal Patterns of Cloud Cover and Fog Inundation in Coastal California: Ecological Implications.Earth Interactions 20:1-19.

Reay, S. 2000. Aspects of the ecology and behaviour of Hylastes ater (Paykull) (Coleoptera: Scolytidae) in second rotation Pinus radiata forests in the central North Island, New Zealand, and options for control.

Reay, S. D., J. M. Thwaites, R. L. Farrell, and P. J. Walsh. 2001. The role of the bark beetle, Hylastes ater (Coleoptera: Scolytidae), as a sapstain fungi vector to Pinus radiata seedlings: A crisis for the New Zealand forestry industry?Integrated Pest Management Reviews 6:283-291.

Reay, S. D., T. R. Glare, and M. Brownbridge. 2012. Hylastes ater (Curculionidae: Scolytinae) Affecting Pinus radiata Seedling Establishment in New Zealand. Psyche: A Journal of Entomology 2012:1-9.

Reid, R. W. 1962. Biology of the Mountain Pine Beetle,Dendroctonus monticolae Hopkins, in the East Kootenay Region of British Columbia II. Behavior in the Host, Fecundity, and Internal Changes in the Female. The Canadian Entomologist 94:605-613.

Rick, T. C., T. S. Sillett, C. K. Ghalambor, C. A. Hofman, K. Ralls, R. S. Anderson, C. L. Boser, T. J. Braje, D. R. Cayan, R. T. Chesser, P. W. Collins, J. M. Erlandson, K. R. Faulkner, R. Fleischer, W. C. Funk, R. Galipeau, A. Huston, J. King, L. Laughrin, J. Maldonado, K. McEachem, D. R. Muhs, S. D. Newsome, L. Reeder-Myers, C. Still, and S. A. Morrison. 2014. Ecological Change on California’s Channel Islands from the Pleistocene to the Anthropocene. BioScience 64:680-692.

48 Rogers, C., A. Aylard, M. James, and C. Rivas. 2019. 2018-19 Population Census of the Island Torrey Pines Pinus ( torreyana ssp. insularis) on Santa Rosa Island, CA. CSUCI Capstone thesis.

Simonin, K., Kolb, T., Montes-Helu, M., Koch, G., 2007. The influence of thinning on components of stand water balance in a ponderosa pine forest stand during and after extreme drought. Agricultural and Forest Meteorology 143, 266-276.

Summers, R., J. Masukawa, and B. D. Hartman. 2019. The Influence of Slope on Vegetation Recovery Following Nonnative Grazer Removal on Santa Rosa Island, California. Western North American Naturalist 78:787.

Taylor, A., T. Biswas, J. M. Randall, K. Klausmeyer, and B. Cohen. 2019. Parched pines: a quantitative comparison of two multi-year droughts and associated mass mortalities of bishop pine {Pinus muricata) on Santa Cruz Island, California. Remote Sensing in Ecology and Conservation 5:1-15.

United States Department of Agriculture, Forest Service, Intermountain Research Station (USDA). 1995. Application of Semiochemicals for Management of Bark Beetle Infestations: Proceedings of an Informal Conference: Annual Meeting of the Entomological Society of America. Issue 318 of general technical report INT, Intermountain Forest and Range Experiment Station (Ogden, Utah).

United States Department of Agriculture (USDA). 2015. Bark Beetles in California Conifers.

United States Department of Agriculture (USDA). 2019. Survey Finds 18 Million Trees Died in California in 2018. Press Release.

Waide, R. B., Willig, M. R., Steiner, C. F., Mittelbach, G., Gough, L., Dodson, S. I., Juday, G. P., and R. Parmenter. 1999. The relationship between productivity and species richness. Annual Review of Ecology and Systematics 30:257-300.

Wang, Zhenhong. 2017. Process strengths determine the forms of the relationship between plant species richness and primary productivity.Plos One 12:1-22.

Williams, A. P., C. J. Still, D. T. Fischer, and S. W. Leavitt. 2008. The influence of summertime fog and overcast clouds on the growth of a coastal Californian pine: a tree-ring study. Oecologia 156:601-611.

49 Yuan, Z., Yu, K., Epstein, EL, Stefanova, K., and Rong Zhang. 2015. Plant species richness is not consistently associated with productivity in experimental subalpine meadow plant communities.Folia Geobotanica 50:207-217.

50 Appendix

51 Figure 15: Data collection sheet that was used to collect data during the 2019/20 Bishop Pine demography survey on Santa Rosa Island, CA.

Field Equipment

- Data Collection Protocol Old Datasheets - New Blank Datasheets Clipboards - Pencils - List of Plot Coordinates GPS with pre-programmed plot coordinates - Pin-Flags - Flagging Tape Candy Canes Aluminum Nails Tree Tags Hammer Compass Clinometer - Fiberglass Transect Tape - Diameter Tape Camera Cell Phone - CSUCI Radio - First Aid Kit - EOS Arrow GPS - Handheld 41b Sledgehammer - Rebar Stamp Kit Aluminum Plot Stakes Gaia (smartphone app) 5ml Plastic Vials Ethanol Sharpie marker Pocketknife

52 Data Analysis Materials

- IBM SPSS Statistics (v26) ArcGIS Pro v2.3.3 Microsoft Excel vl808 Chi Square Calculator (socscistatistics.com) - Bark Beetle Genera of the United States - Identification Key (idtools.org) Olympus (model SZ61), integrated with a digital camera (Lumenera Infinity 2), under 40x magnification

Species List

- Bishop pine - Brandegee’s sage - Brown fungus spp. California buttercup California huckleberry California polypody Carex spp. Chamise Common chickweed Common yarrow - Dudleya spp. Goldback fern Golden yarrow Grass spp. Indian pink Ironwood Island bedstraw Island broom Island deerweed Island jepsonia Island live oak Island scrub oak - Lichen spp. Live-forever Manzanita Monkey flower

53 - Moss spp. Orange fungus spp. - Prickly lettuce - Pseudognaphalium spp. Santa Cruz Island buckwheat Silver lupine Split gill fungus Toy on - White fungus spp. - Wild cucumber - Wild rye Yarrow

54