Tree Mortality in the Yosemite Forest Dynamics Plot: 2013

Brian Moe

Dr. Jim Lutz

Senior Capstone

Environmental Studies

Program on the Environment

University of Washington

Spring 2014

  2  

Abstract

Tree Mortality in the Yosemite Forest Dynamics Plot: 2013

Brian Moe

Capstone Advisor: James A. Lutz, YFDP Principal Investigator and Assistant Professor

Utah State University, Wildland Resources

I participated in a field crew that identified and recorded mortality data for all tree species in the

Yosemite Forest Dynamics Plot in 2013. I used mortality data from the 2013 annual mortality check to

evaluate the presence of factors associated with tree mortality on the 25.6-hectare plot. I calculated

overall annual mortality rate and mortality rates for each of the individual species present in the old

growth, mixed conifer plot. I also calculated mortality rates by diameter class and factor associated with

death. For the two dominant species, Abies conolor (white fir) and Pinus lambertiana (sugar pine), I

separated mortality causes, determining the number of individuals that appeared to die due to singular

and/or multiple causes. A. concolor and P. lambertiana mortality rates both increased in 2013, to 1.67%

and 2.66% respectively. The overall mortality rate in the plot was 1.69%, with only 4 of the 491 (0.8%)

large trees dying this year. Insects were the most common agent of mortality for all trees ≥10 cm dbh,

while stress was the most common agent for the smallest class of trees, between 1-10 cm dbh. Insect and

fungus-related mortalities are not independent of each other, the presence of one agent more often leading

to the presence of the other. The high mortality rates of P. lambertiana continue a trend of increasing

death for that species, possibly because of the historical absence of fire in the plot. The positive

relationship of beetle and fungus-related mortality highlights a particular susceptibility that forests may

have to mortality events; a shift in the abundance of one mortality agent leading to an increase in another.

Further data collection and analysis is needed in the YFDP to confirm trends in mortality, and comparison

to ingrowth data is needed to see if the populations of certain tree species are decreasing as mortality rates

suggest.

Introduction Dead trees remain a crucial part of the structure of forest ecosystems, providing habitat for

animals and other plants. Understanding tree mortality is vital to understanding the long-term dynamics of

a forest ecosystem. While there are many methods that attempt to quantify mortality trends in forests,

  3  

most fall short for a variety of reasons. Annual mortality checks at large permanent plots, like the YFDP,

are the best way to measure mortality because they provide an opportunity to recognize the effects of

competition, biological agents, and physical disturbances on the structure of the forest over time through

direct evidence.1

Tree mortality is a complex and often drawn-out process, caused by multiple factors that work

together to result in the death of an individual.2 These factors can work together to kill trees in very

different ways. Competition for resources amongst trees is a constant factor to consider, but research

shows that competition is likely not the governing process of mortality in old-growth forests.3

Some disturbances, such as fire and wind, can vary in intensity and severity, leading to different

changes to the forest structure. Small fires can cause canopy openings over small areas (

  4  

The beetles and fungi have a mutually beneficial lifestyle, but which pathogen paves the way for the other

is unknown.

This paper will calculate annual mortality rates for species in the YFDP, and attempt to quantify

the effects of bark beetles and fungi on tree mortality. The latter will be done by comparing the presence

of each in the mortalities of the two most abundant species in the YFDP: Abies concolor and Pinus

lambertiana. The objectives of this paper are (1) to establish baseline mortality data for the 2012-2013

season in the YFDP, and (2) to determine whether fungus and beetle-induced mortalities of P.

lambertiana and A. concolor are independent or related to each other.

Study Area Physical Area

The Yosemite Forest Dynamics Plot (YFDP) is a 25.6 ha research forest plot located near Crane

Flat in Yosemite National Park. It is centered at 37.77˚N, 119.82˚W, shown in Figure 1.12 It ranges in

elevation between 1774.1 m and 1911.3 m above sea level. It is the largest permanent monitoring plot in

the National Park System and research is completed cooperatively through Utah State University, the

University of Montana, Washington State University, and the University of Washington, in accordance

with the Smithsonian’s Center for Tropical Forest Science protocol.

Soils in the YFDP are composed of metamorphic parent material. The water-holding capacity

ranges from 70-1560 mm in the top 150 cm of the soil. The climate is Mediterranean, with cool moist

winters and long dry summers. Mean temperatures range from 12.2˚C – 26.1˚C in July and -2.7˚C – 9.4˚C

in February. Average annual precipitation is 106 cm, with most of that falling as snow during the winter

months. Summer usually brings with it a summer drought.13

                                                                                                                         12  Lutz  et  al.  2012.  13  Lutz  et  al.  2012.  

Figure 1. Yosemite Forest Dynamic Plot map

  5  

Vegetation

The plot was established during 2009 and 2010 when all live trees ≥ 1 cm in diameter at breast

height (1.37 m; dbh) were tagged and mapped.14 Following the 2013 mortality check there were a total of

35,499 live stems in the plot. The plot is located in an old-growth Sierra Nevada mixed conifer forest

dominated by Abies concolor (white fir) and Pinus lambertiana (sugar pine). These two species make up

93% of the above ground biomass and 84% of the total individual stems in the plot.15 The plot is also

home to Cornus nuttallii (Pacific dogwood), Calocedrus decurrens (incense cedar), Quercus kelloggii

(California black oak), Prunus virginiana (chokecherry), Prunus emarginata (bitter cherry), Salix

scouleriana (Scouler's willow), Abies magnifica (red fir), Rhamnus californica (coffeeberry), Pinus

ponderosa (ponderosa pine), and Pseudotsuga menziesii (Douglas-fir); as well as several species of

shrubs and herbs. The summer of 2013 was the plot’s third consecutive full mortality check.

Insects and Fungi

P. lambertiana and A. concolor have each coevolved with different bark beetles that are always

present at low levels.16 P. lambertiana is attacked by Dendroctonus ponderosae (mountain pine beetle)

and D. valens (red turpentine beetle). Two beetle galleries found in the 2013 inventory were identified

and recorded as D. brevicomis, though this is likely an error as D. brevicomis is not known to attack P.

lambertiana.17 These two individuals will be verified in 2014. A. concolor is attacked by Scolytus

ventralis and S. subscaber. These species are common enough to contribute to tree mortality within the

plot.

The plot is host to many different fungi. Armillaria ostoyae root rot and Cronartium ribicola

(white pine blister rust) are the most common pathogens to play roles in tree mortality. Armillaria can

attack Abies, Prunus, and Cornus spp. It travels through the roots so it tends to be found in patches.18

White pine blister rust is limited to only five-needled pines, including the sugar pine. Blister rust enters

pines through the needles and leads to branch swelling, needle dieback, and the production of cankers and

fruiting bodies.19 Small sugar pines can be killed by the blister rust but larger individuals will generally

survive, becoming more susceptible to beetle infestation later on. Echinodontium tinctorium (Indian paint

fungus) and Heterobasidion annosum (annosum root disease) are also common fungi found in the plot,

though they are rarely associated with mortality.

                                                                                                                         14  Lutz  et  al.  2012.  15  Lutz  et  al.  2012.  16  Lutz  et  al.  2012.  17  Furniss.  1977.  18  Lutz  et  al.  2012.  19  Van  Mantgem  et  al.  2004.  

  6  

Disturbance History

The plot has not burned since fire records began for the area in 1930. The pre-Euro-American fire

return interval for the YFDP was 10-13 years. Fire exclusion can cause many changes in an ecosystem;

most notably an increase in stand density.20 There is evidence that this increase in density is likely to

result in an increase in insect and pathogen outbreaks as well as an increase in competition-related

mortalities.21 However, this increase in density cannot completely explain all increases in mortality

rates.22

Methods Field Sampling

Field crews were assembled to carry out all of the data collection in the summer of 2013 in the

YFDP. Reference Lutz et al (2012) for any inquiries about the initial establishment of the plot and field

procedures.

A full inventory of the plot was completed by finding every tagged tree in the plot and checking

vigor. Mortality checks were performed on any new dead trees that were not already in the database.

Mortality checks include confirmation of species; confirmation of tag number; assessing the stem and

root condition; measurement of dbh, and; measurement of height. We then identified factors associated

with death by looking for beetle frass, entry and exit holes, fungal fruiting bodies, mistletoe, and any

visible mechanical damage. A bark plate was then removed from each dead tree to reveal the sapwood,

which was examined for beetle galleries and mycelial fans. A patch of bark was also removed below the

surface of the soil to look for evidence of root rot. The tree tag was then hammered into the tree to

confirm that the mortality check had been completed. All data was recorded on a mortality sheet in the

field and collected at the end of each day. The inventory was completed by breaking the plot into 20 m x

20 m grid cells, and teams worked one grid cell at a time.

Upon the completion of the 2013 field season all mortality data was entered into an Excel

spreadsheet and collected into the YFDP database. The codes and comments were interpreted to

determine the FADs (factors associated with death) associated with each mortality. These FADs were

placed into five categories: stress, insect, disease, mechanical, and unknown. Insect and disease FADs

were categorized based on evidence of bark beetles and fungi or root rot. Mechanical FADs included

physical damage to the tree such as crushing, snapping, or uprooting. Stress was usually competition

                                                                                                                         20  Cocke  et  al.  2005.  21  Van  Mantgem  et  al.  2009.  22  Van  Mantgem  et  al.  2004.  

  7  

related, having to do with the tree most likely being outcompeted for sunlight and water resources. All

trees that showed little or no evidence of biotic and mechanical damage, and were in a location where

competitive stress seemed plausible, were marked as stress-related mortalities. It is very likely that some

trees marked as only stress-related mortalities also died due to some biotic factors that were not obvious.23

All FADs that were evident were recorded because mechanical and biotic factors often work together to

weaken and kill a tree.24

Analysis

Mortality rates were then determined using the equation: m = 100 (n / x), where m = mortality

rate, n = number of mortalities, and x = number of live stems in 2012. Mortality rates were determined for

each individual species, each individual diameter class, and each individual FAD class for the entire plot.

Diameter classes were separated into four categories: 1cm ≤ dbh < 10 cm, 10 cm ≤ dbh < 50 cm, 50 cm ≤

dbh < 100 cm, and dbh ≥ 100 cm. This diameter class grouping was used to maintain consistency with

previous studies on mortality within the YFDP and to allow for comparison to those studies.

Further investigation was considered for the two primary species in the YFDP, A. concolor and P.

lambertiana, with identical analysis being carried out for both. Individual mortality rates based on

diameter class and FAD were found for each of these species of interest. Then, an isolation of mortality

factors was completed by separating trees that died due to single factors from those that died due to

compounding factors. This was done so that a chi-squared contingency table could be computed and the

Kolmogorov-Smirnov goodness of fit test could be run to determine if beetle and disease-related

mortality factors were dependent or independent of each other.

Results Mortality rates were analyzed by species, diameter class, and primary factors associated with

death (FAD). The two species with more than 30 mortalities, A. concolor and P. lambertiana were also

considered for further investigation. The overall mortality rate for the plot from 2012-2013 was 1.69%.

Mortality rates by species are shown in Table 1. Of the species with populations greater 1000, C.

decurrens had the lowest mortality rate, and P. lambertiana had the highest, indicative of their slow and

rapid growth rates, respectively.

                                                                                                                         23  Van  Mantgem  et  al,  2004.  24  Franklin  et  al.  2007.  

  8  

Mortality by FAD and DBH

1 cm ≤ DBH < 10 cm 10 cm ≤ DBH < 50 cm

50 cm ≤ DBH < 100 cm

DBH ≥ 100 cm Totals

n m = n / 456 n m = n / 131 n m = n / 8 n m = n / 4 n m = n / 599 Stress 210 46.1% 22 16.8% 2 25.0% 0 0.0% 234 39.1% Disease 66 14.5% 31 23.7% 1 12.5% 0 0.0% 98 16.4% Insect 115 25.2% 81 61.8% 5 62.5% 1 25.0% 202 33.8% Mechanical 161 35.3% 46 35.1% 2 25.0% 0 0.0% 209 34.9% Unknown 92 20.2% 11 8.4% 1 12.5% 3 75.0% 107 17.9% Table 3. Total mortality for YFDP 2013 by FAD and diameter class.

Diameter class Total Mortalities (n)

Population (x)

Mortality Rate (m = n / x)

Proportion of Mortalities (n / 599)

1 cm ≤ DBH < 10 cm 456 21,933 2.1% 76.1% 10 cm ≤ DBH < 50 cm 131 11,810 1.1% 21.9% 50 cm ≤ DBH < 100 cm 8 1,265 0.6% 1.3% DBH ≥ 100 cm 4 491 0.8% 0.7% Totals 599 35,499 1.7% 100% Table 2. Total mortality for YFDP 2013 by diameter class.

Species Mortality Rate (m)

Mortalities (n)

Living Stems in 2012 (x)

Abies concolor (white fir) 1.67% 417 25,037 Abies magnifica (red fir) 0.00% 0 9 Calocedrus decurrens (incense cedar) 0.24% 4 1,641 Cornus nuttallii (Pacific dogwood) 0.99% 26 2,617 Corylys cornuta var. californica (western beaked hazelnut) 0.00% 0 1

Pinus lambertiana (sugar pine) 2.66% 130 4,895 Pinus ponderosa (ponderosa pine) 0.00% 0 2 Prunus emarginata (bitter cherry) 10.0% 2 20 Prunus virginiana (chokecherry) 5.13% 6 117 Pseudotsuga menziesii (Douglas-fir) 0.00% 0 6 Quercus kelloggii (California black oak) 1.14% 13 1,136 Rhamnus californica (coffeeberry) 0.00% 0 13 Salix scouleriana (Scouler's willow) 20.0% 1 5 Total 1.69% 599 35,499 Table 1. Total mortality for each species present in YFDP 2013.

  9  

Mortality based on diameter class is listed in Table 2, and mortality based on the factors

associated with death is listed in Table 3. Mortality based on FAD is also split by diameter class to allow

for more effective analysis. As mentioned before, many trees die because of multiple factors. When this

happens, all evident FADs are recorded. Previous studies on mortality in the YFDP assessed only the

“primary” FAD, but it is more ecologically correct to include all FADs because they work together to

cause mortality.25 Table 3 shows more FADs than mortalities because 38.4% of all tree mortalities had

multiple FADs in 2013.

Stress was the largest contributor to mortality among trees 1 cm ≤ DBH < 10 cm, but played only

a minor role in killing trees larger than that. Insects played the largest role in killing trees 10 cm ≤ DBH <

100 cm. Only four trees larger than 100 cm DBH died in in the plot in 2013, 3 of which died from

unknown causes. Insects killed the only large tree that had an identifiable FAD.

Abies concolor

Abies concolor makes up 70.5% of the living stems in the plot and accounted for 69.6% of the

total mortality in 2013. Total A. concolor mortality rate for 2013 was 1.67%, almost identical to the

1.62% mortality rate from 2012. Table 4 shows the breakdown of A. concolor mortality by diameter

class.

                                                                                                                         25  Franklin  et  al.  1987  

Diameter Class Total Mortalities (n)

Population (x)

Mortality Rate ( m = n / x)

Proportion of Mortalities (n / 417)

1 cm ≤ DBH < 10 cm 314 15,389 2.0% 75.3% 10 cm ≤ DBH < 50 cm 93 8,843 1.1% 22.3% 50 cm ≤ DBH < 100 cm 8 703 1.1% 1.9% DBH ≥ 100 cm 2 102 2.0% 0.5% Totals 417 25,037 1.67% 100% Table 4. Abies concolor mortality for YFDP 2013 by diameter class.

Factor Associated with Death

n m = n / 417

Stress 166 39.8% Disease 67 16.1% Insect 121 29.0% Physical 154 36.9% Unknown 83 19.9% Table 5. Abies concolor mortality for YFDP 2013 by FAD.

Factor Associated with Death n Stress only 97 Disease Only 18 Insect Only 70 Disease and Insect together (other factors also possible)

20

Physical (mechanical) and Disease 7 Physical (mechanical) and Insect 6 Table 6. Relationships that different FADs had with each other for Abies concolor mortality in the YFDP 2013.

 

  10  

Table 5 shows the total number of A. concolor mortalities that were caused (in part or in whole)

by each FAD. Table 5 is most useful when compared with Table 6, which shows the breakdown of each

FAD and whether it worked to kill the trees in isolation or together with other FADs. Many interesting

relationships are distinguished here once the comparison is made. Of the 166 stress-related mortalities, 97

of them (58%) were due to stress alone. The same is true of insect deaths: 70 of the 121 (58%) insect

related deaths were caused solely by insects. Fungi were not as successful at killing trees by themselves;

of the 67 disease (fungus) related deaths, only 18 of them (27%) were caused by disease alone. Disease

and insects worked together to kill 20 trees, or 30% of all disease-related deaths and 17% of all insect-

related deaths.

Table 10 shows the Chi-Squared Contingency Table that test was run to determine if the

frequency of beetle presence was independent of the frequency of fungus in A. concolor mortality. The

computing formula

𝜒! = 𝓃 !!!!!!!  !!"!!"!

!! !! !! !!

was used to determine the Chi-Squared

value. This equation is used in place of

the natural chi-squared equation

because it is a simple 2 x 2 contingency

table.26 The equation results were:

417(20*309-18*70)2  / 327*90*38*379

= 23.8

A chi-squared value of 23.8 is much

greater than the critical value, 11.345,

so the null hypothesis that the two

variables are independent can be

rejected with 99% confidence. In other

words, the presence of one FAD, either bark beetle or fungus, in a dead A. concolor changes the

probability of the other FAD also being present. While the Chi-Squared test does not test whether it is a

positive or negative correlation, it seems obvious from looking at the data that the presence of beetles

increases the chances of fungus also being present.

                                                                                                                         26  Zar.  1984  

Bark Beetle

Present Not Present

Fungus

Present 20 18

Not Present 70 309

Table 10. Abies concolor contingency table

  11  

Pinus lambertiana

Pinus lambertiana makes up 13.8% of the living stems in the YFDP and accounted for 21.7% of

the mortality in 2013. This is up slightly from 2012 when P. lambertiana accounted for 20.1% of all

mortalities. The overall mortality rate for P. lambertiana in 2013 was 2.66%, up slightly from 2012 when

the mortality rate was 2.40%. Table 7 shows the breakdown of P. lambertiana mortalities by diameter

class.

Two of the diameter classes showed large change in mortality rates from 2012 to 2013. The 1-10

cm P. lambertianas had a mortality rate of 3.7% in 2013, an increase from 2.9% in 2012. The largest

sugar pines, those over 100 cm in dbh, showed a significant decrease in mortality from 2012 to 2013. The

2.1% mortality rate in 2012 dropped to 0.3% in 2013, though this rate is exacerbated by a small

population.

Tables 8 and 9 show the different FADs that caused mortality among the P. lambertianas in

2013. Table 8 shows the total number of trees that died as a result (in part or in whole) of each FAD,

while Table 9 looks at the relationships between the FADs and how they worked together or individually

to kill the trees.

Diameter class Total Mortalities (n)

Population (x)

Mortality Rate (m = n / x)

Proportion of Mortalities (n / 130)

1 cm ≤ DBH < 10 cm 101 2,702 3.7% 77.7% 10 cm ≤ DBH < 50 cm 28 1,397 2.0% 21.5% 50 cm ≤ DBH < 100 cm 0 453 0.0% 0.0% DBH ≥ 100 cm 1 343 0.3% 0.8% Totals 130 4,895 2.66% 100% Table 7. Pinus lambertiana mortality for YFDP 2013 by diameter class

Factor Associated with Death

n m = n / 130

Stress 45 34.6% Disease 22 16.9% Insect 72 55.4% Physical 38 29.2% Unknown 11 8.5% Table 8. Pinus lambertiana mortality for YFDP 2013 by FAD.

Factor Associated with Death n Stress only 19 Disease Only 6 Insect Only 36 Disease and Insect together (other factors also possible)

16

Physical (mechanical) and Disease 1 Physical (mechanical) and Insect 5 Table 9. Pinus lambertiana mortality for YFDP 2013 by FAD and their relationships with one another.  

  12  

Just like the FAD relationships that A. concolor had, factors associated with the mortalities of P.

lambertiana worked together to kill the trees more often than not. Of the 45 P. lambertiana stress-related

deaths, 19 (42%) were cases where stress worked alone to kill the tree, most commonly in the form of

suppression with the smallest trees. Insects successfully killed P. lambertiana alone slightly more often,

with 36 of the 72 insect-related deaths

(50%) being caused by insects alone.

Disease and insects worked together

to kill 16 trees, or 73% of all disease-

related deaths and just 22% of all

insect-related deaths. Pinus

lambertianas that die from disease-

related causes are very likely (73%) to

also have insects as a factor associated

with their death. However, the

presence of insects does not

necessarily lead to a higher chance of

disease also being present.

Table 11 shows the Chi-

Squared Contingency Table that test was run to determine if the frequency of beetle presence was

independent of the frequency of fungus in P. lambertiana mortality. The computing formula

𝜒! =𝓃 𝑓!!𝑓!! −  𝑓!"𝑓!" !

𝐶! 𝐶! 𝑅! 𝑅!

was used to determine the Chi-Squared value. This equation is used in place of the natural chi-squared

equation because it is a simple 2 x 2 contingency table.27 The equation results were:

130(16*72 – 6*36)2  / 52*78*108*22 = 11.8

A chi-squared value of 11.8 is greater than the critical value, 11.345, so the null hypothesis that the two

variables are independent can be rejected with 99% confidence. In other words, the presence of one FAD,

either bark beetle or fungus, in a dead P. lambertiana changes the probability of the other FAD also being

present. While the Chi-Squared test does not test whether it is a positive or negative correlation, it is

obvious from looking at the data that the presence of beetles increases the chances of fungus also being

present.

                                                                                                                         27  Zar.  1984  

Bark Beetle

Present Not Present

Fungus

Present 16 6

Not Present 36 72

Table 11. Pinus lambertiana contingency table.

  13  

Implications Mortality rates fluctuate from year to year in all forests, and the YFDP is no exception to that.

Following the 2012 mortality check, P. lambertiana individuals ≥100 cm dbh were dying at a rate of

4.0% annually. This year, only one individual in that diameter class died, a mortality rate of 0.3%. This

makes the outlook for the species look much better than the year prior, highlighting the importance of

continued annual mortality checks in order to more accurately calculate trends in mortality. One

important note is the increased overall mortality rate for P. lambertiana from 2012 to 2013, jumping from

2.40% to 2.66%. P. lambertiana already has one of the highest mortality rates in the plot, and it is

important to compare mortality to ingrowth rates in the future to see if the P. lambertiana population is

indeed declining, following the trend of Pinus spp. in unburned forests in the Sierra Nevadas.28 If this

trend continues the composition of the forest could continue to transform to a denser, A. concolor

dominated stand.

The data suggests that bark beetle and fungi related mortalities for both P. lambertiana and A.

concolor were not independent of each other in 2013. If this is trend is true for the entire plot, not just for

2013, than we can assume that a change in population of one factor will have a large impact on the other

factor. Such a change might present itself in the coming 1-5 years following the Yosemite rim fire of

2013.29 30 It also means that the plot is particularly susceptible to years of extremely high mortality,

because any outside factor that leads to an increase in the presence of beetles may also reinforce the

presence of fungus, having a compounding impact on mortality.

Looking Forward The relationship between bark beetles and fungus should be monitored in future years to

determine the strength of the relationship, comparing the number of mortalities influenced by each factor

over time. The relationship should also be broken down into species specific relationships, comparing

certain types of fungus with certain species of beetles. This will determine which species are aiding each

other, rather than leaving it up to the entire kingdoms that my research shows.

Monitoring of mortality rates for all species need to be continued in the plot, because 4 years of

data is not enough to calculate strong trends for any species. P. lambertiana should be monitored closely,

as the mortality rates for this species have been increasing since data collection began in 2010.

                                                                                                                         28  van  Mantgem  et  al.  2004.  29  Parker  et  al.  2006.  30  Stark  et  al.  2013.  

  14  

The Yosemite Forest Dynamic plot burned at generally low to moderate severity following the

data collection in the summer of 2013. This is the first fire in the primary forest since fire records began

in 1930. The next 3-5 years provide an opportunity to collect data on how fires impact forest composition

and structure, but it is the following years that are very important. After 5 years, will mortality rates for

species return to pre-fire levels? Will the same factors be the most destructive for each species, or will the

primary mortality-causing factors shift? There are not many forest plots that have 3 complete annual

mortality checks to refer to as a baseline for future data. Studies like this one are an important precursor to

future studies about the return of fire to fire-excluded forests.

Work Cited Cocke, A. E., Fulé, P. Z., & Crouse, J. E. (2005). Forest Change On A Steep Mountain Gradient After

Extended Fire Exclusion: San Francisco Peaks, Arizona, USA. Journal of Applied Ecology, 42(5), 814-823.

Dale, V. H., Stocks, B. J., Neilson, R. P., Mcnulty, S., Joyce, L. A., Wotton, B. M., et al. (2001). Climate Change And Forest Disturbances. BioScience, 51(9), 723.

Das, A., Battles, J., Stephenson, N., & Mantgem, P. v. (2011). The Contribution of competition to tree mortality in old-growth coniferous forests. Forest Ecology and Management, 261, 1203-1213. Retrieved January 28, 2014, from http://nature.berkeley.edu/battles/PDF/Das%20et%20al.%202011_The%20contribution%20of%20competition%20to%20tree%20mortality.pdf

Ferrell, G. T. (1996). The influence of insect pests and pathogens on Sierra forests. Sierra Nevada Ecosystem Project: Final report to Congress, vol. II, Assessments and scientific basis for management options. Davis: University of California, Centers for Water and Wildland Resources. Retreived January 27, 2014, from http://scholarsarchive.library.oregonstate.edu.offcampus.lib.washington.edu/xmlui/bitstream/handle/1957/17528/The%20influence%20of%20insect%20pests%20and%20pathogens.pdf?sequence=1

Franklin, J. F., Mitchell, R. J., & Palik, B. J. (2007). Natural disturbance and stand development principles for ecological forestry. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station.

Franklin, J. F., Shugart, H. H., & Harmon, M. E. (1987). Tree Death as an Ecological Process. BioScience, 37(8), 550-556. Retrieved October 29, 2013, from http://www.jstor.org/stable/1310665

Furniss, R. L., & Carolin, V. M. (1977). Western forest insects. Washington, D.C.: U.S. Dept. of Agriculture, Forest Service.

Lutz, J. A., & Halpern, C. B. (2006). Tree Mortality During Early Forest Development: A Long-Term Study Of Rates, Causes, And Consequences. Ecological Monographs, 76(2), 257-275.

Lutz, J. A., Larson, A. J., Swanson, M. E., Freund, J. A., & Bond-Lamberty, B. (2012). Ecological Importance of Large-Diameter Trees in a Temperate Mixed-Conifer Forest. PLoS ONE, 7(5), e36131.

Lutz, J. A., Wagtendonk, J. W., Thode, A. E., Miller, J. D., & Franklin, J. F. (2009). Climate, lightning ignitions, and fire severity in Yosemite National Park, California, USA. International Journal of Wildland Fire, 18(7), 765.

  15  

Halpern, C., & Lutz, J. (2013). Canopy closure exerts weak controls on understory dynamics: a 30-year study of overstory–understory interactions. Ecological Monographs, 83(2), 221-237. Retrieved December 23, 2013, from http://www.esajournals.org/doi/abs/10.1890/12-1696.1

Paine, T., Raffa, K., & Harrington, T. (1997). Interactions Among Scolytid Bark Beetles, Their Associated Fungi, and Live Host Conifers. Annual Reviews, 42, 179-206. Retrieved December 21, 2013, from http://www.annualreviews.org/doi/abs/10.1146/annurev.ento.42.1.179

Parker, T., Clancy, K., & Mathiasen, R. (2006). Interactions among fire, insects, and pathogens in coniferous forests of the interior western United States and Canada. Agricultural and Forest Entomology, 8, 167-189.

Scholl, A. E., & Taylor, A. H. (2010). Fire Regimes, Forest Change, And Self-organization In An Old-growth Mixed-conifer Forest, Yosemite National Park, USA. Ecological Applications, 20(2), 362-380.Smith, T., Rizzo, D., & North, M. (2005). Patterns of mortality in an old-growth mixed-conifer forest of the southern Sierra Nevada, California. Forest Science, 51(3), 266-275.

Stark, D. T., Wood, D. L., Storer, A. J., & Stephens, S. L. (2013). Prescribed fire and mechanical thinning effects on bark beetle caused tree mortality in a mid-elevation Sierran mixed-conifer forest. Forest Ecology and Management, 306, 61-67. Retrieved January 25, 2014, from http://www.sciencedirect.com/science/article/pii/S0378112713003861

van Mantgem, P. J., Stephenson, N. L., Keifer, M., & Keeley, J. (2004). Effects Of An Introduced Pathogen And Fire Exclusion On The Demography Of Sugar Pine. Ecological Applications, 14(5), 1590-1602.

van Mantgem, P. J., Taylor, A. H., Veblen, T. T., Smith, J. M., Larson, A. J., Harmon, M. E., et al. (2009). Widespread Increase Of Tree Mortality Rates In The Western United States. Science, 323(5913), 521-524.

Zar, J. H. (1984). Biostatistical analysis (2nd ed.). Englewood Cliffs, N.J.: Prentice-Hall.