University of Nevada, Reno
The Effects of Climate on Singleleaf Pinyon Pine Cone Production across an Elevational Gradient
A thesis submitted in partial fulfillment of the requirements for the degree of
BACHELOR OF SCIENCE, WILDLIFE ECOLOGY AND CONSERVATION
by
Britney Khuu
Miranda Redmond, Thesis Advisor
Peter Weisberg, Thesis Advisor
May 2017
UNIVERSITY OF NEVADA THE HONORS PROGRAM RENO
We recommend that the thesis prepared under our supervision by
BRITNEY KHUU
entitled
The Effects of Climate on Singleleaf Pinyon Pine Cone Production across an
Elevational Gradient
be accepted in partial fulfillment of the requirements for the degree of
BACHELOR OF SCIENCE, WILDLIFE ECOLOGY AND CONSERVATION
______Peter Weisberg, Ph. D., Thesis Advisor
______Tamara Valentine, Ph. D., Director, Honors Program
May 2017 i
Abstract
Climate change affects forest structure and composition through increasing temperatures and altered precipitation regimes. Arid and semi-arid ecosystems have shown signs of susceptibility towards regional warming. Climate warming may negatively affect tree reproduction, which is an important factor in tree population dynamics. The relationship between climate change and tree reproduction is not fully understood, particularly in mast seeding tree species. This project aims to determine how climate affects seed production across an elevational gradient in singleleaf pinyon pine
(Pinus monophylla), a dominant, widespread mast seeding conifer of the Great Basin.
Historical cone production data were collected and reconstructed for the past 15 years at three sites that span an elevational gradient on Rawe Peak near Dayton, Nevada. The low elevation site had only one year of high seed cone production; therefore, seed production data were not sufficient enough to test any relationships between cone production and climate. Cone production in low, mid, and high elevation sites differed significantly with more cones at the high elevation site. A significant positive relationship was found between cone production and summer precipitation (May to July) from three years prior to mature cone production (one year prior to seed cone initiation) at the high elevation site (p=0.0006; r^2=0.5841). The result suggests that Pinus monophylla stores resources prior to a mast seeding event and that precipitation can be a limiting factor on cone production. With predicted decreases in precipitation due to climate change, cone production may be significantly affected in Pinus monophylla and other mast seeding tree species. Further research with larger sample sizes, more study sites, and a longer time period of investigation is needed to better understand the relationship between cone ii production and climate change, particularly for tree species with long intervals between masting events. Research on this topic is important because changes in seed production due to climate change could alter future forest structure and composition, affecting wildlife and dependent communities.
.
iii
Acknowledgements
I would like to thank my thesis advisors Peter Weisberg and Miranda Redmond for all of their help and support. They have been there for me from beginning to end, from writing my proposal to the final touches on my thesis. I have really learned so much from working with them. I would also like to thank Stephanie Freund for helping me out during the branch gathering process of this project. Finally, I would like to thank my friends and family for their love and encouragement throughout this time. This project was funded by the National Science Foundation’s Experimental Program to Stimulate
Competitive Research (NSF EPSCoR) Research Infrastructure Improvement (RII)
Award.
iv
Table of Contents
Abstract ...... i
Acknowledgements ...... iii
Table of Contents ...... iv
Table of Figures ...... v
Introduction ...... 1
Literature Review ...... 5
Mast Seeding and Its Mechanisms ...... 7
Mast Seeding and Climate ...... 12
Climate Effects on Cone Production ...... 13
Climate Change Effects on Cone Production ...... 15
Significant Effects of Pinyon Pine Regeneration Failure ...... 17
The Need for Historical Cone Data ...... 19
Methodology ...... 20
Results ...... 25
Discussion ...... 33
Conclusion ...... 39
References ...... 40
v
Table of Figures
Figure 1. The focal species, Pinus monophylla, can be long-lived ...... 3
Figure 2. Hypothesized relationships between cone production and late summer temperature at low, mid, and high elevations...... 5
Table 1. Mast seeding tree species and their masting cycle intervals ...... 8
Table 2. A comparison of evidence supporting or in opposition to the four primary mast seeding hypotheses: resource matching, predator satiation, pollination efficiency, and animal dispersal ...... 10-11
Figure 3. Pinus monophylla trees showing canopy thinning and tree mortality as a result of drought ...... 16
Figure 4. Picture of pinyon jay on top of Pinus monophylla tree ...... 18
Figure 5. Satellite image of the site locations at Rawe Peak, showing the site location in relationship to California and Nevada ...... 21
Figure 6. Photo of the high-elevation study site showing sampled Pinus monophylla trees...... 21
Figure 7. Explanation of cone scar abscission methodology ...... 22
Table 3. Trees sampled at each site (low, medium, high) elevation. One low elevation tree was not included in the data set...... 23
Figure 8. Sample of cone production data set showing site, year, cone mean, and cone standard error ...... 24
Figure 9. Average cone production (average number of cones per branch per tree for all trees sampled) at high, medium, and low elevation sites from 2002-2017 ...... 25 vi
Figure 10. Average growing season temperature (March-October) at the study site from
1990-2016 ...... 26
Figure 11. Average annual precipitation at the study site from 1990-2016 ...... 26
Figure 12. Cones per tree and average diameter at root collar (cm) ...... 27
Figure 13. Average cone production and summer precipitation (May to July) from two and three years prior to mature seed cone production ...... 29
Table 4. Single linear regression analysis results of promising climate variables including the regression coefficient (Bi), p-value, and R ...... 30
Figure 14. Cone production and annual precipitation (November to October) from two and three years prior to mature seed cone production ...... 31
Figure 15. Average cone production and growing season temperature (March to October) from two and three years prior to mature seed cone production ...... 32
Figure 16. Cone production and summer temperature (May-July) from two years and three years prior to mature seed cone production ...... 33 1
Introduction
Climate change may be altering forest structure and composition due to effects of increasing temperatures and associated water deficits and changes in precipitation. Arid and semi-arid ecosystems in the western US have already been affected by tree mortality in response to regional warming (Allen et al. 2010; Breshears et al. 2005), suggesting that further increases in temperature will lead to an upward trend in tree mortality. Due to the lack of long-term data sets on seed production, it is unclear how changing climate may affect tree reproduction, particularly of mast seeding tree species characterized by high synchronicity and high inter-annual variability in seed production. Mast seeding, which occurs in many temperate tree species, refers to a particular reproductive behavior where all trees in a population synchronously produce large seed crops in irregular intervals.
Seed production among mast seeding conifers in arid (less than 25 cm total annual precipitation) and semi-arid (25 to 50 cm total annual precipitation) ecosystems is strongly affected by predicted increases in temperature associated with global climate change (Perez-Ramos,Ourcival, Limousin, & Rambal 2010; Redmond, Forcella, &
Barger 2012). Previous research has found strong negative relationships between late summer temperature and seed cone production among two semi-arid conifer species:
Pinus ponderosa and Pinus edulis (Mooney, Linhart, & Snyder 2011; Redmond,
Forcella, & Barger 2012). It is likely that increasing temperatures negatively affect seed production among other semi-arid conifers as well, with accelerated drying of soil, reduced water availability, and increased tree transpiration rates (Gworek, Vander Wall,
& Brussard 2007). With predicted increases in temperatures in the future, inadequate seed cone production could limit future conifer regeneration. Limited seed production is 2 significant because forest resilience may not be high in the event of climate change and forests may not be able to rebound if tree regeneration is limited. Research can help predict how climate change will affect tree population dynamics.
The effects of climate on seed production vary along elevational gradients.
Differential sensitivity to climate along an elevational gradient was found in a study that sampled a network of 29 Pinus uncinata forests in Northeastern Spain; Pinus uncinata trees growing at southern and low-elevation sites were the most negatively affected by warm and dry summer conditions (Galvan, Camarero, & Gutiérrez 2014). Trees in lower elevation areas were more affected by increasing temperatures than trees in higher elevation areas because at lower elevations, precipitation amounts tend to be lower and temperatures tend to be higher. Lower precipitation and higher temperatures result in accelerated drying of soil, reduced water availability, and increased tree transpiration rates (Gworek, Vander Wall, & Brussard 2007). An increase in temperature due to climate change may force species to move upslope or towards cooler microsites. When the altitudinal distributions of 171 forest plant species were compared between two periods, 1905 to 1985 and 1986 to 2005 in western Europe, it was found that climate warming resulted in an upward shift in species-specific optimum elevation (altitude of maximum probability of presence) at an average of 29 meters per decade (Lenoir,
Gegout, Marquet, Ruffray, & Brisse 2008). Since seed production at low elevation populations can be directly affected by climate, climate change can result in an upward shift in elevation for trees. However, the ability to undergo range shifts that allow tree species to track their climate niche further depends upon adequate seed production at upper elevations. 3
This project aims to determine how climate affects seed production across an elevational gradient for a semi-arid conifer, singleleaf pinyon pine (Pinus monophylla)
(Figure 1).
Figure 1: The focal species, Pinus monophylla, can be long-lived (> 800 years) on rocky sites such as shown here. (Source: Peter Weisberg)
This project specifically focuses on Pinus monophylla because it is a dominant, widespread mast seeding conifer of the Great Basin that provides critical habitat for a range of wildlife species. Pinyon pines (Pinus monophylla and Pinus edulis, primarily) often co-occur with several species of juniper (Juniperus) to form pinyon-juniper woodlands. Pinyon-juniper woodland is a major vegetation type that covers over 100 million acres in the Western United States (Romme et al. 2009). Widespread reproductive failure of Pinus monophylla would lead to immense landscape change. Furthermore, pinyon pine seeds are highly valued by local Native American tribes and pine-nut harvesters, in addition to serving as an important food source for many wildlife species. 4
The specific research questions this study addressed are 1.) How does pinyon pine cone production vary across an elevational gradient? and 2.) What is the relationship between annual variability in climate (summer temperature, growing season temperature, summer precipitation, and annual precipitation) and cone production, and how does this relationship vary across an elevational gradient?
I hypothesize that higher elevations, that generally experience greater precipitation and lower temperatures, will yield greater pinyon pine seed production during years with cooler summers. Based on previous research on other semi-arid conifers (Forcella 1981; Mooney, Linhart, & Snyder 2011; Redmond, Forcella, & Barger
2012), I hypothesize cone production will be negatively associated with summer temperature and growing season temperature, and will be positively associated with summer precipitation and annual precipitation. I hypothesize that at all elevations, there is greater seed production when summer temperature is cooler. I predict that cone production among trees at low elevations, which are characterized by warmer temperatures and reduced annual precipitation, will be more sensitive to annual fluctuations in climate. For instance, I hypothesize that high summer temperatures will decrease cone production, with trees at high elevation being less affected than trees at lower elevations (Fig. 2). 5
Elevation Low Medium High Average Cone production Cone production Average Summer Temperature (° C)
Figure 2. Hypothesized relationships between cone production and late summer temperature at low (dotted), mid (dashed), and high (solid) elevations. At all elevations, there is greater seed production when summer temperature is cooler. With cold summers, cone production is highest at low elevation. With hot summers, cone production is highest at high elevation. The low elevation site is expected to be the most affected by climate, with the largest range in cone production due to weather.
Literature Review
Singleleaf pinyon pine (Pinus monophylla) is a widespread semi-arid conifer in the Western United States that is important to wildlife and is also valued by local Native
American tribes and pine-nut harvesters. Pinyon-juniper forest covers over 100 million acres in the region (Romme et al. 2009). Pinus monophylla is a masting tree species, characterized by synchronous, episodic seed production. Mast seeding is where all trees in a population synchronously produce large seed crops in irregular intervals. Pinus monophylla has a 26 month long (3 seasons) seed development cycle (Evans 1988), which means in a mast seeding event all of the trees in a population synchronously begin production of seed crops and 26 months later the seed crops would be released at the same time.
Effects of climate change on Pinus monophylla population dynamics are complex and not fully understood. Climate change can elicit a wide range of responses that are factor dependent and species specific; each species may react differently to climate 6 change with certain species particularly affected (Brubaker 1986). Since the factors that contribute to tree population response are specific to each case, how each species will react is hard to predict until the species has been studied. As species react to climate change at varying rates and with differing responses, some species may outperform others. Certain tree species may be more affected by climate change than other species, which can result in a change in forest structure and lower tree diversity. For example,
Juniperus monosperma is more drought tolerant than Pinus edulis, so pinyon-juniper woodland (Pinus edulis and Juniperus monosperma) would be increasingly dominated by
Juniperus monosperma in the event of consecutive, severe droughts (Mueller et al. 2005).
Species competition can lead to altered forest composition over time. Pinus monophylla is a keystone species for many woodland ecosystems in the western United States, and its climate change response will affect other species depending on the strength of their negative or positive associations with it. For example, pinyon jay is strongly affected by
Pinus monophylla seed production, with a poor seed production year shown to delay or prevent breeding in pinyon jays (Lanner 1981).
There has been extensive research on climate change and its effects on tree health
(Allen et al. 2010; Brashears et al. 2005; Hanson &Weltzin 2005; Liang, Leuschner,
Dulamsuren,Wagner & Hauck 2015; Mueller et al. 2005; Plaut et al. 2013). However, comparatively few studies on climate change effects have examined the effects on tree regeneration and only a fraction of those studies focused on cone production. Therefore, there is a further need for research on how changing climate influences tree cone production, particularly for mast seeding tree species that are expected to be strongly influenced by climate change. 7
This literature review first introduces the concept of mast seeding and what may drive mast seeding, how mast seeding species are affected by climate, and how they are expected to react to climate change. Then, the literature review discusses climate effects on cone production in all tree species and what factors affect how trees respond to climate change. Finally, the possible effects of cone production failure and the need for historical cone data are presented.
Mast Seeding and Its Mechanisms
Mast seeding, which occurs in certain temperate tree species (Table 1), occurs when highly variable and synchronized large seed crops are produced in inter-annual cycles (Kelly & Sork 2002). The masting cycle interval (years between mast seeding events) varies by species and by geographic region (Table 1). Mast seeding tree species, characterized by high synchronicity and high inter-annual variability in seed production, are particularly sensitive to climate change. Since climate factors act as triggers or cues for episodically high seed production (Roland, Schmidt, & Johnstone 2013), climate change can significantly affect this form of tree reproduction and possibly reduce the adaptive value of mast seeding.
8
Masting Species Masting Cycle Interval Citation Pinus palustris 5 to 7 years Patterson & Knapp 2016 Pinus albicaulis 3 to 5 years Morgan & Bunting 1990 Pinus ponderosa 4 to 5 years Oliver & Ryker 1990 Pinus jeffreyi 2 to 4 years Lanner 1998 Picea glauca 2 to 6 years Nienstaedt & Zasada 1990 Fagus crenata 5 to 7 years Kikuchi 1968 Fagus grandifolia 2 to 8 years Tubbs, Carl, & Houston 1990 Quercus lobata 2 to 3 years Elias 1980 Abies alba 3 to 5 years McCartan & Jinks 2015 Table 1. Mast seeding tree species and their masting cycle intervals (years between mast seeding events). Masting cycle intervals vary greatly within a species and range from 2 to 8 years across all species.
Several alternative hypotheses have been posed to explain mast seeding, each hypothesis differing in the relative importance attributed to climate (Table 2). The hypotheses help to identify the mechanisms that produce a correlation between masting and the proposed climate variables. There are four well supported hypotheses that explain mast seeding: the resource matching hypothesis, predator satiation hypothesis, pollination efficiency hypothesis, and the animal dispersal hypothesis (Kelly and Sork 2002). The resource matching hypothesis states that seed production corresponds to environmental variation, with favorable weather corresponding to high seed production (Kelly 1994).
Additionally, favorable weather can act as a cue to initiate masting. Evidence of support for this hypothesis includes temperature acting as a cue for cone production and cone production being affected by temperature. For example, a Picea glauca study found that cone production was affected by summer temperature from two years prior to mature cone production (Krebs et al. 2017). 9
The predator satiation hypothesis states that mast seeding is a reproductive strategy that evolved to satiate seed predators, providing an overabundance of seed during mast seeding years to ensure that some seeds escape predation, while starving predators during non-mast seeding years to keep predator population numbers in check
(Janzen 1971). Studies that supported the predator satiation hypothesis reported that seeds from mast seeding years were harvested at a slower rate and had higher survival rates.
Further evidence in support of the predator satiation hypothesis includes indication that predator populations were affected by mast seeding years, with decline in predator populations in the non-masting year and satiation of predators in the mast seeding year.
The pollination efficiency hypothesis states that masting increases pollination success through synchronized flowering effort (Kelly 1994). A Fagus crenata study provided supporting evidence for the pollination efficiency hypothesis by confirming a relationship between seed production and pollination where higher seed production was linked to lower pollination failure (Kon, Noda, Koyama, &Yasaka 2005).
The animal dispersal hypothesis, perhaps less well supported than the others, states that masting increases dispersal of seeds by reducing post dispersal predation
(Janzen 1971). There is conflicting evidence for the animal dispersal hypothesis; one study supports the animal dispersal hypothesis by discovering increased scatter hoarding and dispersal distance during masting years (Li & Zhang 2007), whereas another study reports lower dispersal distance in masting years (Jansen, Bongers, & Hemerik 2004).
In a Quercus study that tested all four hypotheses (resource matching, predator satiation, pollination efficiency, and animal seed dispersal), the pollination efficiency and predator satiation hypotheses were supported, while the resource matching and animal 10 seed dispersal hypotheses were not supported (Table 2; Koenig, Mumme, Carmen, &
Stanback 1994). A variety of masting patterns were tested, including variation in seed production, distribution of seeds, crop failures, masting in successive years, and regular masting cycles in individuals and within population (Koenig, Mumme, Carmen, &
Stanback 1994).
Hypothesis Resource Predator Pollination Animal matching satiation Efficiency Dispersal Description Good weather Provides an Increases Masting corresponding overabundance pollination success increases to high seed of seed to ensure through dispersal of production. that some seeds synchronized high seeds by Favorable escape predation. flowering effort. reducing post climate can act dispersal as a cue to predation. initiate masting. Evidence in Summer Seeds released in With more seed Increased Support temperature the mast seeding crop, pollination scatter (two years year were failure decreased. 5 hoarding and prior) acts as a harvested at a dispersal cue and can be significantly No evidence of distance used to slower rate than bimodal during estimate cone seeds released in distribution of seed masting production1 the non-mast production within years.3 seeding year.3 years, high Spring variation in seed temperatures Greater survival production among and of seeds in mast years, bimodal precipitation seeding years 4 distribution of seed affect cone production among production. Predator years, crop High cone populations failures, and mast production affected by mast seeding events in was correlated seeding years successive years.6 with the (decline in difference predator between April populations in temperatures poor seed years and associated and satiation of with a dry predators in the 11
spring mast seeding followed by a year)5 humid spring2 No evidence of bimodal distribution of seed production within years, high variation in seed production among years, bimodal distribution of seed production among years, crop failures, and regular masting cycles.6 Evidence in Bimodal Lower Opposition distribution of dispersal seed distance in production masting among years, years4 crop failures, and regular Bimodal masting distribution of cycles.6 seed production among years, crop failures, high variation in seed production among years, and low variation in seed production within years6 1 Krebs et al. 2017 2 Davi et al. 2016 3 Li & Zhang 2007 4 Jansen, Bongers, & Hemerik 2004 5 Kon, Noda, Koyama, &Yasaka 2005 6 Koenig, Mumme, Carmen, & Stanback 1994 12
Table 2. A comparison of evidence supporting or in opposition to the four primary mast seeding hypotheses: resource matching, predator satiation, pollination efficiency, and animal dispersal.
Mast Seeding and Climate
Previous research has demonstrated that mast seeding is affected by climate in various ways. Research on acorn production synchrony supports the idea of the Moran effect, in which synchronous seed production is driven by synchronous environmental cues used by trees across large distances (Koenig & Knops 2013). In a study with Quercus ilex, mast seeding is proven to be a physiological response to the variable environment (Pérez-
Ramos, Ourcival, Limousin, & Rambal 2010). Mast seeding is affected most by temperature and precipitation, with wetter and cooler climates being preferred in semi- arid and arid regions with future predictions of drought (Pérez-Ramos, Ourcival,
Limousin, & Rambal 2010; Redmond, Forcella, & Barger 2012). Pinus edulis studies confirmed that pine cone yield variation is heavily correlated to rainfall and temperature in semi-arid ecosystems (Forcella 1981; Redmond, Forcella, & Barger 2012). Likewise, stone pine research showed that cone yield is a response to weather and resource depletion, with water stress being the main limiting factor (Mutke, Gordo, & Gil 2005).
Effects of climate may vary depending on the geographic location of the site, with drier future conditions potentially affecting species in wetter sites more drastically (Pérez-
Ramos, Padilla-Díaz, Koenig & Marañón 2015.
Past growing season climate conditions can strongly influence masting events, with climate from one or two years prior affecting cone production. In Fagus species, it was found that wet and cool summers two years prior to cone production as well as 13 drought one year prior to cone production are positively related to masting activity
(Piovesan & Adams 2001). Carbohydrates are gained and reserved during wet years, after which subsequent drought initiates flowering, culminating in seed production during the third year (Piovesan & Adams 2001).
Mast seeding behavioral patterns and seed production may be altered by climate change. Temperature shifts are predicted to alter masting; specifically, an increase in temperature during the flowering period is predicted to decrease mast seeding behavior
(Koenig, Knops, Carmen, & Pearse 2015). Climate warming can affect seed production through changes in the timing of flower bud initiation. In Quercus ilex, later onset flowering, which allows more sugars and photosynthesis byproducts to be produced prior to flowering, was positively associated with crop size and extent of mast seeding activity
(Fernández-Martínez, Belmonte , & Espelta 2012). Future warmer and drier conditions are expected to result in early onset flowering, which may negatively affect seed production.
Climate Effects on Cone Production
With varying temperature and precipitation at different elevations, it is expected that cone production varies with elevation, although how it varies depends on carbon fixation rates and carbohydrate reserves. Whereas several studies found that there was higher cone production at the higher elevations of a species range (Loewe-Muñoz,
Balzarini, Álvarez-Contreras, Delard-Rodríguez, & Navarro-Cerrillo 2016; Ayari et al.
2011), one study found that at mid elevation there was higher cone production (Davi et al. 2016) and another did not find a relationship between total seed production and elevation (Mantgem, Stephenson, & Keeley 2006). The influence of elevation may be 14 especially strong in mast seeding species because they store carbohydrate reserves prior to mast seeding. Also, there can be significant differences in carbon intake and carbon storage between elevations that may change from year to year (Davi et al. 2016).
With climate change, the timing of climate variables may be altered, such as higher temperatures earlier in the summer. The timing of climate variables plays a large role in seed production, with changes in climate affecting multiple stages of cone development. For stone pine, higher water availability at the time of primordia formation, pollination, and the final ripening of the cones contributed to higher cone yield, as did milder midsummers just after pollination (Mutke, Gordo, & Gil 2005). Cone production can be highly dependent on climate factors throughout a specific time period. The mast seeding cone production of juniper, oak, and pinyon in a given year is highly affected by available moisture during the entire growth year (Zlotin & Parmenter 2008). Effects of climate variables may also be cumulative or lagged across multiple years (Buechling,
Martin, Canham, Shepperd, & Battaglia 2016; Moreira, Abdala-Roberts, Linhart, &
Mooney 2015). The cumulative effects of climate variables (elevated summer temperatures in seed dispersal year, low spring snowfall in bud initiation year, and reduced spring snowfall two years prior to seed dispersal) across multiple years were associated with high seed production in Picea engelmanni (Buechling, Martin, Canham,
Shepperd, & Battaglia 2016). Lagged temperature effects, where spring mean temperature two years before seed cone maturation was the best predictor of seed production, were also observed for ponderosa pines (Moreira, Abdala-Roberts, Linhart, &
Mooney 2015).
15
Climate Change Effects on Cone Production
While climate change impacts ecological processes in a wide range of ecosystems, arid- and semi-arid ecosystems are especially susceptible to changes in climate. Arid ecosystems have less than 25 cm total annual precipitation while semi-arid ecosystems have 25 to 50 cm total annual precipitation. The Intergovernmental Panel on
Climate Change states that future expected climatic changes include increases in temperature, and frequency and severity of drought (IPCC 2001). Arid and semi-arid ecosystems may be especially sensitive to climatic change because of the low baseline levels of water availability in these ecosystems (Hanson & Weltzin 2000). Temperature increases caused by climate change have already increased tree mortality in semi-arid forests (Allen et al. 2010; Liang, Leuschner, Dulamsuren, Wagner & Hauck 2015).
Predicted climate changes are expected to negatively affect trees in arid and semi- arid ecosystems in various ways. With temperature changes, extreme climate conditions may become more frequent. Extreme climate conditions are shown to negatively affect growth, crown development, and cone production in trees (Thabeet et al. 2005).
Increasing summer temperatures were linked to a decrease in tree growth, with moisture being the major growth constraint (Battle et al. 2007). Climate changes are not expected to affect trees of all age classes in the same manner. Severe drought significantly affects seedlings and saplings, although prolonged drought even affects mature trees, making them more susceptible to insects or disease (Hanson & Weltzin 2000) (Figure 3). 16
Figure 3. Pinus monophylla trees showing canopy thinning and tree mortality as a result of drought. (Source: Peter Weisberg)
Increased tree mortality negatively affects forest resilience and increases the importance of successful tree regeneration. Prolonged, frequent drought leads to tree mortality in multiple ways. It decreases a tree’s ability to utilize shallow soil moisture, caused by a decrease in the area in which the roots can absorb water (Plaut et al. 2013). It also reduces transpiration after precipitation, which prevents growth of absorbing roots, xylem, and leaves (Plaut et al. 2013). With long-term intensive drought, trees have no opportunity to recover from those effects and only become weaker over time, ultimately leading to tree mortality.
Climate change can affect a tree differently depending on its location along environmental gradients. In mountain beech, seed production increases over time (1965-
2009) were more marked for trees at higher elevations, and trees at higher elevations were more heavily influenced by the tested climate variables (Allen, Hurst, Portier, & 17
Richardson 2014). The larger increase in seed production was thought to be a result of higher nutrition from the soil and higher nitrogen availability in higher elevations (Allen,
Hurst, Portier, & Richardson 2014).
Climate change may show the strongest adverse influences on seed production among semi-arid, mast seeding species. Previous studies have found that predicted increases in temperature associated with global climate change would strongly affect seed production among mast seeding conifers in arid and semi-arid ecosystems (Pérez-Ramos,
Ourcival, Limousin, & Rambal 2010; Redmond, Forcella, & Barger 2012). Specifically, strong negative relationships between late summer temperature and seed cone production were found among Pinus ponderosa and Pinus edulis, two semi-arid conifer species
(Mooney, Linhart, & Snyder 2011; Redmond, Forcella, & Barger 2012).
It is also possible that climate change will not adversely seed production among semi-arid mast seeding species. One study proposed that mast seeding may not be affected by mean temperatures, but rather by differences between summer temperatures
(temperature differential; Kelly et al. 2012). In this case, changes in mean temperature will not affect mast seeding as much as yearly weather variability (Kelly et al. 2012).
Significant Effects of Pinyon Pine Regeneration Failure
Climate change results in alteration and restructuring of dependent communities through the decline of certain tree species. Pinyon-juniper stands in New Mexico and northwestern Oklahoma serve as examples of communities already altered by climate change. In those revisited pinyon-juniper stands, seed cone production in pinyon pine had declined by 40% over a 34-year time span likely due to climate conditions throughout the years (Redmond, Forcella, & Barger 2012). Previous drought events have changed 18 pinyon-juniper forest (Pinus edulis and Juniperus monosperma) composition because
Pinus edulis is more sensitive to drought than Juniperus monosperma (Mueller et al.
2005), therefore Pinus edulis suffered more tree mortality following drought events.
Altered communities reduce available biotic associations for pinyon pine, making the community structure change persistent.
There is a wide range of effects that mast seeding has on wildlife behavior and population number. Pinyon jay (Figure 4) populations are significantly affected by Pinus monophylla seed production.
Figure 4. Picture of pinyon jay on top of Pinus monophylla tree. (Source: Peter Weisberg)
A poor seed year has been shown to negatively affect pinyon jays by delaying or preventing breeding, since there is a heavy reliance on pinyon seeds as a food source for both the parents and the young (Lanner 1981). Pinyon jay populations have been limited by recent decreases in pinyon-juniper forest foraging habitat, contributing to pinyon jay population decline (Witt 2015).
Masting tree species support wildlife by providing shelter and food. Besides 19 supporting valuable species that contribute to biodiversity, mast seeding can also support invasive and harmful wildlife populations that have detrimental environmental impacts.
For instance, mast seeding of turkey oak and chestnut in the Apennine Mountains was found to be a food source for wild boar, influencing wild boar population dynamics
(Cutini et al. 2013). Likewise, in New Zealand, beech trees support a large invasive mammal population (Tompkins, Byrom, & Pech 2013).
Mast seeding cycles have affected movements of seed predators at local and continental scales by encouraging migration and attracting nomadic populations. Mast seeding activity has affected irruption patterns in boreal birds, with differing mast seeding cycles at opposite regions promoting irruptive activity and migration patterns
(Strong, Zuckerberg, Betancourt, Koenig, & Walter 2015). Seed predator movement, population count, breeding, and social interactions were heavily affected by seed availability and mast seeding cycles in Bornean forests, with resident populations limited through mast seeding and an increase in nomadic seed predator populations (Curran &
Leighton 2000).
The Need for Historical Cone Data
Historical cone data are necessary for studying mast seeding species due to the highly variable nature of their reproductive outputs. Studies on mast seeding species have been limited because of the need for long-term datasets. Many methods to collect cones are neither reliable nor logistically feasible. One unreliable and generally infeasible method requires cones to be manually collected from each tree every year throughout the entire study period (Calama, Gordo, Mutke, & Montero 2008). This method is labor intensive and has a risk of inaccurate cone estimation (Calama, Gordo, Mutke, & 20
Montero 2008). Another method is to visually count cones from the ground from a section of a tree and use a set conversion equation to estimate the number of cones per tree (LaMontagne, Peters, & Boutin 2005). However, this method is often species specific due to the conversion values, and therefore is not accurate in many cases
(LaMontagne, Peters, & Boutin 2005).
A simple, efficient method for evaluating historical (past 10-20 years) cone production is by counting cone abscission scars on tree branches (Forcella 1981). Used since the 1930’s (Forcella 1981;Redmond, Forcella, & Barger 2012), this method has since been validated by a study on pinyon pine, where tree cone production estimated using the cone abscission scar method is compared to a historical cone production data set (Redmond et al. 2016). This method is not perfect, as summer growth and conelet abortion may prevent accurate cone scar dating, but these cases are infrequent and of minor significance (Forcella 1981; Redmond et al., 2016). Since the cone abscission method does not require significant cone or branch collection and is more accurate than the other methods, this method will be used to determine cone production in this study.
Methodology
Pinyon cone production data were collected at three semi-arid sites that span an elevational gradient at Rawe Peak, which is 10 miles east of Dayton, Nevada (Figure 5). 21
Figure 5. Satellite image of the site locations at Rawe Peak, showing the site location in relationship to California and Nevada.
Sites of similar slope and aspect were chosen to represent three distinct elevations
(low, medium, high). The elevation for the low site was 1750 meters, the medium site was 1900 meters, and the high site was 2200 meters (Figure 6).
Figure 6. Photo of the high-elevation study site showing sampled Pinus monophylla trees. (Source: Stephanie Freud)
At each of the three sites, transects were established and all trees were sampled that were within 5 meters of the transect, at least 5 meters away from any other sampled 22 tree, at least 20 centimeters in DRC (diameter at root collar), and did not appear to have wounds on the branches. I sampled 10 reproductively mature trees at each site and made sure that at least six trees per site were cone-bearing trees. For each tree, I used loppers to harvest six cone-bearing branches per tree to be processed back in the laboratory, selecting for healthy, weight-bearing branches (green needles, no wounds, and sturdy branches). The total number of cone-bearing branches was counted to estimate the total number of cones produced per year per tree. Stem diameter, canopy area, height, tree vigor, and other factors that may be related to tree reproduction were also noted.
Upon returning from the fieldwork, I followed the cone abscission scar methodology outlined in Redmond et al. (2016) to obtain an estimate of pinyon pine cone production over the past 15 years (Figure 7).
Figure 7. Explanation of cone scar abscission methodology. (Source: Miranda Redmond)
Historical reproductive output was estimated using the visible abscission scars 23 that remain after the cones are dropped from cone-bearing branches (Figure 7, D). I counted cones and cone scars at each terminal bud scale scar (indicating branch growth from year to year) for the last 15 years from cone-bearing branches of each tree collected.
Previous research has established that four to five branches from four to five trees are sufficient to estimate cone production at each site (Forcella 1981), therefore, five high elevation site trees were processed for cone production. For the medium and low elevation sites, there was less cone data so all of the trees (10 for each site) were sampled.
I was not able to obtain cone scar estimates on all of the trees sampled. The branches from one low elevation site tree had cone scars that were hard to differentiate from tree wounds and branch years did match up with one another so it was not used in the analysis. Ultimately, five high site trees, ten medium site trees, and nine low site trees were sampled (Table 3). Four low elevation trees and one medium elevation tree had no cone scars.
Site Trees sampled High Elevation 5
Medium Elevation 10
Low Elevation 9 Table 3. Trees sampled at each site (low, medium, high) elevation. One low elevation tree was not included in the data set.
Using my data set (Figure 8), I conducted exploratory data analysis using scatterplot graphs to examine relationships between cone production and climate (summer temperature, growing season temperature, summer precipitation, and annual precipitation) at each of three elevations. 24
Figure 8. Sample of cone production data set showing site, year, cone mean, and cone standard error.
I obtained precipitation and temperature data from the Northwest Alliance for
Computational Science & Engineering (PRISM Climate Group, Oregon State
University). Linear regression analyses were performed to test the relationship between cone production and climate variables: summer precipitation (May to July), annual precipitation (November to October), growing season temperature (March to October) and summer temperature (May to July). I also performed a one-way analysis of variance
(ANOVA) to compare differences in mean cone production at the three sites. The relationship between tree size and seed cone production was studied to determine if differences in mean cone production might be affected by resource allocation towards growth.
Yearly cone production was normalized (z-standardized) to standard deviate units, by taking the mean cone production of specific years minus long term average cone production(mean of all the years) divided by the standard deviation of all the years.
Masting years are defined as years in which the standard deviate is higher than the absolute value of the lowest negative standardized deviate (LaMontagne & Boutin 2007; 25
LaMontagne & Boutin 2009; Redmond, Forcella, & Barger 2012). Using this method, the standard deviate is positive for high seed production years, is near zero for average seed production years, and is negative for poor seed production (non-masting) years.
Results
There have been changes in cone production over time, with average cone production increasing from 2002-2017 (Figure 9).
2
1.5 High Elevation Site 1 Medium Elevation Site Production* Average Cone 0.5 Low Elevation Site
0 2002 2003 2004 2005 2007 2009 2010 2011 2012 2013 2014 2015 2017 2006 2008 2016 Years( 2002-2017) Figure 9. Average cone production (average number of cones per branch per tree for all trees sampled) at high, medium, and low elevation sites from 2002-2017. Average cone production was highest at the high elevation site and lowest at the low elevation site.
Cone production was highest at high elevation sites and lowest at low elevation sites (Figure 9). The mean annual cone production for the high elevation site was 0.596
(standard error: 0.264), the medium elevation site was 0.221 (standard error: 0.080), and the low elevation site was 0.094 (standard error: 0.058). Cone production was calculated by multiplying the number of cones by the number of branches for each tree, and was averaged across trees for each year to calculate the yearly average cone production. There was a significant difference in cone production between the high, medium, and low sites, with the greatest cone production in high-elevation sites (p=0.0047). 26
The increase in average cone production over time may be related to changes in climate, especially temperature and precipitation. In recent years, average growing temperatures at the study site have been consistently higher than they have been over the last 10 years (Figure 10). The late summer temperatures at the study site were consistent over the study period. However, average annual precipitation at the site has been variable from 1990 to 2016 (Figure 11).
13 12 11 10 9 8 Temperature (° C) 1990 1995 2000 2005 2010 2015 Average Growing Season Year
Figure 10. Average growing season temperature (March-October) at the study site from 1990-2016. Growing season temperature has been consistently higher in recent years.
100 80 60 40 20 0 Precipitation (mm) 1990 1995 2000 2005 2010 2015 Year
Figure 11. Average annual precipitation at the study site from 1990-2016. Variable annual precipitation throughout the years.
Alternatively, the changes in cone production can be attributed to other variables.
Tree size has also changed over time, growing larger in recent years. With bigger trees, 27 resource allocation between growth and reproduction might be changing. It was found that in Abies alba, large trees were the most productive and less radial growth in previous years resulted in higher cone production (Davi et al. 2016). When testing cone production at each site against tree size and canopy area, there were no correlations between cone production and tree size or canopy area (low elevation site: p=0.288; medium elevation site: p=0.511; high elevation site: p=0.320), suggesting that increases in tree size did not influence cone production.
Cone production at both branch and tree levels were compared to average diameter at root collar (DRC). In the scatterplot which compares cones per tree and average DRC, there is a slight left skew in distribution (Figure 12), indicating that there are slightly more cones per branch and per tree in trees with a lower average DRC.
500 400 300 200
Cones Per Tree 100 0 10 20 30 40 50 60 Average Diameter at Root Collar (cm)
Figure 12. Cones per tree and average diameter at root collar (cm). Slight left skew in distribution indicating more cones in smaller trees.
High elevation sites experienced four masting events (2010, 2013, 2015, 2016) between 2001-2016, whereas medium and low elevation sites showed less masting activity, with three masting events (2011, 2015, 2016) at medium elevation and two masting events (2015 and 2016) for low elevation. Masting occurred at all elevation sites 28 in 2015 and 2016. There were no masting years at any of the three sites prior to 2009
(2001-2009).
Other studies of masting have indicated that cone production may be affected by climatic conditions from previous years. Therefore cone production was compared to climate data from two and three years prior to mature cone production. While there are enough data (more than three data points of high cone production) to look at how climate influences cone production at high and medium elevation, there are not enough data (one data point) at the low elevation site.
There was a significant relationship between summer precipitation (May to July) from three years prior to mature seed cone production and average cone production at the high elevation site (Figure 13; Table 4). 29
High'Elevation'Site'(x22)' High'Elevation'Site'(x23)' ' ' 2" 2"
1.5" 1.5"
Average' 1" 1"
0.5" 0.5" Cone''Production'(average'number'of'cones'per' branch'per'tree''for'all'trees'sampled) 0" 0" 0" 50" 100" 150" 0" 50" 100" 150"
Medium''Elevation'Site'(x22)' Medium''Elevation'Site'(x23)' ' 1.2" 1.2" 1" 1" 0.8" 0.8" 0.6" 0.6" 0.4" 0.4"
0.2" 0.2" 0" 0" 0" 20" 40" 60" 80" 100" 120" 0" 20" 40" 60" 80" 100" 120"
' Low''Elevation'Site(x22)' Low''Elevation'Site'(x23)' ' 1" 1"
0.8" 0.8" " 0.6" 0.6"
0.4" 0.4" 0.2" 0.2"
0" 0" " 0" 20" 40" 60" 80" 100" 120" 0" 20" 40" 60" 80" 100" 120" " Summer'Precipitation'from'May'to'July'(mm)''
Figure 13. Average cone production and summer precipitation (May to July) from two and three years prior to mature seed cone production. Shows a significant relationship between summer precipitation (May to July) from three years prior to mature seed cone production and average cone production at the high elevation site.
30
2 Variable Bi P-value R Annual Precipitation (x-2) -0.0011 0.2556 0.0912 (November to October) Summer Precipitation (x-3) 0.0184 0.0006 0.5841 (May to July)
Summer Temperature (x-2) -0.0026 0.6329 0.0167 (May to July) Table 4. Single linear regression analysis results of promising climate variables including the regression coefficient (Bi), p-value, and R. The only significant variable was summer precipitation from three years prior to mature cone production from May to July.
At the medium and low elevation sites, there was no significant relationship between summer precipitation (May to July) from three years prior to mature seed cone production and average cone production (Figure 13). There was no significant relationship between summer precipitation (May to July) from two years prior to mature seed cone production and average cone production at any of the elevation sites (Figure
13). There was no significant relationship between other climate variables and cone production at high and medium sites.
Cone production was compared to annual precipitation (November to October) two years and three years prior to mature cone production and no significant relationship was found (Figure 14). 31
High%Elevation%Site%(x12)% High%Elevation%Site%(x13)% " Average% 2" 2"
1.5" 1.5" Cone%%Production
1" 1"
0.5" 0.5"
0" 0" % (average%number%of%cones%per%branch%per%tree%%for%all%trees% 0" 200" 400" 600" 800" 1000" 0" 200" 400" 600" 800" 1000"
Medium%%Elevation%Site%(x12)% Medium%%Elevation%Site%(x13)% 1.2" 1.2"
sampled) 1" 1" 0.8" 0.8"
% 0.6" 0.6"
0.4" 0.4" 0.2" 0.2"
0" 0" 0" 200" 400" 600" 800" 1000" 0" 200" 400" 600" 800" 1000"
Low%%Elevation%Site%(x12)% Low%%Elevation%Site%(x13)% 1" 1"
0.8" 0.8"
0.6" 0.6" 0.4" 0.4"
0.2" 0.2"
0" 0" 0" 200" 400" 600" 800" 1000" 0" 200" 400" 600" 800" 1000"
Annual%Precipitation%(November%to%October)%from%two%and%three%years%prior%to% mature%cone%production%(°C)%
Figure 14. Cone production and annual precipitation (November to October) from two and three years prior to mature seed cone production.
Cone production was also compared to growing season temperature (March to
October) two years and three years prior to mature cone production and no significant relationship was found (Figure 15). 32
" " "" High'Elevation'Site'(x22)' High'Elevation'Site'(x23)' ' 2" 2" "
Average' 1.5" 1.5"
1" 1"
Cone''Production'(average'number'of'cones'per'branch' 0.5" 0.5"
0" 0" 10" 10.5" 11" 11.5" 12" 12.5" 13" 10" 10.5" 11" 11.5" 12" 12.5" 13"
Medium''Elevation'Site'(x22)' Medium''Elevation'Site'(x23)' ' for'all'trees'sampled) 1.2" 1.2" 1" 1"
0.8" 0.8" 0.6" 0.6"
0.4" 0.4" 0.2" 0.2" ' 0" 0" 10" 10.5" 11" 11.5" 12" 12.5" 13" 10" 10.5" 11" 11.5" 12" 12.5" 13"
Low''Elevation'Site'(x22)' Low''Elevation'Site'(x23)' ' 1" 1" " per'tree'' "0.8" 0.8" "0.6" 0.6" " 0.4" 0.4" " 0.2" 0.2" 0" 0" 10" 10.5" 11" 11.5" 12" 12.5" 13" +0.2" 10" 10.5" 11" 11.5" 12" 12.5" 13"
Growing'Season'Temperature'from'March'to'October'(°C)'
Figure 15. Average cone production and growing season temperature (March to October) from two and three years prior to mature seed cone production.
Comparing summer temperature during ovule and pollen meiosis (May to July), two and three years prior to mature cone production, respectively, there was no significant correlation between temperatures and cone production at any of the elevation sites (Figure 16). 33
High%Elevation%Site%(x12)%% High%Elevation%Site%(x13)% 2" 2" "
Average% 1.5" 1.5"
1" 1" Cone%%Production%(average%number%of%cones%per%branch%per%tree%% 0.5" 0.5"
0" 0" 10" 11" 12" 13" 14" 15" 16" 10" 11" 12" 13" 14" 15" 16"
for%all%trees%sampled) Medium%%Elevation%Site%(x12)% Medium%%Elevation%Site%(x13)%
1.2" 1.2" 1" 1" 0.8" 0.8" 0.6" 0.6"
0.4" 0.4"
0.2" 0.2"
% 0" 0" 10" 11" 12" 13" 14" 15" 16" 10" 11" 12" 13" 14" 15" 16"
Low%%Elevation%Site%(x12)%% Low%%Elevation%Site%(x13)%
1" 1"
0.8" 0.8"
0.6" 0.6" 0.4" 0.4"
0.2" 0.2" 0" 0" 10" 11" 12" 13" 14" 15" 16" 10" 11" 12" 13" 14" 15" 16" Summer%temperature%(May%to%July)%from%two%and%three%years%prior%to%mature%cone% production%(°C)%
Figure 16. Cone production and summer temperature (May-July) from two years and three years prior to mature seed cone production
Discussion
The research questions for this study were 1.) How does pinyon pine cone production vary across an elevational gradient? and 2.) What is the relationship between annual variability in climate (summer temperature, growing season temperature, summer precipitation, and annual precipitation) and cone production, and how does this relationship vary across an elevational gradient? 34
Cone production was expected to be negatively associated with late summer temperature and growing season temperature and positively associated with summer precipitation and annual precipitation, with lower elevation trees more sensitive to annual fluctuations in climate. Trees that are sensitive to annual fluctuations in climate are likely to experience decreased seed production with climate change, which can in turn negatively affect wildlife by reducing available resources and habitat. If low elevation trees are more affected, wildlife may be forced to move to higher elevation to forage and find shelter, changing community interactions and population structures in higher elevation areas.
In contrast to my predictions, there was only a significant relationship between cone production and summer precipitation (May to July) from three years prior to mature seed cone production at the high elevation site. There was no relationship between cone production and summer precipitation at the medium and low elevation sites. There were also no significant relationships between cone production and the other climate variables
(late summer temperature, growing season temperature, and annual precipitation). Low elevation cone data were not sufficient, with only two mast seeding events over a 15-year period, therefore, it could not be tested whether or not lower elevation trees were more sensitive to annual fluctuations in climate. The lack of sufficient low elevation cone data may be because low elevation trees were especially affected by previous drought conditions. Climate conditions have not been favorable in the past 15 years in this region.
In the western United States, the previous severe drought was a multi-year long drought lasting from 2002 to 2004 (Cook, Woodhouse, Eakin, Meko, & Stahle 2004). Trees having less stored carbohydrate reserves from long term drought may play a role in 35 limiting mast seeding activity. There could also be a resource allocation towards growth that had been taking away from development of reproductive structures. Trees facing environmental constraints may not place resources in either growth or reproduction and instead into carbohydrate reserves.
When testing for resource allocation towards growth, I tested the relationship between cone production at each site against tree size and canopy area and did not find any significant relationships between cone production and tree size or canopy area. Cone production at branch level and at tree level were compared to average DRC to find the relationship between tree size and cone production. The scatterplot with cone production and average DRC showed a slight left skew in distribution (Figure 12), which indicates that there are slightly more cones per branch and per tree in trees with a lower average
DRC. Despite the findings in an Abies Alba study that found that large trees were the most productive (Davi et al. 2016), in this study, smaller trees with a lower DRC produced more cones, indicating a possible resource allocation towards growth instead of reproduction in some trees.
There was a significant difference between the high, medium, and low elevation sites in cone production. The high elevation site produced a significantly larger amount of seeds compared to the medium and low sites, both in terms of the number of mast seeding events and cone production during a masting event. Since cone production was greater at the high elevation site, trees at low elevation sites may be more susceptible to climate change than trees at higher elevations. Alternatively, higher seed production at the high elevation site may be because high elevation trees generally produce more seeds than trees at low elevation for Pinus monophylla. 36
The significant relationship at the high elevation site between cone production and summer precipitation (May to July) from three years prior to mature seed cone production suggests that Pinus monophylla stores resources prior to a mast seeding event.
There is evidence that a mast seeding event can deplete a tree of stored nutrients (Sala,
Hopping, Mcintire, Delzon & Crone 2012). After depletion of stored resources from a mast seeding event, the tree would subsequently need to reacquire more nutrients over time. The positive relationship between summer precipitation and cone production indicates that precipitation may be a limiting factor on cone production. With predicted decreases in precipitation in the Great Basin, climate change may significantly affect cone production or mast seeding event frequency.
The relationship between cone production and summer precipitation supported the resource-matching hypothesis, in which climate factors influence seed production and favorable climate can act as a cue to initiate masting. The results did not provide information that opposed or supported the other mast seeding hypotheses (predator satiation, pollination efficiency, and animal dispersal).
The lack of relationship between cone production and summer precipitation at the medium and low elevation sites could have been due to a variety of reasons. The health of the trees could have played a factor in masting activity at the lower elevation sites.
Although we sampled for healthy looking trees, a healthy tree this year could have experienced low vigor over the past 15 years. Previous droughts have affected water resource availability and have negatively impacted tree health (Breshears et al. 2008), possibly making it more difficult for trees to track masting signals. Lower elevation trees could have been more heavily impacted by the previous droughts. Pinus uncinata trees at 37 low-elevation sites were the most negatively affected by warm and dry summer conditions (Galvan, Camarero, & Gutiérrez 2014). At lower elevations, precipitation amounts tend to be lower and temperatures tend to be higher and lower precipitation and higher temperatures result in accelerated drying of soil, reduced water availability, and increased tree transpiration rates (Gworek, Vander Wall, & Brussard 2007). Insects and diseases could have also plagued the tree stands in this study in the past, with drought, insects, and diseases contributing heavily to tree mortality in pinyon-juniper woodlands during the 2002 drought (Shaw, Steed & DeBlander 2005). Dwarf mistletoe, a source of possible health problems (Parker, Chambers, & Mathiasen 2017), was found on 37.5% of the sampled trees.
The observed lack of correlation between the other climate variables and cone production may result from many possible factors. The climate data that I used were not obtained from local weather stations, none of which are located nearby the study area, and instead were obtained as interpolated climate data from the PRISM Climate Group.
Such climate data are imprecise for a given location, although accurate at more regional scales. Precipitation in the region is highly localized, making it difficult to obtain accurate climate data. With future studies, it would be preferable to obtain local climate data using portable weather stations.
My focal species may not react similarly to climate change as other related species. Pinus monophylla is typically found in arid sites, which can affect mast seeding event frequency. There has not been much prior research on mast seeding activity in this species, therefore more research on this species is needed. 38
Future research should be expanded to include data collected from sites from multiple locations. My study only had one replicate each for low, medium, and high elevation sites. One similar study had included 9 sites (Redmond, Forcella, & Barger
2012) while another similar study had sampled 13 sites (Buechling, Martin, Canham,
Shepperd, & Battaglia 2016). With limited sites, if one site was not productive, then the study would be drastically limited. In this case, there were not sufficient cone data at the low elevation site, which limited the data analyses.
The relatively small number of trees sampled within each site could also have limited this study. I sampled 30 trees (10 per site) and not all of them (5 trees total) had cone scars in the last 15 years. Similar studies had sample sizes between 36 to 90 trees
(Redmond, Forcella, & Barger 2012), 195 trees (Buechling, Martin, Canham, Shepperd,
& Battaglia 2016), and as large as 607 trees (Mooney, Linhart, & Snyder 2011). A larger sample size would allow for variation and outliers in cone data to be identified.
Also, research is needed over a longer time period. This study looked at cone production in the last 15 years. One study that looked at seed production over 40 years found an especially significant mast year 36 years into the study. However, the cone abscission scar method limits how many years of data can be found since cone scars fade over time and become harder to distinguish; therefore, another method may be needed to quantify cone production over a longer time period.
39
Conclusion
Climate change may negatively affect tree reproduction, particularly in mast seeding tree species. My focal species was Pinus monophylla, a dominant, widespread mast seeding conifer of the Great Basin. I studied how cone production varied across an elevational gradient and the relationship between climate (summer temperature, growing season temperature, summer precipitation, and annual precipitation) and cone production. Cone production was highest at the high elevation site. The results supported the resource- matching hypothesis.; a significant relationship was found between cone production and summer precipitation (May to July) from three years prior to mature cone production.
The significant relationship at the high elevation site between cone production and summer precipitation (May to July) from three years prior to mature seed cone production suggests that Pinus monophylla stores resources prior to a mast seeding event and that precipitation can be a limiting factor on cone production. With predicted decreases in precipitation due to climate change, cone production may be significantly affected in Pinus monophylla and other mast seeding tree species. Further, continued research is needed to better understand the relationship between cone production and climate change.
40
References
Allen, C. D., Macalady, A.K., Chenchouni, H., Bachelet, D., Mcdowell, N., Vennetier, M.,
Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H., Cobb, N. (2010). A global
overview of drought and heat-induced tree mortality reveals emerging climate change
risks for forests. Forest Ecology and Management, 259(4), 660–684.
doi:10.1016/j.foreco.2009.09.001
Allen, R. B., Hurst, J. M., Portier, J., & Richardson, S. J. (2014). Elevation-dependent
responses of tree mast seeding to climate change over 45 years. Ecology and Evolution,
4(18), 3525-3537. doi:10.1002/ece3.1210
Ayari, A., Moya, D., Rejeb, M., Mansoura, A. B., Albouchi, A., Heras, J. D., Henchi, B.
(2011). Geographical variation on cone and seed production of natural Pinus halepensis
Mill. forests in Tunisia. Journal of Arid Environments, 75(5), 403-410.
doi:10.1016/j.jaridenv.2011.01.001
Battles, J. J., Robards, T., Das, A., Waring, K., Gilless, J. K., Biging, G., & Schurr, F. (2007).
Climate change impacts on forest growth and tree mortality: a data-driven modeling
study in the mixed-conifer forest of the Sierra Nevada, California. Climatic Change,
87(S1), 193-213. doi:10.1007/s10584-007-9358-9
Breshears, D. D., Cobb, N. S., Rich, P. M., Price, K. P., Allen, C. D., Balice, R. G., Romme,
W. H., Kastens, J. H., Floyd, M. L., Belnap J., Anderson, J. J., Myers O. B., & Meyer, C.
W. (2005). Regional vegetation die-off in response to global-change-type drought.
Proceedings of the National Academy of Sciences, 102(42), 15144-15148. doi:
10.1073/pnas.0505734102 41
Breshears, D. D., Myers, O. B., Meyer, C. W., Barnes, F. J., Zou, C. B., Allen, C. D., . . .
Pockman, W. T. (2009). Tree die-off in response to global change-type drought: mortality
insights from a decade of plant water potential measurements. Frontiers in Ecology and
the Environment,7(4), 185-189. doi:10.1890/080016
Brubaker, L. B. (1986). Responses of tree populations to climatic change. Vegetatio, 67(2),
119-130. doi:10.1007/bf00037362
Buechling, A., Martin, P. H., Canham, C. D., Shepperd, W. D., & Battaglia, M. A. (2016).
Climate drivers of seed production in Picea engelmanniiand response to warming
temperatures in the southern Rocky Mountains. Journal of Ecology, 104(4), 1051-1062.
doi:10.1111/1365-2745.12572
Calama, R., Gordo, F. J., Mutke, S., & Montero, G. (2008). An empirical ecological-type
model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau
(Spain). Forest Ecology and Management, 255(3-4), 660-673.
doi:10.1016/j.foreco.2007.09.079
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M., & Stahle, D. W. (2004). Long-
Term Aridity Changes in the Western United States. Science, 306(5698), 1015-1018.
doi:10.1126/science.1102586
Curran, L. M., & Leighton, M. (2000). Vertebrate responses to spatiotemporal variation in
seed production of mast-fruiting Dipterocarpaceae. Ecological Monographs, 70(1), 101.
doi:10.2307/2657169
Cutini, A., Chianucci, F., Chirichella, R., Donaggio, E., Mattioli, L., & Apollonio, M. (2013).
Mast seeding in deciduous forests of the northern Apennines (Italy) and its influence on 42
wild boar population dynamics. Annals of Forest Science, 70(5), 493-502.
doi:10.1007/s13595-013-0282-z
Davi, H., Cailleret, M., Restoux, G., Amm, A., Pichot, C., & Fady, B. (2016). Disentangling
the factors driving tree reproduction. Ecosphere,7(9). doi:10.1002/ecs2.1389
Elias, T.S. (1980). The complete: Trees of North America: Field Guide and natural history.
New York, NY: Van Nostrand Reinhold Co.
Evans, R. A. (1988). Management of pinyon-juniper woodlands. Ogden, UT: U.S. Department
of Agriculture, Forest Service, Intermountain Research Station. 34.
Fernández-Martínez, M., Belmonte, J., & Espelta, J. M. (2012). Masting in oaks:
Disentangling the effect of flowering phenology, airborne pollen load and drought. Acta
Oecologica, 43, 51-59. doi:10.1016/j.actao.2012.05.006
Forcella, F. (1981). Ovulate cone production in pinyon: Negative exponential relationship with
late summer temperature. Ecology, 62(2), 488–491. doi:10.2307/1936722
Forcella, F. (1981). Estimating pinyon cone production in New Mexico and western
Oklahoma. The Journal of Wildlife Management, 45(2), 553. doi:10.2307/3807947
Galván J. D., Camarero, J. J., & Gutiérrez, E. (2014). Seeing the trees for the forest: Drivers of
individual growth responses to climate in Pinus uncinata mountain forests. Journal of
Ecology, 102(5), 1244-1257. doi:10.1111/1365-2745.12268
Gworek, J. R., Vander Wall, S. B., & Brussard, P. F. (2007). Changes in biotic interactions
and climate determine recruitment of Jeffrey pine along an elevation gradient. Forest
Ecology and Management, 239(1), 57-68. doi:10.1016/j.foreco.2006.11.010 43
Hanson, P. J., & Weltzin, J. F. (2000). Drought disturbance from climate change: Response of
United States forests. Science of The Total Environment, 262(3), 205-220.
doi:10.1016/s0048-9697(00)00523-4
IPCC (Intergovernmental Panel on Climate Change). (2001). Climate Change 2001: the
Scientific Basis. Cambridge University Press, Cambridge.
Janzen, D. H. (1971). Seed predation by animals. Annual Review of Ecology and Systematics,
2(1), 465-492. doi:10.1146/annurev.es.02.110171.002341
Jansen, P. A., Bongers, F., & Hemerik, L. (2004). Seed Mass And Mast Seeding Enhance
Dispersal By A Neotropical Scatter-Hoarding Rodent. Ecological Monographs,74(4),
569-589. doi:10.1890/03-4042
Kelly, D. (1994). The evolutionary ecology of mast seeding. Trends in Ecology & Evolution,
9(12), 465-470. doi:10.1016/0169-5347(94)90310-7
Kelly, D., Geldenhuis, A., James, A., Holland, E. P., Plank, M. J., Brockie, R. E., . . . Byrom,
A. E. (2012). Of mast and mean: differential-temperature cue makes mast seeding
insensitive to climate change. Ecology Letters,16(1), 90-98. doi:10.1111/ele.12020
Kelly, D., & Sork, V. L. (2002). Mast seeding in perennial plants: Why, how, where? Annual
Review of Ecology and Systematics, 33, 427–447.
doi:10.1146/annurev.ecolsys.33.020602.095433
Kikuchi S. (1968). A research on the fruit-bearing of beech forests for the natural
regeneration. Yamagata Univ. Agric. Sci. 50: 221-306 [In Japanese with English
summary] . 44
Koenig, W. D., Mumme, R. L., Carmen, W. J., & Stanback, M. T. (1994). Acorn Production
by Oaks in Central Coastal California: Variation within and among Years. Ecology,75(1),
99-109. doi:10.2307/1939386
Koenig, W. D., & Knops, J. M. (2013). Large-scale spatial synchrony and cross-synchrony in
acorn production by two California oaks. Ecology, 94(1), 83-93. doi:10.1890/12-0940.1
Koenig, W. D., Knops, J. M., Carmen, W. J., & Pearse, I. S. (2015). What drives masting? The
phenological synchrony hypothesis. Ecology, 96(1), 184-192. doi:10.1890/14-0819.1
Kon, H., Noda, T., Terazawa, K., Koyama, H., & Yasaka, M. (2005). Evolutionary advantages
of mast seeding in Fagus crenata. Journal of Ecology, 93(6), 1148-1155.
doi:10.1111/j.1365-2745.2005.01040.x
Krebs, C., O’Donoghue, M., Taylor, S., Kenney, A., Hofer, E., & Boutin, S. (2017). Predicting
white spruce cone crops in the boreal forests of southern and central Yukon. Canadian
Journal of Forest Research,47(1), 47-52. doi:10.1139/cjfr-2016-0180
LaMontagne, J. M., Peters, S., & Boutin, S. (2005). A visual index for estimating cone
production for individual white spruce trees. Canadian Journal of Forest Research,
35(12), 3020-3026. doi:10.1139/x05-210
LaMontagne, J. M ., & S. Boutin. (2007). Local-scale synchrony and variability in mast seed
production patterns of Picea glauca. Journal of Ecology 95,991 –1000.
doi:10.1111/j.1365-2745.2007.01266.x
LaMontagne, J. M ., & S. Boutin. (2009). Quantitative methods for defining mast-seeding
years across species and studies. Journal of Vegetation Science 20, 745 –753. doi:
10.1111/j.1654-1103.2009.01068.x 45
Lanner, R.M. (1981). The piñon pine: a natural and cultural history. University of Nevada
Press.
Lanner, Ronald M. (1998). Seed dispersal in Pinus. In: Richardson, David M., ed. Ecology and
biogeography of Pinus. Cambridge, UK: The Press Syndicate of the University of
Cambridge. 281-295.
Lenoir, J., Gegout, J. C., Marquet, P. A., Ruffray, P. D., & Brisse, H. (2008). A Significant
Upward Shift in Plant Species Optimum Elevation During the 20th Century. Science,
320(5884), 1768-1771. doi:10.1126/science.1156831
Li, H., & Zhang, Z. (2007). Effects of mast seeding and rodent abundance on seed predation
and dispersal by rodents in Prunus armeniaca (Rosaceae). Forest Ecology and
Management, 242(2-3), 511-517. doi:10.1016/j.foreco.2007.01.063
Liang, E., Leuschner, C., Dulamsuren, C., Wagner, B., & Hauck, M. (2015). Global warming-
related tree growth decline and mortality on the north-eastern Tibetan plateau. Climatic
Change, 134(1-2), 163-176. doi:10.1007/s10584-015-1531-y
Loewe-Muñoz, V., Balzarini, M., Álvarez-Contreras, A., Delard-Rodríguez, C., & Navarro-
Cerrillo, R. M. (2016). Fruit productivity of Stone pine (Pinus pinea L.) along a climatic
gradient in Chile. Agricultural and Forest Meteorology, 223, 203-216.
doi:10.1016/j.agrformet.2016.04.011
Mantgem, P. J., Stephenson, N. L., & Keeley, J. E. (2006). Forest reproduction along a
climatic gradient in the Sierra Nevada, California. Forest Ecology and
Management,225(1-3), 391-399. doi:10.1016/j.foreco.2006.01.015 46
McCartan, S. & Jinks, R. (2015). Upgrading Seed Lots of European Silver Fir (Abies alba
Mill.) using Imbibition-Drying-Separation. Tree Planters Notes, Volume 58, Number 2
(2015) pp. 21-27.
Mooney, K. A., Linhart, Y. B., & Snyder, M. A. (2011). Masting in ponderosa pine:
Comparisons of pollen and seed over space and time. Oecologia, 165, 651–661.
doi:10.1007/s00442-010-1742-x
Moreira, X., Abdala-Roberts, L., Linhart, Y. B., & Mooney, K. A. (2015). Effects of climate
on reproductive investment in a masting species: Assessment of climatic predictors and
underlying mechanisms. Journal of Ecology, 103(5), 1317-1324. doi:10.1111/1365-
2745.12434
Morgan, P. & Bunting , S. C. (1990). Fire effects in whitebark pine forests. In: Schmidt,
Wyman C.; McDonald, Kathy J., compilers. Proceedings--symposium on whitebark pine
ecosystems: ecology and management of a high-mountain. 166-170.
Mueller, R. C., Scudder, C. M., Porter, M. E., Talbot Trotter, R., Gehring, C. A., & Whitham,
T. G. (2005). Differential tree mortality in response to severe drought: Evidence for long-
term vegetation shifts. Journal of Ecology, 93, 1085–1093. doi: 10.1111/j.1365-
2745.2005.01042.
Mutke, S., Gordo, J., & Gil, L. (2005). Variability of Mediterranean Stone pine cone
production: Yield loss as response to climate change. Agricultural and Forest
Meteorology, 132(3-4), 263-272. doi:10.1016/j.agrformet.2005.08.002
Nienstaedt, H. & Zasada, J. C. (1990). Picea glauca (Moench) Voss white spruce. In: Burns,
Russell M.; Honkala, Barbara H., technical coordinators. Silvics of North America.
Conifers. Agric. Handb. 1, 204-226. 47
Oliver, W. W., Ryker, R.A. (1990). Silvics of North America. Conifers. Agric. Handb.1, 413-
424.
Parker, T. J., Chambers, C. L., & Mathiasen, R. L. (2017). Dwarf Mistletoe and Breeding Bird
Abundance in Ponderosa Pine Forests. Western North American Naturalist ,77(1), 40-50.
doi:10.3398/064.077.0106
Patterson, T. W., & Knapp, P. A. (2016). Stand Dynamics Influence Masting/Radial Growth
Relationships in Pinus palustris Mill. Castanea,81(4), 314-322. doi:10.2179/16-085
Pérez-Ramos, I. M., Ourcival, J. M., Limousin, J. M., & Rambal, S. (2010). Mast seeding
under increasing drought: Results from a long-term data set and from a rainfall exclusion
experiment. Ecology, 91, 3057–3068. doi:10.1890/09-2313.1
Pérez-Ramos, I. M., Padilla-Díaz, C. M., Koenig, W. D., & Marañón, T. (2015).
Environmental drivers of mast-seeding in Mediterranean oak species: does leaf habit
matter? Journal of Ecology,103(3), 691-700. doi:10.1111/1365-2745.12400
Piovesan, G., & Adams, J. M. (2001). Masting behaviour in beech: linking reproduction and
climatic variation. Canadian Journal of Botany, 79(9), 1039-1047. doi:10.1139/cjb-79-9-
1039
Plaut, J. A., Wadsworth, W. D., Pangle, R., Yepez, E. A., Mcdowell, N. G., & Pockman, W.
T. (2013). Reduced transpiration response to precipitation pulses precedes mortality in a
piñon-juniper woodland subject to prolonged drought. New Phytologist, 200(2), 375-387.
doi:10.1111/nph.12392
PRISM Climate Group, Oregon State U. (n.d.). Retrieved March 06, 2017, from
http://www.prism.oregonstate.edu/ 48
Redmond, M. D., Forcella, F., & Barger, N. N. (2012). Declines in pinyon pine cone
production associated with regional warming. Ecosphere, 3(12),1-14. doi:10.1890/ES12-
00306.1
Redmond, M. D., Weisberg, P. J., Cobb, N. S., Gehring, C. A., Whipple, A. V., & Whitham,
T. G. (2016). A robust method to determine historical annual cone production among
slow-growing conifers. Forest Ecology and Management, 368, 1-6.
doi:10.1016/j.foreco.2016.02.028
Roland, C. A., Schmidt, J. H., & Johnstone, J. F. (2013). Climate sensitivity of reproduction in
a mast-seeding boreal conifer across its distributional range from lowland to treeline
forests. Oecologia, 174(3), 665-677. doi:10.1007/s00442-013-2821-6
Romme, W. H., Allen, C. D., Bailey, J. D., Baker, W. L., Bestelmeyer, B. T., Brown, P. M.,
…Weisberg, P. J. (2009). Historical and Modern Disturbance Regimes, Stand Structures,
and Landscape Dynamics in Piñon–Juniper Vegetation of the Western United States.
Rangeland Ecology & Management, 62(3), 203-222. doi:10.2111/08-188r1.1
Sala, A., Hopping, K., Mcintire, E. J., Delzon, S., & Crone, E. E. (2012). Masting in whitebark
pine (Pinus albicaulis) depletes stored nutrients. New Phytologist,196(1), 189-199.
doi:10.1111/j.1469-8137.2012.04257.x
Shaw, J. D., Steed, B. E., & DeBlander, L. T. (2005). Forest Inventory and Analysis (FIA)
Annual Inventory Answers the Question: What Is Happening to Pinyon-Juniper
Woodlands? Journal of Forestry,103, 280-285.
Strong, C., Zuckerberg, B., Betancourt, L., Koenig, J.,& Walter, D. (2015). Climatic dipoles
drive two principal modes of North American boreal bird irruption. Proceedings of the 49
National Academy of Sciences of the United States of America. 121(21). Doi:
10.1073/pnas.1418414112.
Thabeet, A., Vennetier, M., Gadbin-Henry, C., Denelle, N., Roux, M., Caraglio, Y., & Vila, B.
(2009). Response of Pinus sylvestris L. to recent climatic events in the French
Mediterranean region. Trees, 23(4), 843-853. doi:10.1007/s00468-009-0326-z.
Tompkins, D. M., Byrom, A. E., & Pech, R. P. (2013). Predicted responses of invasive
mammal communities to climate-related changes in mast frequency in forest ecosystems.
Ecological Applications, 23(5), 1075-1085. doi:10.1890/12-0915.1
Tubbs, Carl H.; Houston, David R. (1990). Fagus grandifolia Ehrh. American beech. In:
Burns, Russell M.; Honkala, Barbara H., technical coordinators. Silvics of North
America.(1).325-332.
Witt, Chris. (2015). Using adjunct forest inventory methodology to quantify pinyon jay habitat
in the great basin. In: Stantion, Sharon M.;Christensen, Glenn A. Pushing boundaries:
new directions in inventory techniques and applications: Forest Inventory and Analysis
(FIA) symposium 2015. 302.
Zlotin, R., & Parmenter, R. (2008). Patterns of mast production in pinyon and juniper
woodlands along a precipitation gradient in central New Mexico (Sevilleta National
Wildlife Refuge). Journal of Arid Environments, 72(9), 1562-1572.
doi:10.1016/j.jaridenv.2008.02.021