Introduction annual average temperatures during 1900-1930, cooling of

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Introduction annual average temperatures during 1900-1930, cooling of

Introduction

Temperate forests are a key component
of many terrestrial ecosystems, are vital habitat to a myriad of species, and play
a major role in the global carbon cycle. In the coming decades, forest
communities in the Great Lakes region will be heavily impacted by the large
magnitude and rapid rate of climate change—along with increased biotic
stressors and forest fragmentation (Scheller & Mladenoff 2008)—through
expansion, contraction, and/or shift in forest species’ distributions (Handler
et a. 2014). Several studies have documented severe weather patterns (Changnon
2011), changes in timing of lake ice (Magnuson et al. 2000), changes in tree
phenology (Andresen et al. 2012), and changes in wildlife distributions (Myers
et al. 2009) that indicate climate change is already occurring (Handler et al.
2014). Some species in the Great Lakes have shifted toward the poles and been replaced
by more southerly species that find the new climate suitable (Baule et al. 2014).
There is concern of whether the migration of species’ ranges can track the rate
of global warming (Davis & Shaw 2001; Davis 1989; Iverson et al. 2004).
Populations at the margins of their species distribution are expected to be
most impacted (Parmesan 2006). Understanding how forest communities in the
Great Lakes region are affected by climate change is critical to determining
how to best manage them for the future.

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Similar to global trends in the
past century, Michigan experienced steady annual average temperatures during
1900-1930, cooling of about 0.5°C during 1930-1980, and rapid warming of about 1.3°C
after 1980 (Figure 1) (Andresen 2007). Over the Midwest, temperature rose at an
increasing rate of approximately 0.059°C per decade during 1900-2010, 0.12°C
per decade during 1950-2010, and 0.26°C per decade during 1979-2010 (Andresen
et al. 2012), with 2000-2012 having the fastest rate in the Great Lakes region (Baule
et al. 2014). Temperatures during winter and nighttime have increased more than
any other season and daytime temperatures (Andresen et al. 2012). The growing
season increased by 9 days, on average, in the Midwest during 1991-2012
compared to 1901-1960 (Walsh et al. 2014). In a study by Hayhoe et al. (2010), predicting
climate change for the Great Lakes using 1961-1990 as the historical reference
period, annual average temperatures are expected to increase by 1.4 ± 0.6°C during
2010-2039 with greater increases in the winter and smaller increases in spring
and summer. During 2040-2069, temperatures are expected to increase by 2 ±
0.7°C under lower and 3 ± 1°C under higher emissions, with greater increases in
the summer than winter and spring. During 2070-2099, temperatures are expected
to increase by 3 ± 1°C under lower and 5 ± 1.2°C under higher emissions, with
temperature increases at least a degree higher in the summer compared to
winter. Temperature is expected to increase the most at high northern latitudes
(IPCC 2013).

In Michigan, annual precipitation
decreased during 1895-1930, increased from late-1930s to 1990 except for a
relatively dry period in the late 1950s/early 1960s, and leveled off after 1990
(Figure 2) (Andresen 2007; Andresen et al. 2012). The statewide upward trend
was associated with an overall increase in number of wet days and 2-day
consecutive wet days, rather than an increase in heavy rainfall (more than 25
mm total annual precipitation) (Andresen 2007). Over the Midwest, annual
precipitation generally increased in the past several decades, mostly due to
increased intensity of heavy rainfalls (Pryor et al. 2009). Between 1900 and
2012, annual precipitation increased by 11% with a 37% increase in the amount
of precipitation falling in the heaviest 1% of precipitation days during
1958-2012 (Baule et al. 2014). The number and areal extent of extreme droughts decreased
in the central Midwest during 1916-2007 (Mishra et al. 2010). Intense
precipitation events are expected to continue increasing (Baule et al. 2014),
but some models predict that total annual precipitation will remain unchanged
while seasonal distribution of precipitation will change substantially (Baule
et al. 2014). Hayhoe et al. (2010) predict that winter and spring precipitation
will increase 20% under lower and 30% under higher emission scenarios, with
larger increases around the end of the century compared to near terms. Summer
precipitation is expected to slightly increase or decrease by up to 50%. More
precipitation is expected to fall as rain rather than snow due to warmer winter
temperatures, and snow days are expected to decrease 30-50% by the end of the
century under lower scenarios and 45-60% under higher emission scenarios.

 

My research question concerns how
the productivity and vigor of populations at their species’ northern
distribution limits are affected by climate change, such as those of tulip
poplar and white oak for which there is current knowledge gap. My approach is
to conduct a comparative study on the growth responses of tulip poplar and white
oak, which are at and near their northern distribution limits, respectively, and
sugar maple, American beech, and northern red oak, which are not at their distribution
limits (Burns & Honkala 1990), at a site in Hastings, MI (Figure 3). I will
use dendrochronology to determine the relationship between growth of each
species and climatic factors (temperature and precipitation).

 

 

Biological rationale

I assume that growth of tulip poplar and white oak in the study area are
mainly limited by low temperature because they are located at and near their
northern distribution limit, respectively (Burns & Honkala 1990). Climate primarily
influences the geographic distribution of plants, with vegetation types
corresponding to certain temperature and precipitation regimes (Box 1981;
Siefert et al. 2015). Species ranges are often limited by low temperatures and
short growing seasons at northern, high-latitudinal margins (MacArthur 1972;
Brown et al. 1996; Kramer et al. 2000; Boisvenue & Running 2006). Some populations
at their northern distribution margins have colonized land at higher latitudes
and elevations due to warmer temperatures (Kullman 2002; Chen et al. 2011). This
suggests that tulip poplar and white oak may show high sensitivity and positive
correlation to temperature in the study area.

 

I assume that growth in populations of sugar maple, American beech, and northern
red oak are less limited by temperature compared to tulip poplar and white oak
because they are situated in the middle of their geographic ranges at the study
site (Burns & Honkala 1990). This follows my first assumption such that if
northern marginal populations are limited by low temperature, and temperature
is higher along lower latitudes, then populations at lower latitudes of the
distribution will experience temperatures closer to their optimum. For this
reason, their growth may be more controlled by site-specific, non-climatic
factors such as nutrient availability and stand competition.

 

Two
studies, Tardif et al. (2001) and Goldblum & Rigg (2005), focused on tree
populations in the deciduous-boreal forest ecotone in Ontario and Montreal to study
growth-climate relationships of sugar maple, eastern hemlock, and American
beech. These species are at their northern distribution limits in this region.
All three had a negative correlation with prior-year summer temperatures
(Tardif et al. 2001; Goldblum & Rigg 2005), likely due to
temperature-induced water stress. Water stress inhibits the accumulation of carbohydrate
reserves for the next growing season (Lane et al. 1993). Sugar maple had
negative growth correlation with prior-year fall temperatures (Goldblum &
Rigg 2005), which may also be indicative of temperature-induced water stress
preventing continued accumulation of carbohydrate reserves. Sugar maple and eastern
hemlock had positive correlations with winter and spring temperatures (Goldblum
& Rigg 2005; Tardif et al. 2001). Warmer spring temperatures lengthen the
next growing season through early snow melt (Tardif et al. 2001). Warmer winter
temperatures that reach above-freezing may lead to soil saturation that can be
detrimental to growth. The positive correlation with winter temperature may be
due to autocorrelation with growing season conditions (Goldblum & Rigg
2005). American beech and eastern hemlock had negative growth correlation with
current-year summer temperature, while sugar maple had positive correlation (Tardif
et al. 2001; Goldblum & Rigg 2005). High summer temperatures induce a
higher water potential gradient around trees, leading to rapid evaporative
water loss and reduced growth (Fritts 1976). High temperatures also prevent
evaporative cooling which increases respiration rates and reduces net
photosynthesis, and therefore, growth (Fritts 1976).

 

Eastern hemlock showed positive
growth correlation with prior-year summer precipitation, while sugar maple
showed negative correlation with prior-year summer precipitation in May (Tardif
et al. 2001). High precipitation may counteract the evaporative stress induced
by high temperatures. Excessive precipitation in May was detrimental to sugar
maple growth due to soil saturation (Tardif et al. 2001). Sugar maple showed
negative growth correlation with prior-year late fall precipitation (Goldblum
& Rigg 2005). This may be due to excess water creating saturated soil
conditions that are detrimental to growth. Sugar maple, American beech, and
eastern hemlock showed positive growth correlation with current-year summer
precipitation (Tardif et al. 2001). High precipitation in the current-year
summer reduces water stress.

 

Hypotheses

I hypothesize that growth in tulip
poplar and white oak will be positively correlated with winter and current-year
spring temperatures, negatively correlated with prior-year fall and
current-year summer temperatures, negatively correlated with winter and
current-year spring precipitation, and positively correlated with prior-year
fall and current-year summer precipitation. In addition, I expect that growth
will show increased negative correlation with current-year summer temperature, and
decreased positive correlation with winter and current-year spring temperature.
I also expect that growth will show increased positive correlation with current-year
summer precipitation, and increased negative correlation with winter and current-year
spring precipitation.

 

I hypothesize that growth in sugar
maple, American beech, and northern red oak will be positively correlated with winter
and current-year spring temperatures, and negatively correlated with prior-year
fall and current-year summer temperatures, but with weaker relationships than
tulip poplar and white oak. I also hypothesize that growth will be positively
correlated with prior-year fall and current-year summer precipitation, and
negatively correlated with winter and current-year spring precipitation, but
also with weaker relationships than tulip poplar and white oak.

 

Methods and Materials

I will use tree core samples of tulip
poplar, white oak, sugar maple, American beech, and northern red oak that were previously
collected from a stand at Warner Sanctuary (Michigan Audubon Society) in
Hastings, MI (geographic coordinates, lat/long: 42.620669, -85.396601) during
2016, with generally two cores per tree for approximately 10 trees of each
species. I will obtain average monthly temperature and precipitation data for
the study area from the PRISM Climate Group at Oregon State University from
1985 to 2015 (Daly et al. 2008).

 

For each tree of each species, I will
use methods outlined by Stokes and Smiley (1968) to build skeleton plots of the
two cores and graphically cross-date them to build an individual composite plot
for the tree (Table 1). Cross-dating is the process of assigning a
chronological year to each growth ring (Stokes & Smiley 1968). For each
species, I will build a master composite plot (tentative master chronology) by
graphically cross-dating the individual composite plots. I will measure ring
widths of each tree core using CooRecorder software (Cybis Dendrochronology
2017) and feed the raw ring-width data into COFECHA statistical software (Holmes
1982) for cross-dating verification of the master chronology (Grissino-Mayer
2001). Once cross-dating errors have been resolved, the quality of cross-dating
will be determined using indicators such as mean sensitivity and series
intercorrelation. Effective mean sensitivity is between 0.1 and 0.8 and series
intercorrelation greater than 0.5 is desirable (Grissino-Mayer 2001).

 

Next, I will standardize the master
chronology using a cubic smoothing spline detrending method to remove or dampen
age-related and stand dynamic growth trends in ARSTAN statistical software (Table
1) (Cook 1983; Cook et al. 1986). The quality of the sample will be determined
using indicators such as EPS (expressed population signal) and running rbar. Residual
EPS should be greater than or equal to 0.85 for each chronological year (Speer
2010).  

 

Finally, I will run response function
analysis (Fritts 1976; Cook & Kalriukstis 1990), a multivariable principal
components regression, to determine the relationship between tree growth and
climate for each species.  I will run
evolutionary and moving intervals analysis to determine temporal changes of
dendroclimatic relationships. Both analyses will be conducted through DENDROCLIM2002
statistical software (Table 1) (Biondi & Waikul 2004).

 

 Table 1: Timeline for research activities.

Semester

Activity

Spring 2017

Cross-dating with skeleton plots;
literature review

Summer 2017

Cross-dating with skeleton plots;
ring measurement (using CooRecorder), and cross-dating verification with
COFECHA; literature review

Fall 2017

Standardization with ARSTAN;
literature review

Spring 2018

Response function and
evolutionary and moving intervals analyses with DENDROCLIM2002; literature
review; finish final research paper

 

Predicted Results

My predictions for results are
stated above in my hypotheses.

 

I plan on using tables to display information
such as number of trees, number of missing rings, mean ring width and standard
deviation, chronology length, COFECHA output (series intercorrelation and mean
sensitivity), and ARSTAN output (expressed population signal and running rbar)
for each species. This information may support my hypotheses by informing the
audience of the quality of my data, which influences the strength of my
inference.

 

I plan on using figures to display the
study area location, mean temperature and precipitation by month, the detrended
residual ARSTAN chronology with sample depth, and coefficients of evolutionary and
moving interval and response function analyses by month. The residual ARSTAN
chronologies and coefficients of response function analysis may support my
hypotheses of growth responses to seasonal climate among the range-limited
species and non-range-limited species. The coefficients of the evolutionary and
moving interval analysis may support my hypotheses of how the strength of
responses will change over time among the range-limited species.

 

Implications

The increase in productivity and
vigor of tulip poplar and white oak in Michigan could have beneficial effects
on wildlife, timber resources, and recreation. Tulip poplar is browsed by
white-tailed deer and squirrels during fall and winter, and its pollen is
consumed by hummingbirds, butterflies, and bees (Dickerson 2002). Its wood is
valuable as utility lumber, and is used in furniture and wood veneer (Dickerson
2002). It is often used for reforestation purposes because of its rapid growth
(Dickerson 2002). According to the University of Florida Institute of Food and
Agricultural Sciences (IFAS) Extension, white oak is used for browsing by
white-tailed deer, rabbits, porcupines, and beavers, and are considered an important
food source during fall and winter when there are shortages. Their acorns are
consumed by over 100 species of vertebrates. Animals such as deer, bears, and
wild turkeys eat acorns as a major part of their diet. Deer will alter their
movement patterns in response to tree mast production (Ober 2017). The
reproductive success of bears in a given year is affected by the amount of tree
mast produced the previous year, and the size of their home range reduces when
mast is abundant (Ober 2017). Wild turkeys have a similar pattern to bears in
response to mast production (Ober 2017). White oak is a source of durable wood
and is considered the most important timber oak (Tirmenstein 1991). It is used
in furniture, veneer, and flooring. Both species have direct and indirect recreational
value because they are enjoyed as landscape trees and sustain animals used for
hunting recreation, such as deer, rabbits, wild turkeys, and ring-necked
pheasants.

 

A range expansion of these two
species may potentially alter forest composition. Tulip poplar and white oak
may become more prominent at higher latitudes that are currently dominated by
maple, beech, birch, and hemlock. Pryor et al. (2014) suggest that under high
emission scenarios by the end of the century, the maple/beech/birch/hemlock forest
type will be greatly reduced and replaced by oak and hickory in the northern
Lower Peninsula of Michigan. The forest types that we are accustomed to seeing
may also change because all individual species within a forest type will not have
the same response to climate change (Pryor et al. 2014). We may see different
mixtures of co-dominants over time.

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