CAN TREE-RING CHRONOLOGIES DETERMINE THE CLIMATIC VARIABLES IN A
PICEA ENGELMANNII DISTRIBUTION MODEL?
Truettner, Charles M. (1), Cole, Kenneth L. (1), Cobb, Neil S. (1), Giles, John R. (1), and Peters, Michael J. (1)
(1)Northern Arizona University, Merriam-Powell Center for Environmental Research, Flagstaff, AZ
AMENDED ABSTRACT - Species distribution models traditionally evaluate the variables
most critical for a species by applying spatially explicit datasets of environmental
variables (e.g. topography, soil types, climate datasets) in relation to the presence
and/or absence of the species. These variables may or may not be similar to variables
derived using other methods, especially since the factors controlling a species’ limit
are unlikely to be the same over its entire range. The link between a species'
physiological processes and limiting variables within a large-scale spatial dataset is
essential to progress the science of species distribution models. In this study, 19
published tree-ring chronologies extracted from the International Tree Ring Data Bank
in Southwestern, USA are correlated with a PRISM climate dataset to understand
which climatic variables are influencing the climate-growth relationship of Picea
engelmannii Parry ex Engelm. These climate variables are then used in a species
distribution model to assist in predicting the occurrence of P. engelmannii.
Furthermore, the shift in the distribution of P. engelmannii, according to the
parameters in the historic model, is projected into a future time-step (2070 – 2099 AD)
using General Circulation Models downscaled to the PRISM 4 km grid (Garfin et al.
2010). Tree-ring chronologies and forest stand dynamics are analyzed from three
different elevation classes in the Pinaleño Mountains to validate the possible upward
migration of P. engelmannii in its southernmost distribution. Preliminary results using
both spatial datasets and tree-ring chronologies demonstrate that in this region the
distribution of P. engelmannii is strongly influenced by the severity of the early
summer drought. The species distribution model can assist land managers in decisionmaking processes pertaining to adaptation strategies of species response to 21st
century climatic warming.
Climatic Variable
ITRDB
Chronology
Response Function
Coefficient Value
Percent Contribution on
Distribution Model
July Maximum Temperature
Mount Graham
-0.188716
41.5%
Pikes Peak
0.218544
Fool Creek
0.343433
Lexan Creek
0.316675
Arapahoe
0.330258
Timberline Pass
0.303518
Milner Pass
0.225561
Cameron Pass
Medicine Bows
Peak
0.216592
Fool Creek
0.239452
Lexan Creek
0.261686
Milner Pass
0.286223
Cameron Pass
Medicine Bows
Peak
0.321327
July Minimum Temperature
February Maximum Temperature
January Maximum Temperature
January Precipitation
0.155162
54.7%
0.169234
-0.260457
Cottonwood Pass
Milner Pass
-0.204697
Cameron Pass
-0.162741
Pikes Peak
-0.212032
Niwot Ridge
-0.226795
Timberline Pass
-0.181294
Pikes Peak
-0.257046
Hidden Peak
0.225881
Sheep Trail
-0.199184
.4%
.2%
3.2%
Hawk Peak in the Pinaleño Mountains
PIEN (Picea engelmannii), ABAZ (Abies lasiocarpa var. arizonica), POTR (Populus tremuloides), PSME (Pseudotsuga menziesii), D signifies dead standing tree
Three Populations of P. engelmanni in the Pinaleño Mountains
Three populations of P. engelmannii were cored on the northeastern aspects of
Hawk Peak in the Pinaleño Mountains. The highest population is located in the
endangered Mt. Graham Red Squirrel Refugium and is one of the last stands of
closed-canopy forest above the ecotone (2,930 m to 3,100 m) defined in Stromberg
and Patten (1991). Another population is found in the ecotone, and the last is
located on an aspect directly above Soldier Creek meadow.
160
140
120
100
Determining Climatic Variables - The “correlation function” and “response function” coefficients were computed for the 19 published tree-ring
chronologies from the ITRDB in relation with 4km PRISM data from the years 1895 to 1980 using DENDROCLIM20022. Climatic variables with significant
response function coefficients found in three or more ITRDB chronologies are used as the climatic variables in the P. engelmannii model. The climatic
variables are trained on different timescales and the climatic variables in the P. engelmannii model are based on mean monthly measurements, which
leads to incongruences in the climate datasets. However, the trends seem to correspond to the monthly climatic factors influencing the climate-growth
relationship on a distribution level. A jackknife analysis was run to measure the percent contribution of each climatic variable on the distribution model.
1. These GCMs were downscaled to 4km using a bias-correction method described in Garfin et al. (2010)
2. DENDROCLIM2002 uses univariate estimates of Pearson’s product moment correlation and a principal component regression model with bootstrapped error estimates
to determine significant climatic variables affecting the climate-growth relationship of a tree-ring chronology (Biondi and Waikul 2004).
37
47
60
SEEDLING
255
38
8
21
116
40
60+
ABAZ
157
100
23
8
2
1
ABAZD
106
82
15
3
2
1
PIEN
69
8
4
2
2
1
PIEND
65
25
15
18
6
2
Saplings and Seedlings for
Ecotone
120
40
300
250
200
150
100
50
0
20
0
100
80
60
10 to 19.9
20 to 29.9
30 to 39.9
40 to 49.9
50 to 59.9
ABAZ
129
62
33
11
2
ABAZD
42
59
26
7
3
PIEN
4
3
1
PIEND
3
6
1
PSME
3
4
4
1
10
POTR
5
3
9
7
2
POTRD
2
1
1
ABAZ
ABAZD
PIEN
Sapling
141
3
5
12
Seedling
242
4
8
235
22
Ecotone Chronology Statistics
Number of Trees: 11
Series Intercorrelation : .747
Average Mean Sensitivity: .296
Saplings and Seedlings for
Soldier Creek
140
 Climatic factor influencing negative growth for three Pinaleño Mountain
populations coincides with early summer drought, similar to climatic
factors influencing species distribution model
120
100
80
60
40
20
0
POTR
60 +
Diameter Size Classes for Soldier Creek
 Probability of occurrence decreases with elevation for climate-growth
envelope of Picea engelmannii between historic distribution model and
future projected distribution model
 Stand dynamics display possible decrease in the presence of Picea
engelmannii in the Pinaleño Mountains except for the sheltered meadow
site below the Spruce-fir/Mixed Conifer ecotone
10 to 19.9 20 to 29.9 30 to 30.9 40 to 40.9 50 to 50.9
Refugium Chronology Statistics
Number of Trees: 12
Series Intercorrelation : .720
Average Mean Sensitivity: .228
Number of Individuals
Number of Individuals
Discussion Topics
ABAZ
20
Number of Individuals
occurrence for Picea engelmannii based on the climate-growth relationship determined by 19 published tree-ring chronologies from the International
Tree-Ring Data Bank (ITRDB) (Phillips et al. 2006). Parameter-elevation Regression on Independent Slope Model (PRISM) dataset from 1950 to 1999 at
4km scale provided the climate data to train the P. engelmannii model (www.prism.oregonstate.edu). Specifically, the mean of the maximum
temperature and minimum temperature for each month for each year were calculated. Across the 50 year window, absolute maximum and minimum
temperatures were selected from the average monthly data and assigned to each cell. Total monthly precipitation was averaged across the 50 year
window and assigned to each grid cell. Corresponding climate variables in five GCMs and an ensemble of 48 runs for 22 GCMs were used to project how
the climate-growth envelope would shift under the A1-B emission scenario1. The future GCM projections were classified in a thirty-year interval from
2070-2099. Forest Inventory and Analysis Database (FIA v. 5.1, http://www.fia.fs.fed.us/) provided the occurrence data to train the climate-growth
envelope. If P. engelmannii is present in a plot within the FIA Database, then the centroid of the 4km PRISM grid cell in which the presence point was
within is defined as a presence point. Thus, the monthly climatic variables (cf. max mean monthly temperature, minimum mean monthly temperature,
and mean monthly precipitation) for each 4km grid cell were used to develop the P. engelmannii climate-growth envelope. The extent of the model
includes the four corner states (CO, AZ, NM, UT) and the portion of Wyoming south of 42° N.
ABAZD
87
140
Number of Individuals
SPECIES DISTRIBUTION MODELLING METHOD - Maxent, a machine-based modelling algorithm, is used to model the probability of
PIEND
0
Diameter Size Classes for Ecotone
The diameter at breast height (DBH) for each standing tree, dead or alive, was
measured in 7 circular plots (.1 ha) with 4 seedling and sapling plots (.01 ha) in each.
Trees are classified in 10 cm DBH diameter size classes to analyze and compare
successional stages in the three populations. In addition, sapling (2.5 cm ≤ DBH < 10
cm) and seedling (DBH < 2.5 cm) counts are graphed for recruitment trends.
PIEN
SAPLING
0
(2070 - 2099)
POTR
80
20
Chronologies built from at least 11 trees with two cores per tree (south and east
facing sides) dating back to 1951 were detrended and standardized in the R statistical
software package, Dendrochronology Program Library in R (dplR) (Bunn 2008). A
Climate-Growth
modified negative-exponential curve was fitted to each tree series to eliminate the
Envelope Trained on age-growth trends. The standard chronologies were built and compared with
4/6 GCM A1-B Emission monthly PRISM data using DENDROCLIM20022. This comparison resulted in
Scenario Agreement correlation coefficients where positive values reflect an increase in growth, and
negative values a decrease.
300
250
200
150
100
50
0
Number of Individuals
180
Number of Individuals
Climate-Growth
Envelope Trained on
PRISM Data
(1950 – 1999)
Saplings and Seedling for Mt.
Graham Red Squirrel Refugium
Diameter Size Classes for Mt. Graham
Red Squirrel Refugium
10 to 19.9
20 to 29.9
30 to 39.9
40 to 49.9
50 to 59.9
60 +
ABAZ
125
76
32
8
7
2
ABAZD
68
52
11
5
2
0
PIEN
26
11
10
5
0
1
PIEND
12
13
6
4
4
1
PSME
1
0
0
0
2
8
POTR
0
1
9
10
1
1
POTRD
1
1
1
0
1
0
500
400
300
200
100
0
ABAZ
ABAZD
PIEN
PIEND
POTR
POTRD
Sapling
77
19
30
9
3
0
Seedling
382
15
54
5
189
85
Soldier Creek Chronology Statistics
Number of Trees: 15
Series Intercorrelation : .692
Average Mean Sensitivity: .272
Literature and Data Cited
Acknowledgements: I would like to thank
Merriam-Powell Center for Environmental
Research and the National Climate Change and
Wildlife Science Center (USGS) for funding my
research. In addition, I would like to thank the
Coronado National Forest (USFS) for permitting
and assistance locating field sites.
Biondi, F., and K. Waikul. 2004. DENDROCLIM2002: A C++ program for statistical calibration of climate signals in tree-ring chronologies. Computer Geosciences 30: 303-311.
Bunn, A. G. 2008. A dendrochronology program library in R (dplR). Dendrochronolgia 26: 115 – 124.
Contributors of the International Tree-Ring Data Bank, IGBP PAGES/World Data Center for Paleoclimatology, NOAA/NCDC Paleoclimatology Program, Boulder, Colorado, USA.
Garfin, G. M., Eischeid, J. K., Lenart, M. T., Cole, K. L., Ironside, K., and N. Cobb. 2010. Downscaling climate projections in topographically diverse landscapes of the Colorado Plateau
in the arid southwestern United States. The Colorado Plateau IV; Shaping Conservation Through Science and Management (ed. by C. van Riper, III, B. F. Wakeling and T. D. Sisk), pp. 22-43.
The University of Arizona Press, Tucson.
Phillips, S. J., Anderson, R. P., and Schapire, R. E. 2006. Maximum Entropy modelling of species geographic distributions. Ecological Modelling 190: 231-259.
PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu, created 17 March 2011
Stromberg, J. C. and Patten, D. T. 1991. Dynamics of the spruce-fir forests on the Pinaleno Mountains, Graham Co., Arizona. The Southwestern Naturalist 36(1): 37-48.
US Forest Service, Forest Inventory and Analysis Database, http://apps.fs.fed.us/fiadb-downloads/datamart.html, created 28 August 2012