INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2007)
Published online in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/joc.1629
A cross-taxa phenological dataset from Mohonk Lake,
NY and its relationship to climate
Benjamin I. Cook,a,b * Edward R. Cook,b Paul C. Huth,c John E. Thompson,c Anna Forsterc
and Daniel Smiley†
a
University of Virginia, Department of Environmental Sciences, 291 McCormick Road, Charlottesville, Virginia 22904, USA
b Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, NY 10964-1000, USA
c Mohonk Preserve, Daniel Smiley Research Centre, New Paltz, NY 12561, USA
ABSTRACT: We present a detailed analysis of a rare cross-taxa native species phenology dataset (plant flowering, insect
first sighting, and amphibian first sighting) from Mohonk Lake, NY. This dataset is highly unusual in North America for
its longevity of record, consistency of methodology and location, diversity of species available, and availability of local
daily meteorological data. For each phenology series, we examined the flowering and first sighting Julian calendar dates
for the existence of temporal trends. Only one of the five animal species (katydid) showed any evidence for a significant
trend in first sighting. In contrast, the plant species showed a rich mixture of temporal trends in flowering that could
be divided into four classes: woody plant–no trend, woody plant–negative trend, herbaceous plant–negative trend, and
herbaceous plant–positive trend. Many of the trends were found to be statistically significant and robust to the method of
trend estimation. The data within each of the four plant classes were also pooled as anomalies to provide more complete
temporal coverage for tests of trend robustness. The results were strongly consistent with the trends of the individual
species within each class and highly significant statistically. We next correlated the flowering and first sighting dates
against growing degree-day (GDD) summations for each day of the year to measure the sensitivity of each species to this
common form of climatic forcing on phenology. All species showed a significant sensitivity to GDD summations, with
peak correlations falling on or near the varying median flowering or first sighting dates. These results were robust whether
the GDD analyses were conducted over the complete set of observations for each species (beginning on or after 1928)
or over a latter period with a more serially complete set of cross-taxa observations (1970–2002). The GDD correlations
indicate significant climate sensitivity in all species, but the different magnitudes of correlation and timing of maximum
correlation imply that plant and animal responses to climate changes in the future will not be homogeneous for the tested
species at Mohonk Lake. Copyright  2007 Royal Meteorological Society
KEY WORDS
phenology; global change; climate variability
Received 29 December 2005; Revised 22 August 2007; Accepted 25 August 2007
1.
Introduction
Phenology is the study of the timing and occurrence of
recurring biological events or ‘phenophases’. Examples
include first flowering of plants, bud burst of trees, and
the arrival of migrating birds in spring (Chmielewski and
Roetzer, 2001; D’odorico et al., 2002; Fitter and Fitter, 2002; Walther et al., 2002), all typically expressed
as calendar year Julian dates. Because the timing of
many of these events is sensitive to environmental
conditions, investigations of trends and variability in
available phenology records may provide insight into
global and regional climate change impacts on biological systems (Parmesan and Yohe, 2003). Indeed, this
has been done for many areas, most notably in Europe
(Menzel and Fabian, 1999; Menzel, 2000) where a
* Correspondence to: Benjamin I. Cook, Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, NY 10964-1000, USA.
E-mail: [email protected]
† Deceased.
Copyright  2007 Royal Meteorological Society
plethora of long-term records are available across the
continent.
Changes in phenology due to climate can be difficult to interpret for a number of reasons, especially
when the observations are based on unmanaged ecosystems. For example, phenological observations can be
affected by changes in local land use in a way that
could obscure or distort larger scale climatic influences on
the phenophases, or changes in local biodiversity. Other
examples include the confounding influences of urban
heat islands and altitude effects (Roetzer et al., 2000),
which add local signals to any regional scale climate
forcing of phenology. In turn, the lack of climate data
local to the phenological observations can make causal
associations difficult to infer. And even when local, it
is important that the meteorological observations not be
contaminated by local non-climatic influences or observational biases.
Problems with the phenology records themselves can
also limit their usefulness for climatic investigations.
B. I. COOK ET AL.
One example is limited species selection (Schwartz and
Reiter, 2000). Because different species are likely to
exhibit different sensitivities to climate, records that
include only a few species may show an exaggerated
or damped response to external forcing. In the extreme,
this problem can take the form of ‘responder’ and ‘nonresponder’ species virtually at the same site that provide
quite different histories of response to the same largescale climatically driven environmental changes (Bradley
et al., 1999). Multiple species help remove this potential
for species bias on data interpretations and also give
a better indication of how the system as a whole is
responding. Lack of a standardized methodology for
recording the phenological observations may likewise
mean the possibility of observer bias in the records that
could obscure effects of climatic changes.
Finally, a problem especially in the United States is
the limited availability of long-term records for many
species, making it difficult to investigate trends or lower
frequency variability in a species’ phenology that may
be due to environmental changes lasting several years
to decades. One dataset in North America that begins
to attend to this problem is a phenology database first
begun by Aldo Leopold in southern Wisconsin (Bradley
et al., 1999). This record covers 74 phenophases, but
suffers from two major drawbacks: a 29-year lapse of
observations from 1947 to 1976 and the lack of local,
high quality weather data for modelling the changes
found in the phenophase records thought to be due to
climate.
Here we present a phenological dataset from southeastern New York that addresses a number of the stated
problems, i.e. cross-taxa phenological data made up of
phenophases of 19 plant and 5 animal species that
are relatively free of local non-climatic influences (e.g.
urban heat island effects) and that has the added bonus
of an accompanying long-term, high quality, and local
daily weather dataset. The phenology data come from
the Mohonk Preserve’s Daniel Smiley Research Center,
located about 135 km north of New York City, west of the
Hudson Valley village of New Paltz. The preserve sits at
about 380 m above sea level on a quartzite conglomerate
ridge above the Hudson River Valley and is far from the
influence of any urban heat island effects or large water
bodies (other than the 4.5 ha Mohonk Lake and the Hudson River about 10 km to the east). For over a century,
the Smiley family at Mohonk and later the Mohonk Preserve has been recording and collecting a wide variety of
environmental data. One of the most notable datasets is
a daily weather record begun in 1896 under the auspices
of the US Weather Bureau and that is currently part of
the National Weather Service. The records include minimum and maximum temperature, precipitation, snowfall,
and lake ice in/out dates. Beginning in the 1920s, Daniel
and Keith Smiley, founders of the Preserve, also began
recording a wide range of phenological observations in a
systematic way, including first flowering of a variety of
herbaceous and woody native plant species, first sightings
Copyright  2007 Royal Meteorological Society
of insects and amphibians, and the spring arrival dates of
migrating birds.
The most remarkable aspect of Mohonk Preserve
datasets is their consistency in both instrumentation
and methodology. The Stevenson Screen thermometer
shelter and US Weather Bureau rain gauge have never
been moved from their original locations. The rain
gauge is the same gauge installed in 1896 and the
minimum/maximum thermometer has been replaced only
a few times, always with comparable thermometers
issued by the National Weather Service. The local area
itself has also not changed extensively with regard to
urbanization, deforestation, or other land use changes.
Additionally, over the entire period of record, there have
been only five primary observers, all of whom had years
of overlap assisting their predecessors. All these factors
help reduce both observer errors and contamination of
the signals by local factors, making the observations
more likely to reflect local manifestations of larger scale
climate variability. Similarly, the phenophases have been
recorded using a consistent methodology (detailed in
Section 2).
Our investigation of the Mohonk Lake phenology
dataset examines it for trends and models it with the local
high quality weather records. We focus on phenophases
that are likely to be influenced locally, namely plant phenological records and over-wintering animals. Migratory
bird arrival dates are left for a future analysis. Section
2 describes the processing and quality control of the
phenology data, its statistical properties, and our use of
trend estimation methods and growing degree-day (GDD)
summations for modelling the phenophases. Section 3
presents the trend estimation results and the comparisons
between GDDs and the phenology records. Section 4 concludes with a summary of our results and future directions
for the work.
2.
Data and methods
Table I lists the 24-species records by scientific and common name, first year of observation, number of observations, and median first flowering or the first sighting
Julian date of the year. The phenophases within each
plant and animal group are ordered from the earliest
to the latest median date of occurrence. The 19-plant
phenophases include 18 species that range from herbaceous perennials (e.g. dwarf ginseng, marsh marigold,
and hepatica), to woody perennials (e.g. mountain laurel,
hobblebush, and shadbush), and even one tree species
(flowering dogwood). The species red-berried elder is
represented twice, once for first flowering and once for
first fruiting. The five animal phenophases include three
insect and two amphibian species. All but spotted salamander indicate the first sighting of that particular animal
species. The salamander phenophase is actually a record
of first observed egg mass deposition.
All records listed in Table I were digitized from notecard observations taken by observers at Mohonk Lake
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
Table I. The list of Mohonk Lake phenophases used in this study. There are 24 in total ordered from the earliest to the latest by
median day of observation. All plant species (except red-berried elder fruiting) represent date of first flowering. Spring peeper,
tent caterpillar, mourning cloak butterfly, and katydid dates represent first sighting. Spotted salamander dates are dates of first
sighting of the egg masses. The first year of each phenophase record is provided along with the total number of observations and
the minimum and maximum days of observation. Equivalent information for the observations made only since 1970, when the
number of observations (N) per phenophase are more consistent, are also given for comparison.
Plant phenophases: Day of first flowering (except for red-berried elder fruit)
Scientific name
Common name
First
year
Total
N
Min
day
Median
day
Max
day
N
(1970)
Min
day
Median
day
Max
day
Hepatica acutiloba
Sanguinaria canadensis
Eruthronium americanum
Thalictrum thalictroides
Caltha palustris
Amelanchier arborea
Trillium erectum
Hedyotis caerulea
Viburnum latanoides
Aquilegia canadensis
Trillium undulatum
Sambucus racemosa
Cornus florida
Polygala paucifolia
Rhododendron nudiflorum
Kalmia latifolia
Viburnum acerifolia
Sambucus racemosa
Rhododendrum indicum
Hepatica
Bloodroot
Trout lily
Rue anemone
Marsh marigold
Shadbush
Red trillium
Quaker ladies
Hobblebush
Wild columbine
Painted trillium
Red-berried Elder
Flowering dogwood
Fringed polygala
Pinxter
Mountain laurel
Maple leaf Viburnum
Red-berried Elder Fruit
Rhododendron
1928
1931
1932
1956
1953
1930
1931
1933
1931
1938
1952
1938
1931
1931
1931
1931
1939
1953
1931
34
32
39
30
35
59
37
40
33
30
29
48
24
42
40
59
27
28
35
86
92
96
102
101
93
108
97
108
111
115
109
114
114
118
141
140
136
170
105
108
111
115
115
116
119
119
122
126
126
126
126
130
132
155
155
170
187
126
124
126
131
129
129
133
135
146
143
139
141
139
144
151
167
172
192
197
21
23
22
21
26
31
23
25
20
22
20
27
12
29
14
31
18
17
23
86
92
96
102
101
100
108
107
108
111
115
109
114
114
126
141
140
157
174
105
107
110
115
115
113
119
120
123
127
127
122
126
128
130
154
155
169
187
124
118
125
127
129
128
133
135
146
136
139
136
135
139
144
163
172
192
196
112
106
159
126
288
29
16
26
26
31
48
76
66
98
198
88
93
94
113
221
108
103
147
126
288
Animal phenophases: Day of first sighting
Nymphalis antiopa
Ambystoma maculatum
Pseudacris crucifer
Malacosoma americanum
Family Tettigoniidae
Mourning cloak
Spotted salamander
Spring peeper
Eastern tent caterpillar
Katydid sp.
1934
1950
1930
1935
1938
over the years. Observations were made along predetermined routes and at specific locations. This methodology helped reduce observer bias or error. Following
digitizing, the original notecard records were rechecked
and problematic observations were dropped if they clearly
exceeded the range of phenological variability of the
species in question (the 3-sigma rule). Finally, for this
analysis, we also only retained records for which at least
24 observations (years) were available to ensure a reasonable level of significance in our statistical analysis.
Following the quality control checks, we analysed the
statistical properties of each phenology series for its
median Julian date of observation along with its range
(minimum and maximum dates) (Table I). This was done
for the full period of record and over the more serially
complete period across all phenophases since 1970. The
first year of observation for the plant phenophases ranges
from 1928 for Hepatica to 1956 for Rue Anenome, with
a median starting year of 1931 and a range of median
days of first flowering from 105 days for hepatica to
187 days for rhododendron. For the animal phenophases,
the first year of observation ranges from 1930 for spring
Copyright  2007 Royal Meteorological Society
54
31
52
44
61
48
76
66
98
188
89
94
96
113
219
peeper to 1950 for spotted salamander, with a median
starting year of 1935 and a range of median dates of
first sighting from 89 for Mourning Cloak to 219 for
katydid. From the varying starting years, the number of
potential observations available clearly differs. However,
from the list of total observations available (Table I),
it is also clear that none of the phenophase records
is serially complete either. The observations per plant
phenophase range from 24 for flowering dogwood to 59
for shadbush and mountain laurel, with a median of 35
observations available for analysis. While the missing
phenophase data complicate the analyses of these records
for trend and climate response, no missing data gap
exceeds 15 years and the median gap size for all plant
phenophases is only one year with an inter-quartile range
of 4 years.
Compared to the 29-year gap for all 74 phenophases
used by Bradley et al. (1999) to estimate phenology
trends in Wisconsin, our maximum gap size is relatively small. In addition, the observations of the 36
plant phenophases used in Bradley et al. (1999) from
their Table I range from 10 to 31 with a median of
Int. J. Climatol. (2007)
DOI: 10.1002/joc
B. I. COOK ET AL.
22 observations. With a median of 35 observations, the
Mohonk Lake phenological records compare quite favorably to those used for trend estimation by Bradley et al.
(1999) and are arguably better sampled overall. Consequently, we do not regard the missing data gaps in our
data as a fatal flaw in our estimation of phenology trends
at Mohonk Lake.
We tested the 24 phenophases for statistically significant trends over the full set of observations available
to ensure that the longest time periods possible were
available for trend estimation (cf. Bradley et al., 1999).
We also used three different methods of trend estimation
with various levels of robustness: standard linear regression by ordinary least squares (Draper and Smith, 1981),
a version of linear regression based on highly robust
estimates of location and scale (Gnanadesikan, 1977,
p. 131), and monotonic trend detection using the rankbased non-parametric Mann–Kendall test (Mann, 1945;
Kendall, 1975). The two regression-based methods provide slope estimates that explicitly measure the linear rate
of change in each phenophase series. The robust linear
regression method is mainly used here to check the stability of the standard least square slope estimates to the
possible presence of phenophase outliers not caught by
the previous quality control checks. The non-parametric
Mann–Kendall test is also suitable in the presence of
outliers, does not assume that the data are normally distributed, and is more tolerant to missing data (Yue and
Pilon, 2004). In addition, it is more sensitive to monotonic trends that may be linear or non-linear in form
and has more statistical power for detecting non-linear
trends than linear regression (Yue and Pilon, 2004). However, the Mann–Kendall test does not provide an explicit
estimate of slope.
In terms of statistical significance, for our study, a trend
will be considered detected in a given phenophase if all
three tests are significant at the two-tailed 90% level (the
‘3-test rule’). Following the trend tests, each series was
compared against likely climate predictors derived from
the local weather data using Spearman rank correlations.
This was done after the Mohonk Lake daily meteorological records were digitized and carefully quality controlled. Figure 1 shows a summary of some annual time
series pulled from the meteorological data. Perhaps fortuitously, mean annual temperature (Figure 1(a)) tracks
Northern Hemisphere temperature changes quite closely,
with a warming trend up until about 1950, cooling into
the 1960s and early 1970s, and then another warming
trend from the early 1970s to the present. Annual precipitation (Figure 1(b)) shows no overall trend, but largescale features, such as the eastern United States’ drought
in the 1960s, are well resolved. There is also no trend
in the actual length of the frost-free season (Figure 1(c)),
although there is a significant trend towards a greater total
annual number of frost-free days (not shown). Finally,
total annual GDDs (Figure 1(d)), using a base of 0 ° C,
closely follow the mean annual temperature curve.
We tried a variety of variables (e.g. mean temperatures,
precipitation totals, frost dates) over a range of temporal
compositing periods (e.g. weekly, monthly, seasonally).
We also compared the phenology series against largescale climate indices, notably the NINO3 index and the
NAO index, indicators of the strength and polarity of the
El Nino Southern Oscillation and North Atlantic Oscillation, respectively (Hurrell, 1996; Diaz et al., 2001). All
the phenology series showed some sensitivity to the temperature metrics, but no significant correlation with the
large-scale climate indices. The best results (presented
Figure 1. Overview of several annual time series from the Mohonk meteorological dataset, 1896–2002; mean annual temperature (° C),
precipitation (mm), length of frost-free season (days), and total annual growing degree-days (° C).
Copyright  2007 Royal Meteorological Society
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
here) were obtained with GDDs, a common predictor
used in phenological research and modelling (Hunter and
Lechowicz, 1992; White et al., 1997; White et al., 1999;
Kramer et al., 2000). A day qualifies as a GDD if the
mean temperature rises above some threshold value. In
modelling applications, GDDs are summed (‘GDD summations’) until a critical threshold is reached, at which
point the phenological event of interest occurs. GDD
summations often provide good predictive power in phenology because plants, in general, respond to accumulated
forcing (e.g. prolonged warm or wet periods) rather than
specific events (e.g. mean April temperature, mean winter
temperature).
Our approach differs somewhat from other analyses
using GDD summations. To compare the phenology
series versus GDD, we calculated GDD summations for
every day for each year. This gave us a GDD summation
time series for each day of the year, calculated from the
beginning of the year, day of year (DOY) 1 through
that day. We then correlated the time series of GDD
summations for each day against each phenophase and
retained the maximum negative or positive correlation as
indicative of the strength of relationship between GDD
summations and the phenology series. This was done
using all observations available for each phenophase and
only those observations available for the more serially
complete period since 1970. As a sensitivity test for
this method, we ran also these GDD correlations using
summations beginning on DOY 1, DOY 32, and DOY 50
using a GDD threshold value of 0 ° C. Finally, we repeated
the DOY 1 correlations but replaced the 0 ° C threshold
with a value of 5 ° C to see if the choice of threshold was
important to the outcome.
3.
Results
3.1. Trend test results
The plant trends are organized into four classes: herbaceous perennial – negative slope, herbaceous perennial – positive slope, woody perennial – negative slope,
and woody perennial – no slope (Table II). The slope
classes were determined a posteriori based on an examination of the results within each a priori functional plant
type (herbaceous and woody perennial). Scatter plots
of these four plant classes with fitted linear regression
curves are shown in Figure 2 to illustrate the rationale of
this classification scheme in more detail. For the animal
phenophases, the obvious functional separation is insect
and amphibian. But with only five animal phenophases
and only one species showing a significant trend (katydid
has a significant trend; Table II), there is little reason for
detailed comparison of these functional types. The rest
do not have significant trends. Therefore, the remainder of the discussion on trend will concern the plant
phenophases.
In terms of the overall presence of trend in the plant
phenological data, only a few species exhibit significant
trends. Only 5 of the 19 plant phenophases (26%) have
Copyright  2007 Royal Meteorological Society
trends that pass all three trend tests (p < 0.10): hepatica,
bloodroot, trout lily, shadbush, and red-berried elder. If
the stringent 3-test rule is relaxed to count those based
on the same least squares test as Bradley et al. (1999),
the number increases to 8 or 42% of the total with the
addition of quaker ladies, painted trillium, and mountain
laurel. For comparison, 14 of the 36 plant phenophases
(36%) evaluated for trend by Bradley et al. (1999) had
statistically significant trends (p < 0.10). Thus, in terms
of the proportion of species displaying significant trends,
the Mohonk Lake results are comparable to the cross-taxa
plant phenology results in Wisconsin.
The trend results in Table II are also ordered from
the earliest to the latest median Julian date within each
plant class to highlight another interesting feature in the
data. For the herbaceous perennial plants with negative
slopes, those with the most significant trends are the three
with the earliest median date of flowering. The reverse
is weakly the case for the three herbaceous perennial
plants with positive slopes, i.e. those slopes increase
with the median Julian date. No such ordering either
way is suggested in the slopes of the woody perennials
with negative slopes. Given the small number of cases,
it is difficult to conclude too much here. However, the
increase in slope towards the earlier negative slope in
herbaceous species is likely if springtime comes earlier
now due to warming temperatures because they are most
sensitive to that change in climate. This relationship
appears to carry over even when the non-significant
members in this class are included. The correlation of
the slopes versus. median Julian dates of flowering for
all seven species is r = 0.90 when based on the nonrobust slopes and r = 0.82 when based on robust slopes
(Table II). Both correlations are statistically significant
(p < 0.05) with five degrees of freedom. Thus, the
rate of change towards earlier flowering in that class
of herbaceous perennials appears to depend somewhat
sensitively on each species’ characteristic timing of
flowering.
We further explored the trends in the plant phenology
records by pooling the within-class time series to get
a more serially complete indication of the composite
response. The pooling was done by converting each
time series to anomalies relative to the 1970–2002 mean
prior to pooling the data. In so doing, we were able to
achieve 67–89% coverage over the nominal 1928–2002
period for the four phenology classes. Figure 3 shows the
composite anomaly times series. The results confirm what
is indicated in Figure 2 for the individual phenophases,
and both negative trend plant classes pass the 3-test
rule after pooling (Table II). Overall, the herbaceous
species show a greater tendency both in the occurrence of
negative trend and the strength of the trend after pooling,
−0.12 to −0.15 days/year for seven herbaceous species
versus about −0.08 days/year for three woody species
with negative trends (Table II). The herbaceous species
with positive trends remain somewhat equivocal after
Int. J. Climatol. (2007)
DOI: 10.1002/joc
Copyright  2007 Royal Meteorological Society
∗
Number of observations.
Mourning cloak
Spotted salamander
Spring peeper
Eastern tent caterpillar
Katydid sp.
Hepatica
Bloodroot
Trout lily
Rue anemone
Red trillium
Wild columbine
Fringed polygala
Pooled data
Marsh marigold
Quaker ladies
Painted trillium
Pooled data
Shadbush
Red-berried elder
Mountain laurel
Pooled data
Hobblebush
Flowering dogwood
Pinxter
Maple leaf viburnum
Red-berried elder fruit
Rhododendron
Pooled data
Common name
Insect
Amphibian
Amphibian
Insect
Insect
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Hebaceous perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Woody perennial
Phenophase type
None
None
None
None
Positive
Negative
Negative
Negative
Negative
Negative
Negative
Negative
Negative
Positive
Positive
Positive
Positive
Negative
Negative
Negative
Negative
None
None
None
None
None
None
None
Trend class
34
32
39
30
37
30
42
64
35
40
29
50
59
48
59
67
33
24
40
27
28
35
61
Total Na
105
108
111
115
119
126
130
115
115
119
126
119
116
126
155
126
122
126
132
155
170
187
144
Median day
1934
1950
1930
1935
1938
54
31
52
44
61
89
94
96
113
219
Animal phenophases: Day of first sighting
1928
1931
1932
1956
1931
1938
1931
1928
1953
1933
1952
1933
1930
1938
1931
1930
1931
1931
1931
1939
1953
1931
1931
First year
Phenophases organized by type and trend class
0.655
0.632
0.600
0.569
0.000∗
−0.027
−0.068
−0.006
−0.027
0.140
−0.193
−0.165
−0.100
−0.027
−0.031
0.025
−0.047
−0.152
0.072
0.072
0.131
0.084
−0.101
−0.159
−0.027
−0.081
−0.014
0.091
−0.029
−0.043
0.000
0.000
−0.010
0.045∗
0.038∗
0.064∗
0.512
0.289
0.723
0.565
0.002∗
0.198
0.078∗
0.058∗
0.022∗
0.065∗
0.017∗
0.068∗
0.021∗
0.523
0.882
0.686
0.844
0.115
0.937
0.973
−0.142
−0.154
−0.105
−0.067
−0.051
−0.028
−0.046
−0.118
0.087
0.120
0.160
0.128
−0.092
−0.133
−0.061
−0.085
−0.055
0.008
−0.028
−0.015
0.173
−0.005
−0.001
−0.082
−0.084
−0.059
−0.041
0.429
Slope
p
0.743
0.599
0.949
0.574
0.100∗
0.094∗
0.035∗
0.060∗
0.803
0.634
0.756
0.627
0.000∗
0.665
0.265
0.104
0.107
0.009∗
0.005∗
0.317
0.015∗
0.841
0.079∗
0.665
0.670
0.996
0.996
0.805
p
Least squares
(robust)
Slope
Least squares
(non-robust)
Plant phenophases: Day of first flowering (except for red-berried elder fruit)
−0.029
−0.094
0.014
−0.080
0.262
−0.284
−0.236
−0.236
−0.030
−0.107
−0.005
−0.030
−0.265
0.155
0.138
0.249
0.201
−0.190
−0.211
−0.132
−0.191
−0.085
0.079
−0.043
−0.032
0.097
−0.022
−0.003
Tau
0.757
0.456
0.886
0.443
0.003∗
0.018∗
0.058∗
0.058∗
0.814
0.350
0.971
0.782
0.002∗
0.191
0.210
0.058∗
0.039∗
0.033∗
0.034∗
0.139
0.022∗
0.489
0.589
0.696
0.815
0.470
0.851
0.970
p
Mann–Kendall
non-parametric
Table II. Phenophase trend test results. The phenophases are organized by general type (herbaceous perennial, woody perennial, and animal) and general trend class (negative, positive,
and none) based on the trend test results. Each trend class is also ordered from the earliest to the latest median day of observation. The results for the pooled data in each class are
also provided to provide more serially complete tests of overall trend in each class. Three methods of trend estimation were used to test for consistency of results for each phenophase:
non-robust ordinary least square regression, robust least square regression, and the Mann–Kendall non-parametric test for monotonic trend. Only the regression tests provide estimates of
slope. The Mann–Kendall tau is not a slope statistic. Significant trends (p < 0.10; two-tailed) are indicated with asterisks by the probabilities.
B. I. COOK ET AL.
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
(a)
(b)
(c)
(d)
Figure 2. Trends in plant phenological time series, grouped by functional type and response: (a) woody species, non-responsive; (b) woody
species, negative trend; (c) herbaceous species, negative trend; (d) herbaceous species, positive trend. This figure is available in colour online at
www.interscience.wiley.com/ijoc
pooling (only two of three tests pass) and the six woody
plants with no trend remain the same after pooling.
3.2. Growing degree-day results
Figure 4(a)–(x) shows the running correlation plots for
the GDD summations based on DOY 1 and DOY 50
starting days versus each phenology series. Spearman
rank correlations based on GDD summations are plotted
for each day of the year of the summations. A black
dashed line indicates the a priori 95% confidence level for
significance. Overlain onto each correlation plot is a box
plot of the observed phenological dates located over the
median Julian date of occurrence. The box plots provide
information on the median, inter-quartile range, and total
range of the observations for comparison to the timing
of strongest correlation with GDD summation. Results
from these graphs are summarized in Table III, displaying
Copyright  2007 Royal Meteorological Society
the correlation on the median flowering or first sighting
date for each series as well as the strongest correlation.
We conducted the Spearman correlations for the entire
period of record (1928–2002), as well as the latter period
(1970–2002) when the phenophases were more serially
complete as a sensitivity test.
All species show significant correlations with the GDD
summations, although the strength of the correlations
varies, consistent with the idea of cross-taxa differences
in temperature sensitivity. Correlations remain high, and
in some cases improve, when the analysis is confined to
the 1970–2002 period, but none change enough to question the results based on the full periods of record. All
maximum correlations and correlations on median date
of flowering or first sighting are significant (p < 0.05).
All the phenophases investigated here are clearly responsive to the same climate variable, GDD. However, their
Int. J. Climatol. (2007)
DOI: 10.1002/joc
B. I. COOK ET AL.
(a)
(b)
(c)
(d)
Figure 3. Composite anomaly series from plant phenological time series, grouped by functional type and response. All anomalies are taken
relative to the 1970–2002 mean: (a) woody species, non-responsive; (b) woody species, negative trend; (c) herbaceous species, negative trend;
and (d) herbaceous species, positive trend.
responses in terms of flowering and first sighting differ
in a complex mixture of ways. Overall, GDD correlations are higher in the case of plants, although all animal
species are significantly correlated with GDD summations as well. In most cases, the correlation peak falls
within the inter-quartile range of observations (mountain laurel, shadbush, fringed polygala, hepatica, rue
anemone, wild columbine, marsh marigold, painted trillium, quaker ladies, red-berried elder, red-berried elder
fruit, trout lily, flowering dogwood, and bloodroot). However, the correlation peaks for several species fall outside
the inter-quartile range, either before (rhododendron) or
after (red trillium, maple leaf viburnum, pinxter, katydid).
This could be a statistical artifact, a result of too few
or biased phenology observations. Indeed, for the two
most complete species (mountain laurel and shadbush)
Copyright  2007 Royal Meteorological Society
the peak correlation falls within a day or two of the
median onset date. Also, in many cases it is doubtful
that the maximum negative correlation, which may occur
outside the range of onset dates, is significantly different from the correlation on the median dates (shown in
Table III).
Across species, there are several common features to
the correlation plots. First, for GDD summations beginning on DOY 1, there is a period during January when
the running correlations evolve somewhat stochastically
up to about DOY 50. This is generally a period of low,
or nil GDD summations and also represents a time when
the species are mostly dormant and relatively insensitive
to environmental influences. Following this, for many of
the species there is a ‘ramping’ period in the correlations, defined loosely as the time period during which the
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
Figure 4. Times series and growing degree-day plots for each species from the phenology dataset. The plots are ordered by the plant and
animal classes indicated in Table II. Each figure shows Spearman correlations between running GDD summations and the phenology time series,
including 95% significance levels. Correlations are shown for GDD starting on DOY 1 and DOY 50. The inset figures show the box plot of the
relevant data centred over the median day of flowering or first sighting and the relevant phenology time series with a linear regression curve
fitted to it.
correlations more or less fit to a straight line with a negative slope. The magnitudes of the correlations increase
with various degrees of rapidity until they hit an inflection
point, either a broad ‘floor’ (after which the correlations approximate a flat line and slowly get weaker) or
a sharp ‘elbow’ (after which the correlations rapidly get
Copyright  2007 Royal Meteorological Society
weaker), usually around the median date of blooming or
first sighting.
To test the sensitivity of our methodology, we repeated
the GDD correlations, but instead began counting GDD
on DOY 32 and DOY 50 (Table III). In most cases,
the correlations improved, likely because we removed
Int. J. Climatol. (2007)
DOI: 10.1002/joc
B. I. COOK ET AL.
Figure 4. (Continued).
the earlier, ‘insensitive’ period from the correlations.
Additionally, we ran the correlations with a 5 ° C cutoff
for GDD (as opposed to 0 ° C), but found no meaningful
difference in the correlations (not shown).
The inflection points around DOY 50 in the DOY 1
running correlations are also noteworthy because this
is the first day of the year in the entire daily minimum temperature record since 1896 when sub-freezing
daily temperatures cease to occur at Mohonk Lake until
Copyright  2007 Royal Meteorological Society
the first frost the following autumn. Therefore, DOY
50 represents the beginning day when it is guaranteed to be safe for our tested species to break dormancy and become physiologically active. This result
implies that the running correlations based on DOY
50 GDD summations are the most accurate representations of species sensitivity to GDD at Mohonk
Lake. It also illustrates why having local meteorological observations is important for determining the
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
Figure 4. (Continued).
relationship between the phenology records and climate.
4.
Discussion
In their study of phenology in Wisconsin, Bradley
et al. (1999) determined ‘responders’ based on linear
trends detected in the time series on the presumption
that series showing significant trends were sensitive to
Copyright  2007 Royal Meteorological Society
climate. Here we use a more direct definition, basing
our definition of responder on correlations directly with
local meteorological data (i.e. GDD summations). In
this case, we found all our species fit the definition
of responder, as all were significantly correlated with
GDD summations. The trends in our series, however,
were much more mixed and showed a marked variation
in trend strength and significance. Of the five animal
phenophases, only Katydid showed a significant trend
Int. J. Climatol. (2007)
DOI: 10.1002/joc
B. I. COOK ET AL.
Figure 4. (Continued).
towards later first sighting, but the trend estimates were
unstable (0.14–0.43 days/year depending on the trend
estimator) for reasons that are unclear at this time. The
remaining four animal species failed every test of trend
and would be regarded as non-responders, based solely
on the lack of significant trends in the time series. With
respect to the plants, only 5 of 19 (26%) have statistically
significant (p < 0.10) phenology trends based on the
stringent 3-test rule. When only standard linear regression
Copyright  2007 Royal Meteorological Society
is used, the number of plant responders increase from
5 to 8 (42% of the total). Our level of trend response
is consistent with the proportion of plant responders
identified in a large cross-taxa phenology dataset from
Wisconsin (Bradley et al., 1999). But the analysis here
suggests that classifying plants as responders based only
on time series trends may be inappropriate, as lack of
trend does not necessarily indicate lack of responsiveness
to climate.
Int. J. Climatol. (2007)
DOI: 10.1002/joc
LONG-TERM PHENOLOGY AT MOHONK
Table III. Correlations between phenology series and growing degree-day (GDD) summations ordered by median day of event.
Shown in this table are Spearman rank correlation coefficients for each series on the median date of occurrence of the phenology
events and the minimum Spearman correlation overall GDD summations beginning at day of year 1 (DOY 1), day of year 32
(DOY 32), and day of year 50 (DOY 50). For comparison, see Figure 4(a)–(x). DOY 50 was also tested for the more serially
complete 1970–2002. No meaningful differences were found between the full and 1970–2002 correlations for DOY 50.
Plant phenophases: Day of first flowering (except for red-berried elder fruit)
Common name
Hepatica
Bloodroot
Trout lily
Rue anemone
Marsh marigold
Shadbush
Red trillium
Quaker ladies
Hobblebush
Wild columbine
Painted trillium
Red Berried elder
Flowering dogwood
Fringed polygala
Pinxter
Mountain laurel
Maple leaf viburnum
Red-berried elder fruit
Rhododendron
Complete record of each phenophase used
1970–2002
data only
Median
day
DOY 1
median
DOY 1
minimum
DOY 32
median
DOY 32
minimum
DOY 50
median
DOY 50
minimum
DOY 50
median
DOY 50
minimum
105
108
111
115
115
116
119
119
122
126
126
126
126
130
132
155
155
170
187
−0.733
−0.739
−0.567
−0.477
−0.550
−0.738
−0.469
−0.553
−0.597
−0.536
−0.458
−0.829
−0.470
−0.537
−0.578
−0.785
−0.644
−0.455
−0.498
−0.801
−0.812
−0.630
−0.516
−0.596
−0.746
−0.538
−0.560
−0.635
−0.536
−0.474
−0.851
−0.496
−0.602
−0.653
−0.785
−0.689
−0.551
−0.558
−0.753
−0.768
−0.638
−0.545
−0.583
−0.767
−0.509
−0.545
−0.603
−0.564
−0.507
−0.855
−0.552
−0.588
−0.605
−0.795
−0.680
−0.474
−0.503
−0.793
−0.826
−0.654
−0.568
−0.609
−0.767
−0.545
−0.558
−0.663
−0.583
−0.525
−0.879
−0.578
−0.651
−0.689
−0.797
−0.725
−0.600
−0.557
−0.791
−0.806
−0.621
−0.506
−0.575
−0.733
−0.531
−0.527
−0.606
−0.574
−0.494
−0.876
−0.452
−0.645
−0.619
−0.821
−0.711
−0.461
−0.520
−0.817
−0.845
−0.651
−0.553
−0.595
−0.736
−0.590
−0.563
−0.654
−0.602
−0.537
−0.890
−0.527
−0.671
−0.682
−0.821
−0.722
−0.622
−0.574
−0.814
−0.835
−0.622
−0.438
−0.541
−0.723
−0.507
−0.556
−0.771
−0.661
−0.582
−0.871
−0.541
−0.618
−0.816
−0.868
−0.627
−0.512
−0.528
−0.833
−0.847
−0.662
−0.558
−0.632
−0.757
−0.645
−0.663
−0.809
−0.701
−0.605
−0.908
−0.657
−0.655
−0.892
−0.871
−0.680
−0.637
−0.593
−0.481
−0.418
−0.558
−0.692
−0.374
−0.407
−0.487
−0.481
−0.617
−0.281
−0.477
−0.647
−0.625
−0.717
−0.291
Animal phenophases: Day of first sighting
Mourning Cloak
Spotted Salamander
Spring Peeper
Eastern Tent Caterpillar
Katydid spp.
89
94
96
113
219
−0.430
−0.411
−0.492
−0.600
−0.362
−0.461
−0.401
−0.561
−0.688
−0.374
−0.429
−0.381
−0.501
−0.674
−0.358
The herbaceous perennial plants with negative phenology trends show the strongest evidence for change
in the date of flowering. Three of the seven species in
this class have statistically significant slopes that range
from −0.10 to −0.19 days/year, depending on how the
slope is estimated. The remaining four species in this
class have weaker negative trends that do not pass the
3-test rule, but the magnitudes of the slopes of all the
seven species correlate highly with their median dates
of flowering. This result suggests that the most sensitive
herbaceous perennial plants are those that would benefit
most from earlier springtime warming that would clear
the ground of snow cover soon and warm the local environment around the plants quickly prior to the initiation
of new growth. Pooling the herbaceous data with negative
trends to provide a more serially complete record for
trend detection did not materially change the overall trend
assessment. The pooled rate of change over all seven
phenophases was −0.12 to −0.15 days/year, a highly
significant (p < 0.01) result.
Copyright  2007 Royal Meteorological Society
−0.461
−0.401
−0.561
−0.688
−0.374
−0.455
−0.352
−0.507
−0.680
−0.367
All phenophases correlate significantly (p < 0.05)
with GDD summations, although some species appear
to be more sensitive than others. This is not surprising,
given the complexity of cross-taxa specification and evolution and how each species is thus able to respond to its
local growth environment. The strength of each response
differs over a relatively wide range of correlation (e.g.
the Spearman rank correlation for the DOY 50 minimum
in Table III ranges from −0.53 to −0.89). Even so, all
of the correlations are negative, which clearly shows that
all of the phenophases flower or emerge from the same
basic climate forcing. It would be surprising if this result
did not generalize to the broad range of native species
living around Mohonk Lake.
Bradley et al. (1999) discuss the way in which some
phenophases may be more sensitive to environmental
influences and others much less sensitive so, and they
refer to them as ‘responders’ and ‘non-responders’.
They also suggest that the phenology of some of the
non-responders may be determined more by seasonal
Int. J. Climatol. (2007)
DOI: 10.1002/joc
B. I. COOK ET AL.
Table IV. Monthly temperature trends (° C/year) over three
intervals from the Mohonk temperature record. Significant
trends (p <= 0.10), as determined using a linear least squares
regression, are highlight in grey and bold text.
Trend in mean monthly temp (° C/year)
1896–2002
1928–2002
1970–2002
Month
Slope
p-value
Slope
p-value
Slope
p-value
1
2
3
4
5
6
7
8
9
10
11
12
0.003
0.025
0.013
0.016
0.010
0.016
0.013
0.020
0.007
0.006
0.012
0.015
0.688
0.001
0.074
0.002
0.061
0.000
0.000
0.000
0.089
0.256
0.035
0.039
−0.010
0.016
0.013
0.022
0.007
0.014
0.013
0.020
0.009
0.005
0.004
0.010
0.446
0.203
0.252
0.010
0.385
0.023
0.028
0.001
0.191
0.593
0.642
0.394
0.110
0.096
0.039
0.053
0.024
0.073
0.040
0.042
0.037
0.027
0.024
0.055
0.022
0.034
0.285
0.037
0.374
0.001
0.053
0.047
0.081
0.363
0.479
0.233
changes in photoperiod than by climate. This may be
the case for some of the plant and bird non-responders
identified by Bradley et al. (1999). We cannot test for
photoperiodic effects in our plant analyses here, but
our woody perennial phenophases with near-zero trends
(hobblebush, flowering dogwood, pinxter, maple leaf
viburnum, and rhododendron), all have highly significant
correlations with DOY 50 GDD (−0.61, −0.45, −0.62,
−0.71, −0.52) for each species’ median flowering date
(cf Tables II and III). So while photoperiod may be an
influence of largely constant proportion, it is unlikely to
be the sole explanation for the lack of trend in any of our
putative ‘non-responder’ phenophases.
Overall, the phenological records at Mohonk show
only a restricted trend towards earlier occurrence of
the start of the growing season compared to the many
species records studied in Europe (e.g. Menzel, 2000).
This occurs despite the strong relationship to climate
exhibited by all the phenophases. The most likely reason is the seasonality in temperature trends at our site
(Table IV): the strongest trends are during the winter (December/January/February) and summer seasons
(June/July/August), when the species are either insensitive to temperature forcing (winter) or largely after the
occurrence of the phenological events (summer). There
is some trend in spring (March/April/May) temperatures,
when phenology is most sensitive to climate, but the trend
is weak and anchored by several warm years occurring
towards the end of the record. This, coupled with the
high inter-annual variability in temperatures during all
seasons, likely explains the lack of many strong trends in
our phenological observations.
Still, the combined meteorological and phenological
datasets examined here provide a rare opportunity to
examine how a large number of native plant and animal
species are responding to changing climate over much
Copyright  2007 Royal Meteorological Society
of the 20th century. Few other phenological datasets in
North America have the length, breadth (in terms of
species), and consistency of observation as the Mohonk
dataset. Even fewer are accompanied by long-term, high
quality meteorological records that closely reflect the
micro-meteorological conditions of the species being
observed. Future work with this dataset will include a
more detailed analysis of the plant and animal phenological records described herein, an investigation of the
Mohonk bird arrival records, and a consideration of how
these species compare with others within a larger scale
context of global change and its impact on phenology.
Acknowledgements
This research has been supported by the Comer Foundation. We also acknowledge the long-time efforts of the
Smiley family of Mohonk, especially Daniel and Keith
Smiley, and The Mohonk Preserve in maintaining and
updating the unique phenological and weather records
described here. Lamont-Doherty Earth Observatory Contribution No. 7082.
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