1
1
Supporting information 1
2
Description of phenology models
3
For each model, the state of chilling (Sc) or forcing (Sf) is the time integral from p0 or t0 of
4
the rate of chilling (Rc) or forcing (Rf), which are functions of daily mean temperature
5
x(t). Chilling and forcing accumulate relative to base temperatures (Tbase, Tchill, Tforce, Tmax,
6
or Tmin) until a critical threshold (C* or F*) is reached. The date that the threshold is
7
reached corresponds to the predicted timing of the phenological metric of interest.
8
Chilling and forcing parameters are either fixed across all sites, or are modeled as a linear
9
function of the long-term mean annual temperature ( Ti ) of each site, i.
10
11
Spring Warming 1
12

28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
13
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
14
15
16
Phenology metric is predicted to occur when Sf = F*
𝑡0
Spring Warming 2
17
28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
18
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
19
20
21
Phenology metric is predicted to occur when Sf = F*
𝑝0
Spring Warming 3
22
28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
23
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
24
25
26
𝑡0
Phenology metric is predicted to occur when Sf = a Ti +b
Spring Warming 4

2
27
28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
28
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
29
30
31
Phenology metric is predicted to occur when Sf = a Ti +b
𝑝0
Parallel 1
32
28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
33
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
𝑡0
34
0
𝑥(𝑡) ≥ 10.4⁡or 𝑥(𝑡) ≤ −3.4
𝑥(𝑡) + 3.4
−3.4⁡ < 𝑥(𝑡) ≤ 𝑇opt
𝑅𝒄 (𝑡) = 𝑇opt + 3.4
𝑥(𝑡) − 10.4
𝑇opt < 𝑥(𝑡) < 10.4
{ 𝑇opt − 10.4
35
𝑆𝑐 (𝑡) = ∑ 𝑆𝑐 (𝑥(𝑡))
36
37
38
𝑡0
Phenology metric is predicted to occur when 𝑆𝑓 (𝑡) ≥ 𝑎 ∗ exp⁡(𝑏 ∗ 𝑆𝑐 (𝑡)), where b < 0.
Parallel 2
39
28.4
𝑥(𝑡) > 0
𝑅𝑓 (𝑡) = {1 + exp⁡(3.4 − 0.185 ∗ 𝑥(𝑡))
0
𝑥(𝑡) ≤ 0
40
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
𝑝0
41
0
𝑥(𝑡) ≥ 10.4⁡or 𝑥(𝑡) ≤ −3.4
𝑥(𝑡) + 3.4
−3.4 < 𝑥(𝑡) ≤ 𝑇opt
𝑅𝒄 (𝑡) = 𝑇opt + 3.4
𝑥(𝑡) − 10.4
𝑇opt < 𝑥(𝑡) < 10.4
{ 𝑇opt − 10.4
42
𝑆𝑐 (𝑡) = ∑ 𝑆𝑐 (𝑥(𝑡))
43
44
45
Phenology metric is predicted to occur when 𝑆𝑓 (𝑡) ≥ 𝑎 ∗ exp⁡(𝑏 ∗ 𝑆𝑐 (𝑡)), where b < 0.
𝑝0
46
Alternating 1
𝑥(𝑡) − 𝑇base
𝑅𝑓 (𝑡) = {
0
47
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
𝑡0
𝑥(𝑡) > 𝑇base
𝑥(𝑡) ≤ 𝑇base
3
1 𝑥(𝑡) < 𝑇base
0 𝑥(𝑡) ≥ 𝑇base
48
𝑅𝑐 (𝑡) = ⁡ {
49
𝑆𝑐 (𝑡) = ∑ 𝑆𝑐 (𝑥(𝑡))
50
51
52
53
Phenology metric is predicted to occur when 𝑆𝑓 (𝑡) ≥ 𝑎 + 𝑏 ∗ exp⁡(𝑐 ∗ 𝑆𝑐 (𝑡)), where
c < 0.
𝑡0
54
Alternating 2
𝑥(𝑡) − 𝑇base
𝑅𝑓 (𝑡) = {
0
55
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
56
1 𝑥(𝑡) < 𝑇base
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇base
57
𝑆𝑐 (𝑡) = ∑ 𝑆𝑐 (𝑥(𝑡))
58
59
60
61
Phenology metric is predicted to occur when 𝑆𝑓 (𝑡) ≥ 𝑎 + 𝑏 ∗ exp⁡(𝑐 ∗ 𝑆𝑐 (𝑡)), where
c < 0.
𝑥(𝑡) > 𝑇base
𝑥(𝑡) ≤ 𝑇base
𝑝0
𝑝0
62
Sequential 1
𝑥(𝑡) − 𝑇force
𝑅𝑓 (𝑡) = {
0
63
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
64
1 𝑥(𝑡) < 𝑇chill
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇chill
65
𝑆𝑐 (𝑡) = ∑ 𝑅𝑐 (𝑥(𝑡))
66
67
68
69
Forcing summation (Sf ) begins at t1 when Sc = C*. Phenology metric is predicted to occur
when Sf = F*
𝑥(𝑡) > 𝑇force
𝑥(𝑡) ≤ 𝑇force
𝑡1
𝑡1
𝑡0
70
Sequential 2
𝑥(𝑡) − 𝑇force
𝑅𝑓 (𝑡) = {
0
71
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
72
1 𝑥(𝑡) < 𝑇chill
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇chill
73
𝑆𝑐 (𝑡) = ∑ 𝑅𝑐 (𝑥(𝑡))
𝑥(𝑡) > 𝑇force
𝑥(𝑡) ≤ 𝑇force
𝑡1
74
75
76
77
𝑡1
𝑝0
Forcing summation (Sf (t)) begins at t1 when Sc = C*. Phenology metric is predicted to
occur when Sf = F*
Sequential 3
4
𝑥(𝑡) − 𝑇force
0
𝑥(𝑡) > 𝑇force
𝑥(𝑡) ≤ 𝑇force
78
𝑅𝑓 (𝑡) = {
79
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
80
1 𝑥(𝑡) < 𝑇chill
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇chill
81
𝑆𝑐 (𝑡) = ∑ 𝑅𝑐 (𝑥(𝑡))
82
83
84
85
Forcing summation (Sf ) begins at t1 when Sc = C*. Phenology metric is predicted to occur
when Sf = a Ti +b
𝑡1
𝑡1
𝑝0
86
Sequential 4
𝑥(𝑡) − 𝑇force
𝑅𝑓(𝑡) = {
0
87
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
88
1 𝑥(𝑡) < 𝑇chill
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇chill
89
𝑆𝑐 (𝑡) = ∑ 𝑅𝑐 (𝑥(𝑡))
𝑥(𝑡) > 𝑇force
𝑥(𝑡) ≤ 𝑇force
𝑡1
90
91
92
93
𝑡1
𝑝0
Forcing summation (Sf ) begins at t1 when Sc = a Ti +b. Phenology metric is predicted to
occur when Sf = F*
94
Sequential 5
𝑥(𝑡) − 𝑇force
𝑅𝑓 (𝑡) = {
0
95
𝑆𝑓 (𝑡) = ∑ 𝑅𝑓 (𝑥(𝑡))
96
1 𝑥(𝑡) < 𝑇chill
𝑅𝑐 (𝑡) = ⁡ {
0 𝑥(𝑡) ≥ 𝑇chill
97
𝑆𝑐 (𝑡) = ∑ 𝑅𝑐 (𝑥(𝑡))
𝑥(𝑡) > 𝑇force 
𝑥(𝑡) ≤ 𝑇force
𝑡1
98
99
100
101
102
103
104
105
𝑡1
𝑝0
Forcing summation (Sf ) begins at t1 when Sc = a Ti +b. Phenology metric is predicted to
occur when Sf = c Ti +d.


5
106
Table S1: Summary of model parameters
Model
SW1
SW2
SW3
SW4
PAR1
PAR2
ALT1
ALT2
SEQ1
SEQ2
SEQ3
SEQ4
SEQ5
107
108
109
110
111
112
113
114
115
Parameters
t0, F*
p0, F*
t0, a, b [F*]
p0, a, b [F*]
t0, Topt, a, b
p0, Topt, a, b
t0, Tref, a, b, c
p0, Tref, a, b, c
t0, Tforce, Tchill, F*, C*
p0, Tforce, Tchill, F*, C*
p0, Tforce, Tchill, C*, a, b [F*]
p0, Tforce, Tchill, F*, a, b [C*]
p0, Tforce, Tchill, a, b [F*], c, d [C*]
6
116
Table S2: Optimal parameters for each species based on out-of-sample testing results. Species codes are given in Table 2. Parameters
117
are given with associated 95% confidence intervals in parentheses. If a single numerical value j is listed in the table under F*, then F*
118
= j in the corresponding model. If two values j and k are listed, then 𝐹 ∗ = 𝑇̅𝑗 + 𝑘, such that 𝑇̅ is mean annual temperature. In the first
119
parameter column (t0 / p0), values followed by a * indicate starting photoperiod (p0).
Species
Code
ACRU
ACSA
FAGR
PRSE
BEPA
POTR
JUNI
LITU
QUAL
QURU
PhCam
120
121
t0 / p0*
51
(50, 53)
11.9*
(11.8, 12.0)
12.5*
(12.4, 12.7)
9.5*
(9.3, 9.8)
12.7*
(12.4,12.9)
12.1*
(11.5, 13.2)
11.9*
(11.6, 12.0)
11.6*
(11.5, 11.6)
343
(339, 354)
77
(70, 87)
73
(70, 76)
F*
Tforce / Tref
Topt
-3.2
(-3.4, -2.3)
a
288
(274, 312)
b
-0.02
(-0.02, -0.02)
c
1.2
(1.0, 1.3)
168
(165, 172)
1155
(959, 1300)
(-0.03, -0.02)
6.9
(6.0, 7.2)
105
(101, 108)
1408
(1212, 1653)
(-0.04, -0.03)
-5.7
(-8.6, -3.5)
24.0
(20.6, 26.9)
532
(424, 669)
(-0.01, 0.02)
6.9
(4.5, 9.3)
112.6
(88.6, 130.1)
171.5
(153.6, 193.7)
-0.03
135.7
(126.3, 148.1)
2.1
125.5
(-1.3, 5.5)
(98.2, 146.1)
261.6
(237.2, 290.1)
150.1
(139.4, 158.1)
-0.03
165.6
(152.0, 185.1)
-0.006
7
122
Table S3: ΔAICc (difference between a particular model’s AICc and the lowest AICc across all candidate models for a species) values
123
for models fit to species-specific NPN or PhenoCam data. Species codes are given in Table 2. The best model, based on Akaike’s
124
Information Criterion corrected for small samples (AICc) has ΔAICc = 0 and is indicated by bold type.
125
SW1
SW2
SW3
SW4
PAR1
ALT1
PAR2
ALT2
SEQ1
SEQ2
SEQ3
SEQ4
SEQ5
ACRU POTR PRSE ACSA FAGR JUNI LITU BEPA QUAL QURU PhCam
51.2
30.6
4.8
24.6
1.7
2.3
6.1
2.3
4.9
8.6
0.0
47.2
26.4
5.3
24.4
4.8
4.4
4.4
0.0
0.0
0.0
0.0
51.4
22.9
6.2
2.4
22.0
8.8 15.8
40.6
8.4
59.9
50.4
70.5
7.9
23.9 10.9
3.5
44.8
7.5
58.9
49.4
0.0
0.0
40.2
9.3
61.2
11.2
14.0
1.2
17.1
15.1
15.4
42.9
0.0
16.7
82.8
7.4
26.7
0.8
9.9
18.4
47.4
0.0
0.0
0.0
14.9 113.2
18.6
41.5
0.8
6.1
0.8
63.1
26.3
31.6
8.8
10.1
72.1
10.8
10.8
29.9
6.0
9.7
26.5
0.9
11.7
13.9
53.3
42.9 237.4
79.7 241.1
9.6 10.3
56.1
37.4
27.7
339.9
24.7
34.5 182.5
76.3 180.2 12.4
5.8
13.1 144.3
22.5
2.9
41.6
52.9 215.5
56.3 181.4 34.3
0.7
26.9
45.6
55.5
59.0
68.0
67.4 240.3
73.5 229.8
9.4
7.9
23.9 149.2 180.2
119.0
26.0
69.4 180.1
79.8 213.0 12.7 11.8 210.5 259.5
55.5
115.7
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
0.0 0.2 0.4 0.6 0.8 1.0
Pearson Correlation
0.0 0.2 0.4 0.6 0.8 1.0
Oak−Gum
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
(d)
(e) Elm−Ash−Cottonwood
(f)
Aspen−Birch
Pearson Correlation
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
Pearson Correlation
0.0 0.2 0.4 0.6 0.8 1.0
(b)
0.0 0.2 0.4 0.6 0.8 1.0
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
Oak−Hickory
0.0 0.2 0.4 0.6 0.8 1.0
Pearson Correlation
0.0 0.2 0.4 0.6 0.8 1.0
Pearson Correlation
(a)
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
Ph U R
C U
am
Pearson Correlation
8
Figure S1: Boxplots of Pearson correlation coefficients between MODIS estimated spring
onset and model predicted spring onset across (a-e) pixels dominated by each forest type,
according to 25 km FIA hexagon maps (see Figure 2) or (f) the entire study region.
(c) Maple−Beech−Birch
Total
2.0
2.0
3.0
3.0
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
PhUR
C U
am
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
PhUR
C U
am
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
PhUR
C U
am
0.0
0.0
0.0
2.0
2.0
1.0
2.0
sobs spred
1.0
sobs spred
1.0
3.0
3.0
3.0
Oak−Gum
1.0
sobs spred
1.0
3.0
(d)
(e) Elm−Ash−Cottonwood
(f)
Aspen−Birch
0.0
2.0
sobs spred
1.0
(b)
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
P h UR
C U
am
0.0
0.0
sobs spred
(a)
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
P h UR
C U
am
AC
ACRU
FA SA
PRGR
BESE
PO PA
T
JU R
LI N I
Q TU
U
Q AL
P h UR
C U
am
sobs spred
9
Figure S2: Boxplots of model slope (calculated as quotient of standard deviation of
MODIS estimated spring onset and standard deviation of predicted spring onset) across
(a-e) pixels dominated by each forest type, according to 25 km FIA hexagon maps (see
Figure 2) or (f) the entire study region.
(c) Maple−Beech−Birch
Total