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RESEARCH ARTICLE
Impacts of Climate Change on the Timing of
the Production Season of Maple Syrup in
Eastern Canada
Daniel Houle
1,2☯
*, Alain Paquette
3☯
, Benoît Côté
4
, Travis Logan
2
, Hugues Power
1
,
Isabelle Charron
2
, Louis Duchesne
1
1Direction de la recherche forestière, Ministère des Forêts de la Faune et des Parcs du Québec, 2700
Einstein, Québec, QC G1P 3W8, Canada, 2Consortium sur la climatologie régionale et l’adaptation aux
changements climatiques (Ouranos), 550 Sherbrooke W, Montréal, QC H3A 1B9, Canada, 3Centre for
forest research, Université du Québec àMontréal, P.O. Box 8888, Centre-ville Station, Montréal, QC H3C
3P8 Canada, 4Dep. of Natural Resource Sciences, McGill University, 21,111 Lakeshore Rd., Ste. Anne de
Bellevue, QC H9X 3V9 Canada
☯These authors contributed equally to this work.
*daniel.houle@mffp.gouv.qc.ca
Abstract
Maple syrup production is an important economic activity in north-eastern North-America.
The beginning and length of the production season is linked to daily variation in tempera-
ture. There are increasing concerns about the potential impact of climatic change on this
industry. Here, we used weekly data of syrup yield for the 1999–2011 period from 121
maple stands in 11 regions of Québec (Canada) to predict how the period of production
may be impacted by climate warming. The date at which the production begins is highly var-
iable between years with an average range of 36 days among the regions. However, the
average start date for a given region, which ranged from Julian day 65 to 83, was highly pre-
dictable (r
2
= 0.88) using the average temperature from January to April (T
J-A
). A logistic
model predicting the weekly presence or absence of production was also developed. Using
the inputs of 77 future climate scenarios issued from global models, projections of future
production timing were made based on average T
J-A
and on the logistic model. The projec-
tions of both approaches were in very good agreement and suggest that the sap season will
be displaced to occur 15–19 days earlier on average in the 2080–2100 period. The data
also show that the displacement in time will not be accompanied by a greater between
years variability in the beginning of the season. However, in the southern part of Québec,
very short periods of syrup production due to unfavourable conditions in the spring will
occur more frequently in the future although their absolute frequencies will remain low.
Introduction
Maple syrup production from sap goes back to the first nation peoples of north-eastern North-
America [1]. With time, the methods were modernized and maple syrup production is now an
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 1/14
OPEN ACCESS
Citation: Houle D, Paquette A, Côté B, Logan T,
Power H, Charron I, et al. (2015) Impacts of Climate
Change on the Timing of the Production Season of
Maple Syrup in Eastern Canada. PLoS ONE 10(12):
e0144844. doi:10.1371/journal.pone.0144844
Editor: Gisèle LaPointe, University of Guelph,
Canada, CANADA
Received: July 7, 2015
Accepted: November 24, 2015
Published: December 18, 2015
Copyright: © 2015 Houle et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Third party data under
confidentiality restrictions due to their private nature.
Data are available from the "Fédération des
Producteurs Acéricoles du Québec" upon request.
Furthermore, the data was gathered from private
producers under conditions of confidentiality. The
Federation may be contacted at: http://www.
siropderable.ca/accueil.aspx.
Funding: The costs associated with this research
were covered by the “Ministère des Forêts, de la
Faune et des Parcs du Québec”(DH) and “Le Fond
Vert du Ministère du Développement Durable,
Environnement, et Parc du Québec”within the
important economic activity for the region. Sugar maple (Acer saccharum Marsh.), a dominant
species throughout north-eastern North-America, is the most-used species because of its high
sugar sap content (2–3%) [2]. In addition to sap sweetness, there is increasing interest in the
chemical composition of maple syrup, particularly the phenolic compounds, because of their
antioxidant, antiradical and antimutagenic properties [3].
The period of syrup production [4] as well as the annual yield [5,6] have been shown to
depend on climatic conditions, and therefore vary between years and regions. Recently, sugar
maple producers in the province of Québec (which is responsible for 75% of the world produc-
tion) have expressed concerns about the high year to year variability in the timing of the season
(Houle, unpublished data). Similarly, in a survey conducted among producers of Québec,
Ontario, Atlantic Canada and northeastern USA, 73% mentioned that start of the season was
showing increasing between years variability [7].
Sap volume and sugar content are associated to climatic conditions that prevail during the
production season, as well as in the months preceding it [8–11]. Several processes are involved
in initiating spring sap production, which is based on alternating positive and negative pressure
in the trunk and branches which is favoured by freezing temperatures at night followed by
above-zero temperatures during the day (freeze/thaw cycle) [8,12,13].
The physiological processes involved, as much for volume as for sugar content, can be
related to a few determinant conditions. Some authors noted that the ideal climatic conditions
for sap production are temperatures close to 0°C at night rising above 4°C during the day [11,
12,14]. Others identified the maximum daily temperature [8] or the difference between the
minima and maxima as factor determining production [13]. Climatic conditions in the months
preceding sap production can also be determinant [6]. Precipitation (snow or rain) between
October and April (inclusively) are positively correlated to syrup production in the following
spring [11]. Cold winters are also positively correlated to yield because they are associated with
increased sugar content in sap [6]. Warm temperatures in April may abruptly halt production
by favouring bud break and the proliferation of microorganisms in the tap [6].
Because of the close links between climate and maple syrup production, climatic change is
likely to have a strong impact on the industry [4,6]. Predicted climate change effects for north
eastern North-America include an increase in annual temperature and precipitation which will
be particularly strong during winter, as well as a reduction in the duration of the snow cover
[15]. Below freezing winter temperatures must last long enough for the tree to begin the dehar-
dening process necessary for the beginning of sap production [16]; should this period become
too short, tree physiology and vigor could be affected which would reduce sap production.
Although no significant trends are projected in the number of freeze/thaw cycles [15], the
period of the year where the frequency of those cycles is at its peak is expected to happen
sooner in the season. The possible impacts of these changes on maple syrup production are
many. Because of its dependence on freeze/thaw cycles, the production season should occur
earlier in time [4]. Duchesne et al. [6]suggested that the period of sugar maple production will
have to be shifted 12 and 19 days earlier in 2050 and 2100 respectively, in order to capture the
optimum climatic conditions necessary to maintain the syrup production at its current level.
This paper aims at investigating the impact of climate change on the timing and duration of the
sap season. To date, the temporal variability of syrup yield has been studied mainly on an annual
basis [1,6], or on finer temporal scales (days) but for only one specific site at a time for a one-year
period [11,13]. As a result, models capable of taking into account the variability in fine temporal
scales and regional differences in timing of the sap season are still lacking. Here, we use a weekly
data set of maple syrup production from 121 sugar maple producers located in 11 regions of Qué-
bec for a period of 13 years along with climate data to build models of maple syrup production
timing. These models are then used to make projections of the impact of climate change on the
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 2/14
framework of the Action Plan 2006-2012 on climate
change in association with Natural Resources
Canada (DH and BC) (http://www.mffp.gouv.qc.ca/
accueil.jsp), (http://www.mddelcc.gouv.qc.ca/
ministere/fonds-vert/). The funders had no role in
study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
timing (beginning and end) of maple syrup production based on 77 climate change scenarios
issued from global climate models. We hypothesize that the period of syrup production will occur
sooner in spring and that it will become more variable between years.
Methods
Maple syrup production and climatic data
Weekly maple syrup production data (kg per tap) for the 1999–2011 period were obtained
from surveys collected by the « Fédération des Producteurs Acéricoles du Québec » (FPAQ)
from 121 producers scattered over 11 regions in Québec (Fig 1 and Table 1). Weekly data from
all contributing producers from a given region were aggregated to obtain a total of 143 observa-
tions (13 years times 11 regions) for the beginning, end and length of the sap production
period. Daily climate data from several weather stations in each region (Fig 1) were used to
compute the weekly climate variables needed for the predictive model. The modeling was
Fig 1. Study area showing the 11 regions, the location of the producers (grey dot) participating in the survey (1999–2011) and the weather stations
(black triangle). BSL: Bas-Saint-Laurent; CTS: Côte-du-Sud; BEA: Beauce; QUE: Québec; MAU: Mauricie; LAN: Lanaudière; EST: Estrie; LAU:
Laurentides, Outaouais, Abitibi-Témiscamingue; CDQ: Centre-du-Québec; STY: Saint-Hyacinthe; VAL: Valleyfield.
doi:10.1371/journal.pone.0144844.g001
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 3/14
carried out with the objective of selecting a small number of variables known to be associated
with one or several mechanisms influencing sap flow. One of the most important process iden-
tified, freeze/thaw cycles, was assessed by testing several temperature variation thresholds
between -3 and +5°C as well as mean daily temperature intervals to obtain the best predictors
of the timing of sap flow (see S1 Table for the complete list of variables used and their descrip-
tion). For consistency, we maintain the term freeze/thaw cycle for climate variable names even
when the threshold temperatures was not 0°C. Similarly, for cumulated degrees days above
(thawing) and below (freezing), the threshold is always calculated relative to the threshold tem-
perature considered. Since the freeze/thaw cycle and cumulative degree day calculations are
inherently sensitive to any smoothing of the temperature signals that result from the averaging
of multiple locations, weekly climate variables were first calculated for individual weather sta-
tions and subsequently averaged for each of the 11 regions.
Timing and duration of production season model
We modeled the duration of the production season, which was defined as the interval between
the first (start) and the last (end) week of production for a given region and year. It is important
to note that this definition may not always match exactly with the physiological sap flow
period, but rather with the period during which maple sap is actively transformed into syrup.
In some circumstances, the actual sap flow period may extend over the production season
because the producers may cease to produce syrup because of low sap quality or volume, espe-
cially towards the end of the season. Our definition thus combines both the physiological and
operational aspects of maple syrup production. Logistic regression was used to predict the
duration, start and end of the production season (Eq 1). The outcome modelled in this case is
whether production is achieved or not in a given week.
^
y¼eb1x1þb2x2þ...bnxnÞ
1þeb1x1þb2x2þ...bnxnÞþð1Þ
Where ^
yis the probability of a given week to have some production, β
1
to β
n
are the parame-
ters to estimate, x
1
to x
n
the independent explanatory variables, and εthe residual error of the
model.
Table 1. The number of producers surveyed within each region as well as the statistics on the starts, ends and lengths (Julian days) of the period
of production.
Region Number of producersSurveyed Starts Ends Lengths
BSL 15 83±8 (67–98) 116±4 (109–121) 33±7 (21–42)
CTS 12 77±7 (66–91) 115±3 (109–119) 38±6 (28–49)
BEA 15 73±11 (60–98) 107±6 (95–118) 33±10 (14–49)
QUE 15 71±9 (60–91) 110±5 (95–115) 38±8 (21–49)
MAU 8 77±12 (60–98) 108±8 (88–117) 31±12 (8–35)
LAN 8 73±10 (54–91) 106±7 (94–115) 33±8 (21–56)
EST 12 73±11 (60–98) 108±6 (95–115) 35±10 (14–49)
LAU 10 73±13 (59–104) 110±10 (95–139) 37±9 (21–49)
CDQ 10 71±11 (54–91) 105±8 (93–115) 34±12 (21–56)
STY 8 66±9 (53–81) 102±5 (94–111) 34±10 (14–42)
VAL 8 65±7 (56–76) 101±6 (87–106) 34±7 (21–42)
Shown is the average±standard deviation as well as the range in brackets.
doi:10.1371/journal.pone.0144844.t001
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 4/14
A categorical variable was created to discriminate the weeks that belong to the period of pro-
duction. To select the explanatory variables, Pearson’s correlation was calculated between the
categorical variable and the climatic variables. Climatic variables with the largest correlations
with the categorical variable were tested individually in a logistic model. The Akaike criterion
(AIC) [17] was used to compare the different models. The model with the smallest AIC was
selected and the correlation between the residuals of the selected model and the climatic vari-
ables was calculated. Variables with high correlation with the residuals were added individually
to the initial model in order to produce improved models. Each improved model was compared
to the initial model with a likelihood ratio test. If the likelihood ratio test detected a significant
difference between the original model and the improved model, the improved model was
retained. Among all the retained improved models, the model with the smallest value of AIC
was kept and the precedent steps were repeated in an attempt to improve the model. These
steps were repeated until additional variables did not improve the model. Once a final model
was obtained, a ten-fold cross-validation was used to test model dependency on the "training"
dataset used to calibrate it [18].
The cut-point (the value between 0 and 1 over which the week was considered as a produc-
tion week) was first established at 0.5 and adjusted iteratively to 0.51 in order to maximise the
sensitivity (ability of the model to accurately classify the production weeks) and specificity
(ability of the model to accurately classify the non-production weeks) of the model.
Climate scenarios
A set of 77 simulations from global climatic models (GCM) was used for syrup production sea-
son projections [19], belonging to three greenhouse gas (GHG) emission scenarios [20]. Simu-
lated daily data, necessary to compute changes in sap flow dates and total production, were
obtained for three temporal horizons: 1971–2000 (reference), 2046–2065 (2050 horizon) and
2081–2100 (2090 horizon). To build local climatic scenarios adapted for the projection of
future maple syrup production, a post-processing method (“daily translation”) was applied on
the raw climate model data variables of daily min and max temperatures and daily precipita-
tion [21]. The required reference database of daily observations needed for the post-processing
procedure was obtained from the ~10km x 10km gridded daily data described in Hopkinson
et al. [22] and Hutchinson et al. [23].
Projecting the production season timing
The model previously obtained for predicting the start, end and length of the production sea-
son was run with the 77 climatic scenarios to predict the impact of climate change on the tim-
ing and duration of the maple syrup production for each of the 11 regions (Fig 1). Weekly
predictive climate variables used in the timing and duration were first calculated for all simu-
lated years. For each of the 77 climate scenarios, each simulated year of data is used to deter-
mine the start, end and length of the syrup production season using the timing and duration
model.
Results and Discussion
Variability in production period between regions and years
The average day (Julian day) when the production of syrup started for the reference period ran-
ged from 65 (VAL and STY) to 83 (BSL) between regions (Fig 2,Table 1). A similar range was
also observed for the end of the production with average Julian days of 101 and 116 for the
same regions, respectively. The average temperature for the period of January to April (T
J-A
),
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 5/14
that ranged from -3.7 (VAL) to -7.3°C (BSL), was a strong predictor of the start and end date
(not shown) for each region (Fig 3). Although such a relationship may appear likely, this is the
first time the temperature sensitivity (corresponding to the slope of the relationship shown on
Fig 3) of the “phenology”of maple syrup production is quantified. Based on the latter equation,
the onset of syrup production is expected to advance by about four days for each degree of
warming. This value is in good agreement with reported values of changes in the date of flower-
ing and leafing for many plant species that fall within 2.5 to 5 days for a warming of 1°C [24].
Clearly, such a model based on average temperature cannot be used to predict the day at which
the season will start for a given year but it can be used to predict the long term changes in the
onset of the maple syrup production using only scenarios of annual temperature (see below).
Despite the fact that the period of observations is not long in terms of absolute number of
years (n = 13 years), the data set included strong between years and between regions variability
in terms of weather and timing of maple syrup production. Weekly data for the years 2010 and
2008, the years with the earliest and the latest production periods, respectively, showed a 4–5
week difference in the timing of peak syrup production (data not shown). On a regional and
yearly basis, the earliest date of onset of syrup production, Julian day 53, was observed in the
STY region in 2010 while the latest (Julian day 104) was observed in LAU in 2003 (Table 1).
Ends of the production period showed similar variability with the earliest and latest ends of
production being observed on Julian day 87 (VAL, 2000) and 139 (LAU, 2003), respectively.
Given that when the production starts later it normally ends later, the average length of the
production season was much less variable than start and end dates (Fig 2;Table 1), ranging
from 31 (MAU) to 38 Julian days (CTS and QUE).
Modelling the season of maple syrup production with the logistic model
For explaining/predicting the production or absence of production of syrup in a given week,
the following model was obtained:
Production ¼5:09 þ0:722 FrThw3
:NbCum 0:014 ðFrThw3:NbCumÞ20:07 GDDCum ð2Þ
The logistic model is made of the cumulated number of freeze/thaw events with a threshold
of 3°C (FrThw3:NbCum) along with the same variable squared. It has often been mentioned
Fig 2. Variation in the start (closed circles) and end (open circles) of season (Julian days, left axis). Regions are ranked by increasing mean annual
temperature (closed triangles, right axis).
doi:10.1371/journal.pone.0144844.g002
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 6/14
that freeze-thaw cycles (with a 0°C threshold) are necessary to induce sap flow for sugar maple.
The threshold of 3°C obtained here may indicate that fluctuations around a given temperature
close to 0°C are sufficient for sap flow to occur. However, the 3°C threshold is the one mea-
sured at the meteorological stations disseminated within each region. It is possible that the
sugar maple stands under study may be located at higher elevation and that they were exposed
to temperature variations closer to 0°C than 3°C. The model also includes the cumulated num-
ber of growing degree-days starting on Julian Day 1 using a 5°C threshold (GDDCum). This
latter variable has a negative effect on the number of weeks with maple syrup production. Its
role in the model is likely for predicting the end of the production period when too much heat
has been accumulated by the maple stands. Overall, the model was successful at classifying
weeks in the proper bin (production or not). The global model accurately predicted 83% of the
production weeks and 95% of the non-production weeks (data not shown).
Projections of future changes in the timing of the production season
The logistic model (Eq 2) was used with the climatic data of 77 scenarios to project future dates
of start and end of the season of syrup production, as well as its length. The absolute values as
well as the delta (changes as compared to the reference period 1971–2000) are shown in
Table 2. Depending on the regions, the logistic model predicts that the start and end of the pro-
duction season will occur 9–13 days, and 15–19 days earlier for the 2046–2065 and 2081–2100
Fig 3. Relationship between average annual spring temperature and average Julian day of the
beginning of the season of syrup maple production for the 11 regions studied. The equation is shown
on the figure along with the r
2
.
doi:10.1371/journal.pone.0144844.g003
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 7/14
periods respectively, as compared to the reference period (Table 2). Given that changes in sea-
son start and end of production dates are nearly similar, the simulations predict no changes in
season length for the 2046–2065 period and only a small (1–2 days) but insignificant increase
for the 2081–2100 period. Overall the magnitude of the changes predicted here are coherent
with the results of Skinner et al. [4] who predicted shifts in the sap season in the order of 15
and 30 days for 2100 using only the B1 and A1fi climate change scenarios, respectively. The B1
Table 2. Projections (average ±standard deviation) for the start, end (Julian days; jd), and length (days; d) of production season and the total
annual production for the reference period 1971–2000, and two future periods, 2046–2065 and 2081–2100.
Mean average date for each period Mean average change relative to 1971–2000
(n = 77 scénarios) (n = 77 scénarios)
Region Variable (1971–2000) (2046–2065) (2081–2100) (2046–2065) (2081–2100)
BSL Start (J.d) 86.2 ±1.4 76.5 ±5.3 69.3 ±7.9 -9.7 ±5.0 -16.9 ±7.7
End (J.d) 123.1 ±0.8 114.0 ±5.4 108.2 ±7.7 -9.1 ±5.2 -14.8 ±7.5
Length (d) 43.9 ±1.9 44.5 ±6.3 45.6 ±8.1 0.6 ±5.8 1.7 ±7.5
CTS Start (J.d) 81.3 ±1.5 71.1 ±5.1 64.3 ±7.0 -10.3 ±4.8 -17.0 ±6.7
End (J.d) 118.3 ±0.9 108.6 ±4.7 102.8 ±7.1 -9.7 ±4.5 -15.5 ±6.9
Length (d) 44.0 ±2.0 44.2 ±6.3 44.9 ±8.7 0.2 ±5.3 0.9 ±7.5
BEA Start (J.d) 74.5 ±1.9 62.7 ±5.3 55.5 ±7.1 -11.8 ±5.5 -19.0 ±7.2
End (J.d) 114.1 ±1.1 103.2 ±5.1 97.2 ±7.1 -10.9 ±4.8 -17.0 ±6.9
Length (d) 46.6 ±2.5 47.1 ±7.6 47.8 ±9.2 0.5 ±7.0 1.2 ±8.2
QUE Start (J.d) 82.5 ±1.8 72.4 ±4.9 65.9 ±6.6 -10.0 ±4.5 -16.6 ±6.4
End (J.d) 118.2 ±0.8 108.7 ±4.6 103.3 ±6.6 -9.5 ±4.5 -14.9 ±6.4
Length (d) 42.7 ±2.0 42.9 ±6.2 43.8 ±8.1 0.3 ±5.2 1.1 ±7.0
MAU Start (J.d) 82.5 ±1.6 73.5 ±4.7 67.3 ±6.3 -9.0 ±4.2 -15.1 ±5.9
End (J.d) 116.7 ±0.9 107.8 ±4.7 102.7 ±6.1 -8.9 ±4.6 -14.0 ±5.9
Length (d) 41.2 ±1.9 41.0 ±6.2 41.8 ±7.4 -0.3 ±5.3 0.6 ±6.3
LAN Start (J.d) 77.3 ±1.5 67.0 ±4.8 60.4 ±6.6 -10.4 ±4.6 -16.9 ±6.2
End (J.d) 113.5 ±0.8 103.2 ±4.9 98.0 ±6.3 -10.3 ±4.7 -15.5 ±6.1
Length (d) 43.1 ±1.9 43.0 ±6.6 44.1 ±7.9 -0.2 ±5.9 1.0 ±6.9
EST Start (J.d) 67.5 ±1.9 55.0 ±5.7 49.0 ±7.7 -12.4 ±6.1 -18.4 ±7.6
End (J.d) 109.4 ±1.1 98.2 ±5.7 91.7 ±7.6 -11.2 ±5.3 -17.6 ±7.2
Length (d) 48.9 ±2.3 49.6 ±8.5 48.8 ±10.4 0.7 ±8.2 -0.1 ±9.6
LAU Start (J.d) 76.3 ±1.5 64.7 ±5.5 57.9 ±6.8 -11.6 ±5.5 -18.4 ±6.6
End (J.d) 111.6 ±0.9 101.3 ±4.9 95.8 ±6.7 -10.3 ±4.7 -15.8 ±6.6
Length (d) 42.3 ±1.7 43.3 ±7.0 44.6 ±7.7 1.0 ±6.6 2.3 ±7.1
CDQ Start (J.d) 69.4 ±1.7 56.3 ±5.3 50.1 ±7.2 -13.1 ±5.6 -19.3 ±7.1
End (J.d) 108.7 ±1.1 98.4 ±4.6 92.3 ±6.9 -10.3 ±4.4 -16.4 ±6.7
Length (d) 46.2 ±2.2 48.7 ±7.2 48.6 ±9.1 2.4 ±6.9 2.4 ±8.5
STY Start (J.d) 61.7 ±1.6 48.3 ±6.3 42.9 ±7.6 -13.4 ±6.4 -18.8 ±7.5
End (J.d) 103.5 ±1.4 92.7 ±5.0 86.2 ±7.0 -10.8 ±4.7 -17.3 ±6.8
Length (d) 48.8 ±1.9 50.8 ±8.5 49.7 ±10.0 2.1 ±7.9 0.9 ±9.3
VAL Start (J.d) 60.6 ±1.6 47.2 ±6.3 42.1 ±7.5 -13.3 ±5.9 -18.5 ±7.0
End (J.d) 102.7 ±1.3 91.7 ±4.9 85.9 ±6.9 -11.0 ±4.6 -16.8 ±6.8
Length (d) 49.1 ±2.3 51.0 ±8.5 50.0 ±10.1 1.9 ±7.5 0.9 ±8.9
Shifts in season start and end between the reference (1971–2000) and future periods are also reported as delta days; d). It is important to note that
the ±standard deviation values reported are indicative of uncertainty in the mean Julian date between the 77 climate scenarios and do not represent year
to year variability. Please see Fig 1 title for region acronyms.
doi:10.1371/journal.pone.0144844.t002
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 8/14
scenario is more similar to the median scenario used in our simulations while the A1fi scenario
is one of the most extreme in terms of increased temperature which explains the particularly
strong changes (30 days) in season timing obtained with the latter scenario.
The projections obtained with the logistic model were compared with the projections of the
model based on average T
J-A
temperature. For this comparison, the emphasis was put on the
start of the production season since the length is not projected to be affected in the future.
Moreover, the start of the season is crucial from an operational point of view for the producers
since they normally tap the maple trees a few weeks before they expect the season to begin. On
some occasions, if sap flow starts much earlier than usual, the producers may not be ready to
collect sap and may face economic losses.
The comparison between the projections of the two models is shown in Fig 4 for the first
day of production (panel A) as well as for its displacement in time (panel B) for the 2081–2100
period (n = 847, 77 simulations x 11 regions). There was a strong agreement between the two
models (r
2
= 0.80, p <0.001) regarding the first day of production which was mainly driven by
the regional gradient (both models are ranking the regions fairly in the same order since there
is an average annual temperature gradient between regions, Fig 2). The displacement in time of
the first day of production (panel B) was also comparable in terms of dispersion (similar vari-
ability and range) and both models yielded identical average displacements of -17.5 ± 7 days
(n = 847). A strong correlation between the numbers of days of displacement was not expected
(as compared to day of start, panel A) since there is no major regional differences in the projec-
tion of displacement (Table 2). The large number of simulations as well as the very good agree-
ment of the two models which are based on different and weakly interrelated variables, suggest
a robust assessment of the projected changes in the future. The results mean that sugar maple
trees, as well as syrup producers, must adapt to seasons that will start earlier than at present
which appears plausible given that past observations included some very early and very late
seasons.
Fig 4. Relationship between the Julian day of the start of the season production (left panel, A) as projected with the logistic model (x axis) and the
T
J-A
model (y axis) for the 2081–2100 period for each of the 77 simulation for the 11 regions (n = 847). The right panel (B) shows the distribution of the
displacement (delta Julian day) of the 2081–2100 vs the 1971–2000 period.
doi:10.1371/journal.pone.0144844.g004
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In our study, the use of a great number of climatic scenarios is useful not only for obtaining
a robust estimation of changes in the timing of the maple syrup production season, but also to
assess the variability around the average estimates. Although the use of the median result of
many climatic simulations is seen as robust [15], there is quite a lot of variation among the 77
simulations for a given region. An example of the detailed results obtained with the logistic
model is illustrated on Fig 5 for the Valleyfield region (VAL). The density probability curves of
each of the date of onset (panel A) or ends (panel B) of production for a given year obtained by
each of the 77 individual simulations and for each period of time considered are shown as well
as for season lengths (panel C). Overall, our projections for the start, end and length of the pro-
duction season tend to suggest an increase in variability for the future, as shown by the density
probability curves that show an increasing spread (panels B and C for date of end and season
lengths) in future projections and also by the increasing standard deviation coefficient in the
period 2041–2060 and 2081–2100 as compared to the 1971–2000 period (Table 2). However,
this apparent increase could be simply due to the large number of climate scenarios used
(based on multiple climate models, driven with different scenarios of GHG emissions), whereas
only one "scenario" will be realized in the future. In fact, total climate projection uncertainty is
known to increase into the future as families of GHG emissions (SRES families) diverge and by
the fact that the various climate models have different sensitivities to a given amount of GHG
forcing [25]. To investigate the potential for a higher variability in the future, we computed the
standard deviation in annual production for each period (n = 20 years each) and simulation. In
other words, we compare the variation in the results obtained for each simulation between
periods, not among simulations. We could not detect a significant change in variation for the
future (Table 3). We thus concluded that the shift to an earlier sap season in the future will not
be accompanied by an increase in between year variability in the timing of maple syrup
production.
The data for the VAL region also show that a few simulations for the 2081–2100 period are
projecting years with short seasons (1–3 weeks), or even no season at all (Fig 5C; red lines
crossing the Y axis—intercept = 0 means no season). These rare events will correspond to
“extreme”years in a climate where the average start of the season for the VAL region will be
displaced by 19 days (Table 2). These events will be more frequent for the southernmost
regions (data not shown), where the climate is already warmer but since their absolute fre-
quency is still projected to be small, no significant impacts on between year variability are pro-
jected. Interestingly, such effect (quasi-absence of a season resulting in a low syrup yield) was
already observed in several US states [26] in the exceptionally warm spring of 2012, perhaps
telling of what the future holds for these regions.
Conclusion
In this study, we built models capable of predicting the start and the end (and thereby length)
of the maple syrup production period based on a unique data set of weekly observations made
at 121 maple stands spread in 11 regions of the province of Québec (which is responsible for
75% of the world sugar maple syrup production). There was a strong spatial and temporal vari-
ability in the timing of the production period. In warmer regions, production began on average
17 days earlier as compared to colder regions. The average date of the beginning of production
can be predicted with success with the average temperature from January to April. A logistic
model using climatic variables associated with the physiological process of sap flow was also
built from the 143 individual observations (13 years x 11 regions) for predicting the period of
syrup production. When used in combination with 77 global climate scenarios, the models pre-
dict that the period of production will begin 9–13 and 15–19 days earlier for the 2046–2065
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 10 / 14
and 2081–2100 periods, respectively, but its length is not projected to change. The strong
agreement between the two independent models suggests a robust assessment of the changes in
Fig 5. Probability density for the start (A), end (B) and length (C) of production season for the Saint-Jean Valleyfield region for the 1971–2000,
2046–2065 and 2081–2100 periods, respectively. Each thin line represents the distribution of the different years within each of the 77 projections based on
the 77 global climate scenarios.
doi:10.1371/journal.pone.0144844.g005
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 11 / 14
Table 3. Year to year variability (mean standard deviation ±1 SD) for the start and end, and duration of the season for the three study periods.
standard deviation for each period
(n = 77 scenarios)
Region Variable (1971–2000) (2046–2065) (2081–2100)
BSL Start (J.d) 12.3 ±2.4 13.2 ±2.9 14.4 ±3.5
End (J.d) 7.4 ±1.6 7.7 ±1.9 8.8 ±2.6
Length
(d)
9.6 ±1.9 11.2 ±2.9 12.4 ±3.3
CTS Start (J.d) 14.3 ±2.6 14.9 ±3.1 15.5 ±3.4
End (J.d) 7.8 ±1.8 8.4 ±2.0 9.5 ±2.6
Length
(d)
11.3 ±2.0 13.4 ±3.2 13.8 ±3.7
BEA Start (J.d) 16.4 ±2.0 16.4 ±3.1 16.8 ±3.0
End (J.d) 8.5 ±1.6 9.4 ±2.4 10.7 ±2.9
Length
(d)
13.4 ±2.2 15.2 ±2.9 15.6 ±3.7
QUE Start (J.d) 12.6 ±2.5 13.2 ±2.9 14.4 ±3.3
End (J.d) 7.4 ±1.6 8.1 ±2.0 9.0 ±2.5
Length
(d)
9.9 ±2.2 12.0 ±3.1 12.9 ±3.3
MAU Start (J.d) 12.0 ±1.9 12.8 ±2.9 14.0 ±3.5
End (J.d) 7.6 ±1.5 8.4 ±1.9 8.9 ±2.1
Length
(d)
9.6 ±1.7 11.6 ±2.8 12.4 ±3.3
LAN Start (J.d) 13.9 ±2.0 14.7 ±3.0 15.3 ±3.5
End (J.d) 8.1 ±1.4 8.9 ±2.1 9.5 ±2.4
Length
(d)
11.1 ±2.2 13.2 ±2.8 13.3 ±3.3
EST Start (J.d) 17.7 ±2.4 17.0 ±2.9 16.2 ±2.6
End (J.d) 9.3 ±2.1 9.9 ±2.4 11.1 ±2.3
Length
(d)
14.9 ±2.3 15.7 ±2.8 15.3 ±3.2
LAU Start (J.d) 14.0 ±1.9 15.4 ±3.0 16.1 ±2.9
End (J.d) 8.2 ±1.6 9.0 ±2.1 9.7 ±2.3
Length
(d)
11.6 ±2.0 14.0 ±2.9 13.8 ±3.1
CDQ Start (J.d) 16.8 ±2.0 16.8 ±3.0 16.6 ±2.7
End (J.d) 8.2 ±1.5 8.9 ±1.9 10.3 ±3.0
Length
(d)
13.8 ±2.0 15.5 ±3.1 15.2 ±3.5
STY Start (J.d) 17.8 ±1.7 16.5 ±3.0 15.5 ±3.0
End (J.d) 8.9 ±1.9 9.6 ±2.2 10.8 ±2.5
Length
(d)
15.0 ±2.0 15.7 ±2.8 14.9 ±3.3
VAL Start (J.d) 18.6 ±1.9 16.7 ±2.9 15.6 ±2.9
End (J.d) 8.9 ±1.7 9.8 ±2.3 10.6 ±2.5
Length
(d)
15.7 ±1.8 15.8 ±2.7 15.1 ±3.4
The standard deviation reported is indicative of uncertainty between the 77 climate scenarios. Please see Fig 1 for region acronyms.
doi:10.1371/journal.pone.0144844.t003
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 12 / 14
timing. The results also show that there will be a higher frequency of extreme years with short
season of production in the future, especially in the southern part of the province. However,
according to our projections, this would not be sufficient to quantitatively increase the inter-
annual variability in the dates of beginning and end of the production season. Given that pre-
dicting “extreme”years with very short season of production is more difficult than predicting
“average years”in terms of production season timing, and considering that the data available
for model calibration covered a relatively short period of time (13 years), caution is warranted
when interpreting the projected occurrence of “short seasons”in the future. More research is
needed to further investigate this possibility and to assess the potential impacts on maple syrup
yield.
Overall, the results show that syrup producers will be facing sap seasons starting earlier than
at present which means that they must adapt by being ready to tap the sugar maple trees earlier
in the season. On the other hand, the projected stability of the inter-annual variability in the
beginning of the sap season, indicate that the syrup producers will not be confronted to a
greater between years uncertainty with regards to the moment of tapping the trees.
Supporting Information
S1 Table. Description of climate variables used in the construction of statistical models.
(DOCX)
Acknowledgments
We would like to thank Jean-Pierre Bellegarde and Simon Trépanier from the “Fédération des
Producteurs Acéricoles du Québec”for their support and for giving us access to their unique
data base of weekly sugar maple production. Special thanks also to Anne Blondlot from Oura-
nos for her invaluable help in starting this project. The costs associated with this research were
covered by the “Ministère des Forêts,de la Faune et des Parcs du Québec”and “Le Fond Vert du
Ministère du Développement Durable,Environnement,et Parc du Québec”within the frame-
work of the Action Plan 2006–2012 on climate change in association with Natural Resources
Canada. Regarding the GCM data, we acknowledge the modeling groups, the Program for Cli-
mate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on
Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-
model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of
Energy.
Author Contributions
Conceived and designed the experiments: DH BC LD. Performed the experiments: HP BC DH
TL. Analyzed the data: HP BC TL AP IC. Wrote the paper: DH AP BC HP TL LD IC. Drafting
of manuscript for publication, translation to English, and critical reviewing: AP.
References
1. Tyminski WPJ. The utility of using sugar maple tree-ring data to reconstruct maple syrup production in
New York. Greensboro: University of North Carolina; 2011.
2. Gabriel WJ. Phenotypic selection in sugar maple for superior sap sugar production. Upper Darby, PA:
Department of agriculture, Forest service, Notheastern forest experiment station, Broomall, PA, USA;
1972. 12 p.
3. Perkins TD, van den Berg AK. Maple Syrup–Production, Composition, Chemistry, and Sensory Char-
acteristics. In: Taylor SL, editor. Advances in Food and Nutrition Research. 56. Elsevier, New
York2009. p. 102–44.
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 13 / 14
4. Skinner C, DeGaetano A, Chabot B. Implications of twenty-first century climate change on Northeast-
ern United States maple syrup production: impacts and adaptations. Climat Change. 2010; 100(3–
4):685–702. doi: 10.1007/s10584-009-9685-0
5. Duchesne L, Houle D. Interannual and spatial variability of maple syrup yield as related to climatic fac-
tors. PeerJ. 2014; 2:e428. s doi: 10.7717/peerj.428 PMID: 24949244
6. Duchesne L, Houle D, Côté MA, Logan T. Modelling the effect of climate on maple syrup production in
Québec, Canada. For Ecol Manage. 2009; 258:2683–9.
7. Murphy BL, Chretien AR, Brown LJ. Non-timber forest products, maple syrup and climate change. The
journal of Rural and Community Development. 2012; 7(3):42–64.
8. Kim YT, Leech RH. Effects of climatic conditions on sap flow in sugar maple. For Chron. 1985; 61
(4):303–7.
9. Marvin JW, Erickson RO. A statistical evaluation of some of the factors responsible for the flow of sap
from sugar maple. Plant Physiol. 1956; 31:57–61. PMID: 16654837
10. Plamondon AP, Bernier P. Modélisation de la coulée de l'érable àsucre (Acer saccharum Marsh.) à
partir d'éléments météorologiques. Can J For Res. 1980; 10:152–7.
11. Pothier D. Effets des coupes d'éclaircie et des variations climatiques interannuelles sur la production et
la teneur en sucre de la sève d'une érablière. Can J For Res. 1995; 25:1815–20.
12. Marvin JW. Physiology of maple sapflow (MR 64): University of Vermont; 1958. 29 p.
13. Plamondon AP. Analyse préléminaire de quelques facteurs écologiques influençant la production de la
sève de l'Acer saccharum. Nat Can (Que). 1977; 104:127–34.
14. Marvin JW. Investigation of the sugar content and flow mechanism of maple sap (MR 62): University of
Vermont; 1957. 7 p.
15. Logan T, Charron I, Chaumont D, Houle D. Atlas de scénarios climatiques pour la forêt québécoise:
Ouranos; Direction de la recherche forestière, MRNFQ; 2011. 55 p.
16. Norby RJ, Heartz-Rubin JS, Verbrugge MJ. Phenological responses in maple to experimental atmo-
spheric warming and CO
2
enrichment. Glob Chang Biol. 2003; 9(12):1792–802.
17. Akaike H. Information theory as an extension of the maximum likelyhood principle. In: Petrov BN, Csaki
F, editors. Second international symposium on information theory. Akademiai Kiado, Budapest1973.
p. 267–81.
18. Bradley E, Gong G. A leisurley look at the bootstrap, the jacknife and cross validation. Am Stat. 1983;
37:36–48.
19. Meehl GA, Covey C, Taylor KE, Delworth T, Stouffer RJ, Latif M, et al. THE WCRP CMIP3 Multimodel
Dataset: A New Era in Climate Change Research. BAMS. 2007; 88(9):1383–94. doi: 10.1175/bams-
88-9-1383
20. Nakicenovic N, Alcamo J, Davis G, deVries B, Fenhann J, Gaffin S, et al. Emissions scenarios. Special
report by Working group III of the Intergovernmental Panel on Climate Change: Cambridge University
Press; 2000.
21. Mpelasoka FS, Chiew FHS. Influence of Rainfall Scenario Construction Methods on Runoff Projec-
tions. J Hydrometeorol. 2009; 10:1168–83.
22. Hopkinson RF, McKenney DW, Milewska EJ, Hutchinson MF, Papadopol P, Vincent LA. Impact of
Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over
Canada. JAMC. 2011; 50(8):1654–65. doi: 10.1175/2011jamc2684.1
23. Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, et al. Develop-
ment and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Tempera-
ture and Precipitation for 1961–2003. JAMC. 2009; 48(4):725–41. doi: 10.1175/2008jamc1979.1
24. Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, et al. European phenological response to cli-
mate change matches the warming pattern. Glob Chang Biol. 2006; 12(10):1969–76. doi: 10.1111/j.
1365-2486.2006.01193.x
25. Hawkins E, Sutton R. The Potential to Narrow Uncertainty in Regional Climate Predictions. BAMS.
2009; 90(8):1095–107. doi: 10.1175/2009BAMS2607.1
26. USDA NASS. Maple Syrup 2012: 2012 maple season too warm. Concord, NH: New England Field
Office; 2012 June 13, 2012. 8 p.
Climate Change and Timing of Maple Syrup Production
PLOS ONE | DOI:10.1371/journal.pone.0144844 December 18, 2015 14 / 14
