785
Climate limitations on the distribution and
phenology of a large carpenter bee,
Xylocopa virginica (Hymenoptera: Apidae)
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Dimitri A. Skandalis, Miriam H. Richards, Todd S. Sformo, and Glenn J. Tattersall
Abstract: We studied climatic correlates of the geographic range of a common large carpenter bee (Xylocopa virginica
(L., 1771)), which reaches farther north than any other Xylocopa in North America. Computational models of the species’
range predicted that summer and winter temperatures limit its northern extent, whereas summer precipitation limits its western extent. We empirically evaluated the climatic constraints imposed by different seasons by examining the winter
low-temperature tolerance of X. virginica, and the timing of activity during spring and summer. The bee’s absolute
low-temperature tolerance (supercooling point) did not differ between two populations at mid- and high latitudes, and was
in excess of requirements of a mean winter minimum temperature. Absolute minimum temperature tolerances may not directly influence the range of X. virginica, whereas other measures of cold tolerance, like exposure duration, might be more
relevant. Between years within a study population, spring emergence dates of bees were significantly predicted by spring
temperatures and weather (April: 6–11 °C; May: 13–17 °C). Between populations across the bee’s geographic range, bees in
warmer climates were observed as much as 2–3 months earlier in the year. This suggests that a major constraint on the
bee’s range is the length of the active season, which may be too short for brood development at high latitudes.
Résumé : Nous avons étudié les corrélats climatiques de l’aire de répartition géographique d’une grande abeille charpentière
commune (Xylocopa virginica (L., 1771)), dont la distribution s’étend plus au nord que celle de n’importe quel autre Xylocopa en Amérique du Nord. Les modèles informatiques de l’aire de répartition de l’espèce prédisent que les températures
d’été et d’hiver limitent son expansion vers le nord, alors que les précipitations limitent son expansion vers l’ouest. Nous
avons évalué empiriquement les contraintes climatiques imposées par les différentes saisons en examinant la tolérance de
X. virginica aux températures basses de l’hiver et le calendrier des activités durant le printemps et l’été. La tolérance absolue
de l’abeille aux températures basses (point de surfusion) ne diffère pas chez deux populations des latitudes moyennes et élevées et dépasse les exigences associées à la température minimale moyenne en hiver. Les tolérances absolues à la température minimale peuvent ne pas influencer directement l’aire de répartition de X. virginica, alors que d’autres mesures de
tolérance au froid, comme la durée de l’exposition, peuvent être plus pertinentes. Au sein d’une même population d’étude
d’abeilles, d’une année à l’autre, il est possible de prédire de manière significative les dates d’émergence des abeilles au
printemps à partir des températures et des conditions climatiques du printemps (avril : 6–11 °C; mai : 13–17 °C). Parmi les
populations sur l’ensemble de l’aire de répartition de l’abeille, les abeilles des climats plus chauds apparaissent jusqu’à 2 à
3 mois plus tôt dans l’année. Ces résultats indiquent que la longueur de la saison d’activité est une contrainte importante
pour la répartition des abeilles, car la saison peut être trop courte pour le développement du naissain aux latitudes élevées.
[Traduit par la Rédaction]
Introduction
A species’ success in a given environment is often rooted
in its physiological tolerances and its range of operating conditions. Species with broad tolerances are expected to occupy
a wide range of habitats, whereas those with narrower tolerances should occupy fewer. Rapoport’s rule describes a general pattern in which more northerly species (especially in
North America and Eurasia) tend to have broader ranges
(Stevens 1989; Ruggiero and Werenkraut 2007), suggesting
that northern species have broader physiological tolerances,
especially with respect to traits like thermal tolerance. Highlatitude (and high altitude) distributions are especially challenging because of the shorter summers and longer winters,
and overall greater climatic variability. Insects in climatically
variable environments must cope with a larger range of operating temperatures and so develop broader ranges of physiological tolerances (Kellermann et al. 2009).
Received 11 November 2010. Accepted 26 April 2011. Published at www.nrcresearchpress.com/cjz on 24 August 2011.
D.A. Skandalis.* Department of Biological Sciences, Brock University, St. Catharines, ON L2S 3A1, Canada, and Department of Biology,
University of Ottawa, Ottawa, ON K1N 6N5, Canada.
M.H. Richards and G.J. Tattersall. Department of Biological Sciences, Brock University, St. Catharines, ON L2S 3A1, Canada.
T.S. Sformo. Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA.
Corresponding author: Dimitri A. Skandalis (e-mail: [email protected]).
*Present address: Institut für Neurobiologie, Universität Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
Can. J. Zool. 89: 785–795 (2011)
doi:10.1139/Z11-051
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786
A key problem for high-latitude insects is low winter temperatures, as freezing is usually lethal (Sinclair et al. 2003).
Most insects avoid low temperatures altogether, and many
temperate-zone bees, such as bumblebees (Alford 1969), are
ground-nesters that overwinter in hibernacula excavated in
soil to depths below the frost line. Honey bees (Apis mellifera L., 1758) maintain hive temperatures above freezing by
colony endothermy (Stabentheiner et al. 2003). In contrast,
bees that nest in twigs or wood (such as the carpenter bee
tribes Ceratinini and Xylocopini) usually overwinter in the
same substrates, so remain above ground in winter. Although
their hibernacula afford shelter from precipitation and may
sometimes be insulated by snow, these bees experience lower
and more variable winter temperatures than do bees that hibernate underground.
For these bees, the principle wintering strategy is to rely
on the passive ability of fluids to freeze at much lower temperatures than their melting point. This process, supercooling,
allows insects to survive tissue temperatures lower even
than –30 °C (Addo-Bediako et al. 2000) but is inherently
probabilistic. Insects have a number of strategies to maximize
their supercooling capacity, and thus their chances of survival, especially through evacuating ice nucleators from the
gut (Krunić and Radović 1974; Tanaka 1996; Sheffield
2008), elevating haemolymph solute concentrations (Sakagami et al. 1981), and production of antifreeze proteins to actively control the growth of ice crystals (Duman et al. 2004),
though this latter tends to be a specialized adaptation in highlatitude species. The magnitude of supercooling available to
an insect can be measured as the lowest temperature it can
withstand before it spontaneously freezes (supercooling point,
SCP). Intra- and inter-specifically, insects at high latitudes
tend to have lower SCPs than those at lower latitudes (Tanaka 1996; Addo-Bediako et al. 2000), making SCP both a
convenient and meaningful basis for comparing cold tolerances. Therefore, for species without other means of avoiding
low temperatures, a low SCP may be a prerequisite for successful colonization in many areas.
The active season presents additional thermal challenges,
as ectothermic animals like insects rely on warm air temperatures to conduct activities such as foraging, mating, and
raising brood. Summer performance should also be related to
species’ ranges because, if the active season is too short for
these activities to be completed successfully, then a species
cannot effectively colonize a region (e.g., Fielding et al.
1999). Reliance on ambient temperatures can be offset by
facultative endothermy, in which insects warm their flight
muscles in anticipation of flight, but do not incur the penalties of obligate endothermy. All bees exhibit some endothermy, but this is particularly dramatic in bumblebees,
which can fly even in snowstorms, at temperatures as low as
0 °C (Richards 1973). Bumblebees can maintain highly elevated thoracic temperatures, which has equipped them to colonize cooler and more thermally variable habitats than other
bees (Bishop and Armbruster 1999), because they can begin
rearing brood earlier in the spring while air temperatures are
still low.
In Canada and the United States, there are seven species of
large carpenter bees in the genus Xylocopa Latreille, 1802
(Hurd 1955). Of these, the eastern carpenter bee (Xylocopa
virginica (L., 1771)) has the largest and the most northerly
Can. J. Zool. Vol. 89, 2011
range, extending from Texas to Maine, USA, and to southern
Ontario, Canada. Large carpenter bees, including X. virginica,
resemble bumblebees in appearance but differ greatly in their
habits. As their name suggests, they excavate nests in wood.
Brood produced in spring and early summer eclose as adults
that overwinter in their natal nests (Gerling and Hermann
1978). Because they are above ground, X. virginica hibernating in cold regions cannot avoid freezing temperatures; however, the wooden nest substrate offers some protection from
the elements, snow cover may sometimes provide insulation,
and on sunny days, nest temperatures may benefit from increased insolation, as this species tends to choose south- and
east-facing nesting substrates (Barrows 1980). Carpenter bees
do not store overwintering provisions in their nests, rather
they must subsist throughout the entire winter (6 months or
more in the northern part of the range) on energy sequestered
during development or obtained by feeding after eclosion and
before hibernation. In winter, bees very rarely emerge from
their hibernacula, and then only in the most unseasonably
warm conditions. More usually, adults remain in their hibernacula until they emerge the following spring to begin flight
activity and reproduction (Gerling and Hermann 1978). Unlike bumblebees, X. virginica only flies when temperatures
reach at least 9–15 °C, even though its thoracic temperatures
in flight exceed 40 °C (Baird 1986).
Xylocopa virginica is notable in that its geographic range
extends into regions with colder and longer winters than experienced by most members of the genus (Hurd 1955). Since
X. virginica does not avoid subzero temperatures (for instance, by overwintering underground), we predicted that it
must exhibit sufficient supercooling capacity to survive the
lowest winter temperatures across its range. More specifically, we predicted that the northern limit of its range might
be dictated by its supercooling capacity, with the bee unable
to move into regions with winter low temperatures below its
SCP.
In addition to surviving low winter temperatures, the establishment of a species in a region may also depend on the
duration of winter. There are two reasons for this. First, longer winters require bees to survive for longer periods on energy stores acquired before hibernation. Second, longer
winters entail shorter summers. Since X. virginica require relatively warm temperatures to initiate spring flight and reproductive activities, and since brood have long developmental
times (Gerling and Hermann 1978), winter duration, the timing of spring, and by extension, the duration of summer, may
also be important factors influencing the geographic range.
Finally, summer flight and reproductive activity are not defined only by warm temperatures, but also by precipitation
patterns, as bees do not forage or provision brood during
rainy weather. Thus precipitation patterns might also influence the geographic range.
We investigated the influence of climate, and especially
temperature, on the geographic range of X. virginica, based
on a multifaceted approach including climate modelling,
physiological measurements of cold tolerance, and analyses
of bee flight activity in different portions of the range. Based
on the premise that an animal’s current distribution reflects
climatic factors which are meaningful for its biology, we employ a theoretical GIS model to identify a minimum set of
climatic factors that closely describe the bee’s range. These
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Skandalis et al.
patterns broadly predicted the impact of winter and summer
temperatures and precipitation on the bee’s range. We substantiated these predictions of seasonal influences with empirical measurements of the animal’s physiological tolerances
and operating conditions. To understand the potential influence of winter temperatures, we tested the bee’s tolerance to
very low temperature (–30 °C and below), by measuring
supercooling capacity and testing for the presence of antifreeze proteins. To understand how winter duration, the timing of spring, and summer duration influence geographic
range, we examined variation in the timing of summer flight
activities, based on activity dates for specimens collected
from across the range.
Materials and methods
Estimation of geographic range
The geographic ranges of the carpenter bees (Xylocopa sp.)
in North America were last estimated by Hurd (1955). To assess the current range of X. virginica, we assembled 1312
specimen records of carpenter bees and confirmed sightings
with known dates. Primary records were obtained from the
literature (in Skandalis et al. 2009) and from insect collections at the American Museum of Natural History, Louisiana
State University, Ohio State University, Provincial Museum
of Alberta, Royal Ontario Museum, University of Arkansas,
University of Connecticut, University of Guelph, University
of Kansas, University of Manitoba, University of Missouri,
US Department of Agriculture, US Geological Survey, Valdosta State University, Washington State University, and
Yale University. Some data were accessed through digitized
records at the Global Biodiversity Information Facility
(http://www.gbif.org). Records of sightings without specimens were kindly provided by personal communication with
several professional hymenopterists. Because X. virginica is
easily recognised in eastern North America, we also included
confirmed observations made from photographs uploaded to
an amateur entomological Web site (http://bugguide.net).
These specimens were visually identified as X. virginica
both by hymenopterist curators on the Web site and by one
of the authors (D.A.S.). Although this data set may be a
biased sampling of the species’ range, as observations of the
bee were more frequent near human settlements or active entomologists (particularly in southern Ontario, New England,
and Florida), we cannot discriminate between observational
and biological densities because this species prefers to nest
in human-treated substrates (Barrows 1980).
To gain insight into the factors that influence or constrain
the geographic range of X. virginica, we used the software
DIVA-GIS version 7.2 (Hijmans et al. 2001) to model the
theoretical impact of individual or sets of climatic factors.
Each observation record was assigned a latitude and longitude based on the city. Where city information was not available, the geographic centre of the county was used;
qualitatively similar results were obtained when this data was
omitted. We filtered the data and removed multiple observations that occurred within a 1° × 1° grid (to reduce the impact of the noted sampling biases). In the 1312 records,
there were 409 unique grid locations, and these were used
for climate modelling. Local climatic conditions at each of
these unique locations were used to identify a probable spe-
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cies geographic range using ecologically relevant climate data
(bioclimatic data; 5 arc-min resolution data downloaded
March 2010 from http://www.worldclim.org; detailed in Hijmans et al. 2005), according to the DOMAIN algorithm in
DIVA-GIS. The presence data (known absences are not required by the algorithm) are used to determine the animal’s
range of environmental conditions, and then a probable species range is predicted from geographic locations that have
climates within 5% of the species range (Carpenter et al.
1993). The range prediction modelling is most powerful
when the full set of 19 bioclimatic variables are included,
but the set can be restricted to examine the contribution of
subsets or individual variables separately. To quantitatively
assess the contribution of each variable, a model was trained
with 75% of the known data points, and random absence data
were created at 10× the number of presence points. The quality of the range predicted from limited sets of climatic variables was assessed with a receiver operating characteristic
(ROC). The area under the ROC curve (AUC) approaches
unity when the model is most accurate and approaches 0.5
when the model does not make predictions any better than
expected by chance. With this method, we focused on comparing the effects of temperature and precipitation calculated
for the coldest and warmest, and the wettest and driest, quarters of the year.
Winter low-temperature tolerance
Cold tolerance determination
To link theoretical predictions on the effect of winter temperatures to overwintering physiology, we studied the bee’s
cold tolerance. We first examined the survivorship and performance after exposure to low temperatures of X. virginica.
Twelve individuals (from Maryland 2005) were cooled to
–20 °C and then rewarmed to room temperature (at the same
rate). After rewarming, we observed locomotor function
(walking) to assess obvious signs of cold-related damage.
We then measured the magnitude of cold tolerance in overwintering bees from the Niagara region of southern Ontario
(43°N, 79°W), close to the northern edge of the species’
range, and the USGS Patuxent Wildlife Research Center
(PWRC) in Maryland, USA (39°N, 76°W), in the central portion of the range (Fig. 1, Table 1). Cold tolerance was measured as the supercooling point (SCP), the lowest temperature
of the tissue immediately before spontaneously freezing
(Fig. 2). Specimens were kept at ambient temperatures while
being removed from their nests, then taken to the laboratory
and housed constantly between –1 and 0 °C until experimentation. All specimens were weighed immediately on arrival in
the laboratory. After freezing experiments, specimens were
desiccated and weighed until successive masses differed
by <1.0 mg (<0.5% of dry mass).
To measure SCP, bees were cooled at a rate of 2 °C·h–1; in
2005, this was achieved by decreasing the temperature at
hourly intervals, whereas in 2006 and 2009, the temperature
decrease was continuous with an automated temperature controller. This cooling rate was chosen because preliminary results suggested a considerable elevation of SCP when a rate
of 4 °C·h–1 was used or when bees were placed directly into
–30 °C temperatures, which is not biologically realistic.
Moreover, Kelty and Lee (1999) observed that survivorship
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Fig. 1. Range of a large carpenter bee (Xylocopa virginica) in North America. Specimens with geographic locations were used to develop
models of the effect of climatic factors on the species’ range, as computed by the DOMAIN algorithm implemented in DIVA-GIS. Circles
show the specimen locations used to build the model, and shaded areas indicate predicted ranges. The lightest shade corresponds to the best
range model, whereas the darkest shade represents the poorest model. White arrows with black outlines denote populations studied for cold
tolerance (lower arrow: Beltsville, Maryland; upper arrow: Niagara, Ontario).
Table 1. Collection information for specimens of a large carpenter bee (Xylocopa virginica) used in assessments of cold tolerance.
Location
Beltsville, Md.
Niagara, Ont.
Date collected
15 March 2005
15 March 2005
26 January 2006
10 July 2005
10 January 2006
5 January 2009
No. of specimens
67
37 in 9 nests (21 ♀, 16 ♂)
37 in 7 nests (19 ♀, 18 ♂)
4♀, flying
53 in 12 nests (39♀, 14♂)
24 in 8 nests (11♀, 13♂)
Measured variables
Overwintering survival
Winter SCP
Winter SCP
Summer SCP
Winter SCP
Winter SCP, thermal imaging, haemolymph thermal hysteresis
Note: The Niagara region of southern Ontario, Canada (Ont.; 43°N, 79°W), is close to the northern edge of the species’ range. The USGS Patuxent
Wildlife Research Center (PWRC) in Beltsville, Maryland, USA (Md.; 39°N, 76°W), is in the central portion of the range (Fig. 1). Nests were obtained
in different years because of complications in obtaining animals in mid-winter from human structures. SCP, supercooling point.
of fruit flies (Drosophila melanogaster Meigen, 1830) at subzero temperatures was higher when specimens were cooled at
0.1 °C·min–1, rather than at 1 °C·min–1. For bees collected in
2005 and 2006, dorsal thoracic temperature was recorded
through a T-type Cu–Cn, low inertia thermocouple held in
place by a duct-tape harness. Up to four bees were cooled simultaneously in individual 50 mL tubes immersed in a cooling bath. Thermocouple data were acquired continuously
through a TC-1000 thermocouple meter (Sable Systems International, Las Vegas, Nevada, USA) and recorded with software AcqKnowledge version 3.8.1 (BIOPAC Systems, Inc.,
Goleta, California, USA) at 5 Hz, with each thermocouple
measuring within ±0.1 °C. For bees collected in 2009, we
additionally asked whether some body parts were at an increased risk of freezing. Therefore, temperature data were recorded with a thermal camera (MikroScan 7515 Thermal
Imager, Mikron Infrared®, Oakland, New York, USA; discussed by Tattersall et al. 2004) at 0.2 Hz, allowing the
whole body to be efficiently viewed simultaneously.
Haemolymph thermal hysteresis
To test for the presence of antifreeze proteins, samples of
haemolymph were removed from one male and one female
from each of four nests of wintering bees in Niagara in
2009, and seeded with ice crystals to observe hysteresis of
the freezing and melting points. Hysteresis indicates
non-colligative factors that control the initiation of nucleation. The cuticle was pierced with a 25 gauge needle, and a
10 µL glass pipette was used to wick up the haemolymph
sample (≤0.25 µL). Between 25 and 100 nL of hemolymph
was delivered through a micrometre syringe into a nanolitre
osmometer well (Otagio Instruments) that contained heavy
mineral oil. The sample was quick frozen and then warmed
until a single ice crystal remained. The melting and freezing
points, as well as ice-crystal morphology, were determined at
200× magnification. In the absence of antifreeze proteins, ice
crystals grow as round, flat disks, but in the presence of antifreeze proteins, ice crystals grow hexagonally (Griffith and
Yaish 2004).
Influence of climate on summer activity patterns
Comparisons between population
We used mean spring temperatures at the georeferenced locations to examine how the flight activity period of bees
changes with climate. Dates attached to museum collection
records (see previous) were used to approximate the active
period of the bees, assuming no seasonal observational bias
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Fig. 2. Time course of a large carpenter bee (Xylocopa virginica)
supercooling and freezing. Selected images demonstrate the bee’s
thermal profile as the body equilibrates to ambient temperatures
(colours correspond to temperatures on the superior x axis). Nucleation in the head and abdomen are responsible for a large proportion
of the freezing events (inset).
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isons of observations between years, even when emergence
was extremely rapid (e.g., fraction of observed individuals increased from 0.3 to 0.7 within 1 day). All fitting parameters
were highly significant.
Data at hourly intervals for temperature and weather were
obtained from the Environment Canada weather station at
St. Catharines A (43.20°N, 79.17°W; obtained from http://
www.climate.weatheroffice.gc.ca, accessed 29 June 2010).
Weather was scored on an ordinal scale of 0–4, with 0 representing rain and snow, 1 as drizzle or fog, 2 as mostly
cloudy, 3 as cloudy or hazy, and 4 as clear conditions. Data
were averaged to find the mean daily or monthly temperature
and weather score. The cumulative temperature was determined by summing over all temperatures within a given period.
Data analysis and statistics
Statistical analyses were performed using the software R
version 2.10.1 (R Development Core Team 2008). Significance (a = 0.05) was tested with linear models (type I sums
of squares) for continuous responses and c2 tests for nonordered factor responses. The comparison is described where
relevant. We report partial F scores or c2 statistics, with degrees of freedom as subscripts.
Results
in collection of the records. Across all years, observations
were grouped by their ordinal date (with 1 January as day 0)
for comparisons of spring (<183, end of July) or autumn
(≥183, beginning of August). Multiple observations for a single date and location were removed, resulting in 377 unique
records for spring and 333 unique records for autumn. The
annual temperature at each location was extracted using
DIVA. The date of observation in spring and autumn was
then correlated with the annual temperature.
Variation within a population
The length of winter hibernation and subsequent start of
summer activity periods were inferred from annual observations of bee flight activity at a nesting aggregation at Brock
University in Niagara (43.12°N, 79.25°W; Prager 2008; Peso
and Richards 2010; C. Course and M.H. Richards, unpublished data). Each spring from 2003 to 2009, the site was
monitored on clear days suitable for bee flight, starting from
the first date on which the first adult bee in the aggregation
emerged. Adult bees were regularly caught and paint marked
with a unique colour combination until no unmarked bees
were observed flying in the aggregation; the cumulative number of marked bees estimates the cumulative numbers of bees
that have emerged from their nests to initiate flight activity
each spring (Peso and Richards 2010). We estimated the rate
of spring emergence by fitting curves (logistic or Weibull
functions, according to the better fit) to the cumulative numbers of marked males and females observed each year from
2003–2009, then estimated the date at which half of the population had been marked. In 2010, adults were not marked,
but the dates of first emergence and flight activity were recorded. This process allowed us to make quantitative compar-
Predictions of geographic range
The best theoretical model of the geographic range of
X. virginica was obtained by using all 19 bioclimatic factors
(Best in Fig. 1; ROC AUC = 0.854), but reducing the number of explanatory factors to just annual temperature and precipitation was also effective (AUC = 0.787). Climatic
variation in certain seasons of the year may be more important than annual variation, so we focused on indices that described quarters of the year, namely the coldest, warmest,
wettest, and driest quarters. Temperatures over all quarters
(AUC = 0.743) provided a stronger prediction of range than
did precipitation over all quarters (AUC = 0.668). However,
temperature predicted a more limited northern extent of the
species range, whereas precipitation predicted a more limited
western extent. The coldest and warmest quarters individually
contributed approximately equally to temperature effects
(AUC = 0.597 or 0.586, respectively). The coldest quarter
limited the northern extent of the range, whereas the warmest
quarter limited the westward extent (combined as Temperature model in Fig. 1). The warmest and wettest quarters contributed the largest precipitation effects (AUC = 0.640 or
0.625, respectively), but the wettest quarter provided few
novel predictions beyond those of the warmest quarter alone,
so it was omitted (Precipitation model in Fig. 1). The best
general model with reduced numbers of predictors was generated using temperatures in the coldest and warmest quarters
of the year, and precipitation in the warmest quarter of the
year (AUC = 0.800; Temperature + Precipitation model in
Fig. 1). This suggests that these three factors are the principal
constraints on the species’ range, although the northern extent of the range in Ontario, Canada, and the range in the
Canadian Atlantic provinces were somewhat poorly predicted.
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Can. J. Zool. Vol. 89, 2011
Table 2. Freezing and melting points (°C) of the haemolymph
of the large carpenter bee (Xylocopa virginica).
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Nest ID
and sex
A1 ♂
A2 ♀
D1 ♂
D2 ♀
G1 ♂
G2 ♀
H1 ♂
H2 ♀
Freezing point
–5.08
–5.98
–3.52
–3.09
–6.24
–5.22
–7.81
–6.61
Melting point
–5.04
–5.91
–3.46
–3.05
–6.21
–5.13
–7.75
–6.58
Thermal
hysteresis
0.04
0.07
0.06
0.04
0.03
0.09
0.06
0.03
Fig. 3. Population profiles of supercooling points (SCPs) in a large
carpenter bee (Xylocopa virginica) compared with the mean minimum winter temperatures (Tmin) across the species’ range. The boxplots on the left compare SCPs of bees from Niagara, Ontario, and
Beltsville, Maryland (boxes: medians and quartiles; whiskers: 10th
and 90th percentiles; circles: outliers). SCPs of bees from Maryland
did not significantly differ between years, although those from bees
in Ontario did; overall, there was no latitudinal effect on SCP. The
histogram on the right shows the range of mean minimum temperatures experienced by all georeferenced bee populations (see Fig. 1).
Note: The ID label signifies the individual’s nest, number, and
sex. There is significant thermal hysteresis, the difference between
freezing points and melting points, though the magnitude is too
small to be biologically significant.
Tolerance to low temperatures
We first tested the lethality of cold exposure by cooling
and rewarming bees. Nine individuals did not freeze during
the course of cooling (i.e., they remained supercooled), but
three individuals froze and were dead upon rewarming. Of
the nine that survived cooling, all exhibited apparently normal locomotor activity after warming. The measured SCP is
also seasonal: four females in Niagara cooled in mid-July of
2005 exhibited mean SCPs of only –7.7 to –8.1 °C, which
was approximately 10–20 °C warmer than in winter. Therefore, X. virginica does supercool, is intolerant of freezing,
and shows a marked preparation for winter.
We examined whether the bee uses antifreeze proteins to
promote its supercooling, which can be detected by thermal
hysteresis of the haemolymph. We did not detect any appreciable hysteresis since, despite a statistically significant difference in haemolymph freezing and melting temperatures
(Table 2; Wilcoxon rank test, V = 36, p = 0.01), the magnitude of the hysteresis (0.05 °C) was too small to be biologically relevant (i.e., compared with Duman et al. 2004).
Xylocopa virginica are large insects (mass ~0.6 g, length
~2 cm), so SCP measured at the thorax might not adequately
reflect the temperature at the initiation of freezing if there is
a delay in the propagation of the ice front through the body.
To substantiate SCP as a measure of whole-animal freezing,
we ascertained how quickly the ice front propagated across
the body, using thermal imaging. The ice front propagated
across the body 5–30 s after nucleation, whereas the freezing
exotherm lasted 10–15 min (Fig. 2). In a few bees, minor
freezing events occurred before and after the major SCP
event, but we could not consistently quantify these or measure their impact on survival.
We compared SCPs of bees from the Niagara region near
the northern edge of the range to those from Maryland in the
central portion of the range (Fig. 3). For statistical comparisons, SCP data were arcsine-transformed as a single group
(Shapiro–Wilk test for normality: W = 0.99, p = 0.30). Sex
did not significantly affect SCP, so the sexes were combined
(sex: F[1,149] = 0.67, p = 0.41). Bees from Niagara differed
significantly in SCP between years, but bees from Maryland
did not (Niagara: F[1,75] = 12.16, p < 0.001; Maryland:
F[1,72] = 0.65, p = 0.42). Geographic differences in SCP
were weak when all bees were considered (F[1,149] = 3.07,
p = 0.08; mean difference = 1.98 °C). If only those bees
measured in exactly the same way (in 2006) were compared,
then the statistical difference between Niagara and Maryland
was significant but slight (F[1,88] = 5.126, p = 0.03, R2 =
0.04).
We examined the influence of body size on SCP, considering bees from all populations together. Geographic patterns
of body size were detailed by Skandalis et al. (2009) and the
added 2009 Niagara population followed the same patterns
(not shown). Briefly, bees from Maryland and Niagara do
not differ in mass, but bees from Niagara have significantly
smaller morphological dimensions, such as the width of the
head and length of the wing. We questioned whether such
patterns in body size might represent a strategy for reducing
SCP; smaller bodies with lower water content naturally
supercool more. Using thermal imaging (0.2 Hz) to record
which body part froze first, the origin of nucleation could be
distinguished in 17 of 24 cases: nine times in the abdomen,
six times in the head, and once each in the legs and thorax
(inset, Fig. 2). This distribution was not significantly different
from random (legs and thorax treated together: c2½2 = 4.35,
p = 0.10), nor were relatively larger body parts likely to
freeze first (thorax and legs treated together; mass scored by
quartile; c2½8 = 13.69, p = 0.09).
Finally, we examined the matching of SCP to local temperatures experienced by bees across their range. The local
mean minimum winter temperature at each unique georeferenced location in Fig. 1 was extracted in DIVA, and the resultant distribution of temperatures is shown as a histogram
alongside SCPs in Fig. 3. The SCPs of bees from both Niagara and Maryland were lower than the lowest temperatures at
any point within the geographic range of X. virginica.
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Fig. 4. Observation dates of sightings or collections of a large carpenter bee (Xylocopa virginica) compared with local annual temperatures across the geographic range. Dates are provided as day of
the year, from 1898 to 2009, with 1 January as day 0. Solid circles
represent observations in spring (day <183) and open circles represent observations in autumn (day ≥183). A warmer local climate
predicted significantly earlier observations in the spring, though
there was no significant effect on autumn observation timing.
791
Fig. 5. Cumulative numbers of adults of a large carpenter bee (Xylocopa virginica) marked in the nesting aggregation at Brock University, St. Catharines, Ontario. Data were fitted to estimate the rate
of emergence from overwintering.
Influence of temperature on the timing of summer
activity
Comparisons between populations
We predicted a latitudinal cline in emergence dates and duration of summer bee activity, with bees in more southerly
populations being observed both earlier and later in the year
than in more northerly populations. Geographic records in
Fig. 1 with recorded observation dates were divided into
spring (observation date <183) or autumn observations
(date ≥183). In general, southern bees were observed as early
as February, whereas northern bees did not become active
until May (Fig. 4; effect of latitude on spring observations:
r = 0.44, F[1,377] = 91.73, p < 0.001). This latitudinal pattern
was driven largely by differences in temperature across a latitudinal gradient, with higher annual temperatures in southerly populations predicting earlier observations in spring
(r = –0.43, F[1,377] = 86.45, p < 0.001). There was neither a
latitudinal effect nor a temperature effect on the timing of autumn observations (latitude: F[1,333] = 1.432, p = 0.23; temperature: F[1,333] = 1.73, p = 0.19).
Variation within a single population
We used annual censuses of our study population in Niagara to examine whether annual variation in temperature influenced the timing of spring and summer periods of flight
activity (Fig. 5). Specifically, we tested whether warmer temperatures and better weather resulted in earlier observations
of bees. On average, males emerged from overwintering
20 days earlier than females (F[1,12] = 15.92, p = 0.002) in
mid-April. There was no consistent change from 2003 to
2010 in the first date on which males were observed flying
(F[1,6] = 0.37, p = 0.57), but the midpoint date of male emergence nearly significantly advanced between 2002 and 2009
(cumulative data were not collected for either sex in 2010;
r = –0.73, F[1,5] = 5.72, p = 0.06). In each year, temperatures
in the week prior to emergence ranged from 8 to 22 °C, so
air temperature alone did not predict the timing of first male
emergence; neither were the first observations predicted by
April mean or cumulative temperatures (mean: F[1,6] = 0.41,
p = 0.28; cumulative: F[1,6] = 1.56, p = 0.13). On the other
hand, the midpoint of emergence advanced in years with
higher mean and cumulative April temperatures (Fig. 6;
mean: r = –0.74, F[1,5] = 6.03, p = 0.03; cumulative:
r = –0.78, F[1,5] = 5.27, p = 0.04). Weather scores were not
associated with the timing of male emergence (midpoint:
F[1,5] = 0.46, p = 0.27).
From 2003 to 2010, the first observation of females significantly advanced (r = –0.85, F[1,6] = 15.52, p = 0.008), but
from 2003 to 2009, the midpoint of female emergence did
not advance (F[1,5] = 3.33, p = 0.13). The dates of first observations of females were not predicted by temperatures at
any point in the preceding week, as these ranged from 12 to
22 °C. Nonetheless, females were observed earlier in years
with higher cumulative April temperatures (r = –0.94,
F[1,6] = 15.52, p = 0.004; high r owing to an exceptionally
warm April 2010). The midpoint of cumulative observations
of females was best predicted by the weather score in May
(Fig. 6; weather score: r = –0.89, F[1,5] = 20.02, p = 0.004;
mean temperature: F[1,5] = 2.55, p = 0.09).
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792
Can. J. Zool. Vol. 89, 2011
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Fig. 6. Temperature and weather effects on the timing of the midpoint of emergence of a large carpenter bee (Xylocopa virginica) at the
nesting aggregation at Brock University, St. Catharines, Ontario. Day 0 is 1 January.
Discussion
We combined theoretical and empirical observations and
found that temperatures at different seasons have a strong effect on the ecology of X. virginica. Our range modelling
(Fig. 1) implicated both winter and summer factors in the
distribution of this bee. These factors can be inter-related,
such as summer precipitation patterns and summer temperatures (Gordo et al. 2010). Therefore, to gain insight into the
most likely impacts of climate on the species’ biology and
range, we studied the bee’s physiological tolerance or behaviour in different seasons. To dissect the contributions of different seasons and to validate the theoretical model in Fig. 1,
we studied both the bee’s cold tolerance and its active season
phenology.
Winter cold tolerance
Our modelling implicated winter as a limiting factor in the
distribution of X. virginica. Like most temperate-zone insects,
bees spend more time in hibernation than they do in the active, reproductive phase of their lives. Because of the shorter
duration of summer at high latitudes, increasing numbers of
bee species overwinter as adults to gain a faster start in
spring (Sheffield 2008). Xylocopa virginica overwinters in
the adult stage, is freeze-intolerant, and its lower lethal temperature appears to be its SCP. This is consistent with the observation that in most species tested for both SCP and lower
lethal temperature, when the SCP is close to –30 °C, so is the
lower lethal temperature (Addo-Bediako et al. 2000). As
well, wood burrows of X. virginica are thoroughly cleaned
of debris before overwintering, which minimizes the risk of
external nucleators. Xylocopa virginica does not produce
antifreeze proteins to inhibit the nucleation and propagation
of ice crystals, and so far none have been found in any Hymenoptera (Duman et al. 2004). SCPs in summer were considerably higher than those in winter, a usual result of spring
feeding (Krunić and Salt 1971; Sheffield 2008). Together,
this indicates that the wintering strategy of X. virginica relies
on minimizing the probability of freezing during supercooling by evacuating ice-nucleating agents and increasing the
concentration of colligative solutes.
The range of SCPs, from –20 to –30 °C, is consistent with
our original hypothesis that the bee would tolerate very low
temperatures because it has little other available thermal buf-
fering; queens of buff-tailed bumblebees (Bombus terrestris
(L., 1758)), which are the same size but instead nest underground below the frost line, have SCPs around –15 °C
(Cheng et al. 2009). However, there were no significant differences in SCPs between the Niagara and Maryland populations, and SCPs tended to be 10–15 °C lower than mean
minimum winter temperatures across the bee’s geographic
range (Fig. 3). This suggests that mean SCP actually does
not closely track mean winter temperatures. Our measurements likely represent good approximations to cold tolerance
in the field, as we used comparatively low cooling rates
(2 °C·h–1) that should allow cold-hardening to take place
(Kelty and Lee 1999). However, as Niagara winters are only
4–5 °C colder, on average, than in Maryland, the difference
may not be extreme enough to observe differences in SCP;
the situation could be considerably different if comparing
wintering bees in Florida.
We also predicted a theoretical northern limit beyond the
species’ current range (Fig. 1), which coincides with the observed excess of SCP over local mean minimum winter temperatures (Fig. 3). However, the latter comparison assumes
the bee is regularly exposed to lethally low winter temperatures. For instance, burrowing in wood may buffer transiently
extreme low temperatures, such as those at night. Another
possibility is that the SCP is related to the unpredictability
of winter temperatures and to extreme low-temperature
events that can kill an entire population. Bees might be able
to extend their range into colder regions when winter temperatures are relatively mild (and above SCP), but extreme
events that fall below SCP, even rare hundred-year temperature lows, would re-establish older range boundaries. In
southern Ontario, the bee’s apparent northern limit, such historical extreme events are generally around –30 °C or lower
(Environment Canada, National Climate Data and Information Archive), which is very similar to the limit of SCP in
any individual.
Apart from the unpredictability of winter, the northern
bound of the species range may reflect the stochasticity in
SCP itself. As such, SCP may not be a measure so much of
absolute tolerance, but the probability of withstanding higher,
subzero temperatures. Actual low-temperature tolerance and
survival is influenced by the speed at which temperatures
drop and the duration of exposure to low temperatures.
Although we could not test the effects of longer exposures
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to cold on tolerance, in a preliminary trial in which four bees
were held supercooled at a constant –18 °C overnight, one
bee spontaneously froze after several hours. The probability
of freezing thus increases both with exposure duration and
temperature, even at temperatures warmer than the SCP. The
northern limit to the bee’s range may therefore reflect the
boundary where mean winter temperatures result in dangerously high freezing probabilities. This implies that low SCPs
(less than –20 °C) may be a side effect of reducing the
probability of freezing at higher temperatures (greater than
–20 °C).
Timing of spring and summer activity
We found that warmer spring temperatures resulted in earlier emergence from hibernation both across the geographic
range (Fig. 4) and within a population (Fig. 6). The timing
of spring emergence of A. mellifera (Iberian Peninsula) is
also best predicted by spring minimum temperatures (Gordo
et al. 2010). Not only is this likely true for most bee species
that overwinter as adults (Sheffield 2008), but it appears to
be a fairly general pattern in insects (e.g., Roy and Asher
2003; Cocu et al. 2005; Dell et al. 2005; Gordo and Sanz
2006). Precipitation patterns are also crucial; precipitation
was identified as a theoretical constraint on the bee’s range
(Fig. 1), and poorer weather scores delayed female emergence across years (Fig. 6).
Across the geographic range, northern bees from colder
climates generally were first observed in April or May, as
long as 2 or 3 months after reported sightings in the southernmost populations. Therefore, northern bees must hibernate
for much longer than southern bees which, excluding temperature effects on metabolic rates, also means that they must
subsist on stored energy reserves for much longer periods.
Spring signals the end of freezing conditions and the beginning of temperatures that allow flight activity and brood provisioning. The exact temperature cue for emergence appears
to differ between males and females. Males emerge earlier in
years with high cumulative temperatures, whereas females
emerge after a period of sustained high cumulative temperatures, and good weather thereafter. It has been reported that
adults of X. virginica emerge from their nests once they have
experienced a week of sustained temperatures over 20 °C
(Gerling and Hermann 1978), and that they then forage on
any amenable day when temperatures are over 13–14 °C
(Gerling and Hermann 1978; Baird 1986). Evidence from females corroborates this, as the females first emerged after
sustained warm temperatures and then with amenable
weather. However, we do not find a consistent temperature
threshold for emergence, as conditions in the week prior to
first observations at the Niagara site were occasionally over
20 °C, but were usually 10–15 °C, and sometimes were as
cold as 8–9 °C. Exceptionally warm days in winter can induce some bees to leave the nest: during an unseasonably
warm period in Niagara in mid-March 2010, an adult female
was seen outside her nest, although there were no flowers for
her to feed on (J. Vickruck, personal communication). In
general, X. virginica, especially females, do not all emerge simultaneously. The minimum temperature for emergence is
clearly physiological. Goller and Esch (1990) tested the excitability of muscles of X. virginica and found that it enters chill
coma and is incapable of muscle contraction at 8.5 °C, dis-
793
plays half maximal muscle potential amplitudes at 11 °C,
and double muscle potential duration at 16 °C compared
with 25 °C. Consequently, bees should avoid flying in low
air temperatures (9–13 °C) unless they can expect high rewards and insolation (Baird 1986). Nonetheless, we observed
that some males emerged at these low temperatures, suggesting that the benefits of protandry and territory establishment
sometimes outweigh the cost of flying at suboptimal temperatures.
Climatic limitation of range
The current evidence strongly implicates a limitation of
range by the species’ spring phenology, in addition to the
constraints imposed by winter low temperatures. In Niagara,
bees begin to emerge from their nests in mid-April to midMay, whereas bees in Athens, Georgia, USA. (Gerling and
Hermann 1978) and in Maryland (S. Droege, personal communication), begin foraging in March and many have finished laying eggs by mid-May. Conversely, observation dates
in autumn did not significantly differ (Fig. 4), meaning that
across their range, bees end summer activities at about the
same time, in September–October, but more northern populations have shortened active periods. As a result, in Georgia
and Maryland, brood complete development and eclose in
mid-summer, whereas in Niagara, nests opened at the end of
August 2003 contained pupae that would probably not have
finished developing until mid-September (M.H. Richards, unpublished data). Thus, the northern edge of the range may be
dictated by the arrival of spring conditions that allow bees to
begin flight; if spring is too delayed (both by low temperatures and precipitation), there is insufficient time for brood
to finish development.
Such a constraint may explain why SCP need not match
apparent ecological requirements, which may be common.
Sakagami et al. (1981) noted that two species of dwarf carpenter bees (Ceratina flavipes Smith, 1879 and Ceratina japonica Cockerell, 1911) on the Japanese island of Hokkaido
are conspicuously absent from several areas where mean winter temperatures are well within their tolerances, whereas
they flourish in other regions of Japan with similar winter
climates. Sakagami et al. (1981) suggested that climate in
the active season was likely limiting their geographic distributions. Fielding et al. (1999) suggested that the distribution
of the spittlebug (Neophilaenus lineatus (L., 1758)) is limited
by the factors influencing the timing of oviposition. At higher
altitudes or in colder years, emergence of female spittlebugs
is delayed, which increases the risk that they will not have
finished ovipositing before they freeze in the autumn.
Precipitation and weather influence summer activity, but
may also influence the availability of suitable nesting substrate, either directly by promotion of forest growth or indirectly through its influence on human land use. Xylocopa
virginica prefers nesting in lumber structures and there are
relatively few observations of nesting behaviour in natural
substrates, such as dead trees, suggesting that humans may
be promoting population growth and spread of this species.
Future studies might investigate the interaction of winter tolerance, climate, and historical patterns of land use and nesting substrate availability.
Using the information gained in this study to predict range
and phenological changes with climate change is tempting.
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794
There is anecdotal evidence that in recent years, X. virginica
has been extending its range northward (C.S. Stubbs, personal communication) and that population densities at the
northern edge of the range (e.g., Niagara) have been increasing. This suggests that as global climate change leads to
warmer winters and earlier springs in eastern North America,
the geographic range of X. virginica could expand. However,
this projection may be too simple. Our range models showed
that annual precipitation is also important, restricting the
westward expansion of this species. Global climate change is
likely to lead to quite different precipitation patterns that
could counteract the influence of warmer temperatures and
longer breeding seasons. Moreover, with global climate
change, weather is predicted to become more variable overall,
so the number of extremely low winter temperature events
could actually increase, decimating populations establishing
at the limits of the species’ range. If the duration of winter
becomes more variable, it will be difficult for populations to
predict wintering stores and starvation could be more frequent. Although cold temperatures present risks for insects,
they also promote wintering by depressing metabolism and
reducing the rate of stores use. If winters warm, but the duration of the wintering period does not decrease, then the insects might well starve before they can emerge to feed.
Acknowledgements
We are grateful to Rachael Acott, Melanie Ashcroft, and
Valerie Tattersall whose properties were dismantled during
mid-winter searches for hibernating bees, and to Michael Arduser, John Ascher, Jeffrey Barnes, Andrew Bennett, Matthias Buck, Louise Dumouchel, Dave Holder, Ryan Kimbell,
John Morse, Karen Needham, John Pascarella, Leif Richardson, Scott Shaw, Kristin Simpson, Julianna Tuell, David
Wagner, Amy Wolf, and Richard Zack, for assistance in collection of specimen records. Chris Course, Sam Droege
(PWRC), Marianne Peso, Sean Prager, Amy Rutgers-Kelly,
and Jess Vickruck provided invaluable assistance with data
collection at various stages of the project, including spring
marking data and specimen collection. We also acknowledge
valuable advice and discussions particularly with Brent Sinclair and Fawziah Gadallah, and John Barthell, Charles Darveau, Dan Gerling, and Jeremy Kerr, and several anonymous
reviewers whose insight substantially improved the manuscript. This research was supported by a Brock University
Undergraduate Student Research Award to D.A.S., Natural
Sciences and Engineering Research Council of Canada
(NSERC) Discovery Grants to M.H.R. and G.J.T., and a
Canadian Foundation for Innovation award to G.J.T.
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