Global patterns in fruiting seasons

DOI: 10.1111/j.1466-8238.2008.00408.x © 2008 The Authors

648

Journal compilation © 2008 Blackwell Publishing Ltd www.blackwellpublishing.com/geb

Global Ecology and Biogeography, (Global Ecol. Biogeogr.)

(2008)

17

, 648–657

RESEARCHPAPER

Blackwell Publishing Ltd

Global patterns in fruiting seasons

Steven Ting, Stephen Hartley and K. C. Burns*

ABSTRACT

Aim

To identify geographical and climatic correlates of the timing of fruit productionin fleshy fruited plant communities.

Location

Global.

Methods

We searched the literature for studies documenting monthly variation inthe number of fleshy fruited species bearing ripe fruits in plant communities (i.e.fruit phenologies). From these data, we used circular vector algebra to characterizeseasonal peaks in fruit production (mean date, as an angle) and the length of fruitingseasons (as a circular standard deviation). Generalized linear models and circularcorrelations were used to assess whether latitudinal patterns in fruit phenologiescould be explained by variation in temperature, precipitation and actual evapotran-spiration (AET).

Results

Dates of peak fruit production and the length of fruiting seasons showedconsistent differences with latitude. Annual peaks in fruit production occurred 1 to3 months after the summer solstice at high-latitude sites in both hemispheres.Fruiting seasonality increased with latitude, indicating that fruiting seasons werelonger in the tropics and shorter toward the poles. AET was the best climaticpredictor of fruit phenologies. Annual peaks in fruit production were positivelyassociated with annual peaks in AET and temperature, while fruiting seasons wereshorter in areas with pronounced annual variation in AET.

Main conclusions

Global patterns in fruiting seasons are associated with globalvariation in climate. Across the globe, fleshy fruits are produced after annual periodsof elevated water–energy availability. Fruiting seasonality is also more pronouncedin areas with strongly seasonal water–energy inputs. Therefore, the timing of repro-duction in fleshy fruited plant communities appears to be determined, at least inpart, by spatial and temporal variation in energy supplies needed to subsidise plantreproduction.

Keywords

Circular statistics, climate, evapotranspiration, frugivory, latitude, phenology,

seed dispersal.

*Correspondence: K. C. Burns, School of Biological Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.E-mail: [email protected]

School of Biological Sciences, Victoria University

of Wellington, PO Box 600, Wellington, New

Zealand

INTRODUCTION

Latitudinal gradients in diversity are arguably the oldest and

most controversial patterns in ecology (Hawkins, 2001).

Previous work on latitudinal diversity gradients has typically

focused on taxonomic diversity, or species richness (see

Rosenzweig, 1995; Brown, 1999; Blackburn & Gaston, 2003).

Geographical clines in other measures of ecological diversity

remain poorly understood. For example, the number of species

bearing fruit varies through time in most plant communities and

seasonal changes in fruit availability have been documented in a

large number of plant communities inhabiting a wide range of

geographical locales. However, these data have yet to be synthe-

sized and global patterns in fruit phenologies are unresolved.

An understanding of global patterns in fruit phenologies is

important, given the link between plant phenology and global

climate change. Climate has long been recognized as a cue for

reproduction in fleshy fruited plants (Fisher, 1962). Global

Global patterns in fruit phenologies

© 2008 The Authors

Global Ecology and Biogeography

,

17

, 648–657, Journal compilation © 2008 Blackwell Publishing Ltd

649

temperatures have increased significantly over the last century

and the onset of flowering and fruiting in many plant species has

occurred progressively earlier during this time (Bradley

et al

.,

1999; McCarty, 2001; Walther

et al

., 2002; Chapman

et al

., 2005;

Franks

et al

., 2007; Sherry

et al

., 2007). Changes in the timing

and intensity of fruit set can have strong effects on ecosystem

structure and function. For example, in Barro Colorado Island,

droughts associated with El Niño–Southern Oscillation (ENSO)

events synchronize plant reproductive phenologies. Elevated

fruit production during ENSO events consumes plant energy

stores, which in turn generates future shortages in fruit supplies

and large-scale famine for fruit-eating animals in subsequent

years (Wright

et al

., 1999). Documenting global patterns in fruit

phenologies, and identifying the processes responsible for them,

is critical for an accurate understanding of how climate change

might affect ecosystem function.

Here, we test for global patterns in the phenology of fleshy

fruits. We collated studies from the literature that documented

annual cycles in the number of species producing fleshy fruits in

plant communities. These data were then used to test whether

peaks in fruit production and the length of fruiting seasons

vary consistently with latitude. We also evaluated the climatic

correlates of observed phenological patterns. We tested whether

annual peaks in fruit production are correlated with annual

peaks in precipitation, temperature and actual evapotranspiration

(AET, a proxy for productivity). We also tested whether the

length of fruiting seasons was associated with the annual dispersion

of precipitation, temperature and actual evapotranspiration.

METHODS

Fruit phenologies

Data used in the analyses were collated from previously pub-

lished studies (see Appendix S1 in Supplementary Material).

We searched the literature for studies that quantified annual

variation in the phenology of fleshy fruits. Studies were located

using web-based searches in Web of Science and Google Scholar

with the key words ‘fruit’, ‘fruiting’ or ‘phenology’, and from our

prior knowledge of the literature. We only included studies

reporting 12 or more consecutive months of phenological obser-

vations to promote unbiased comparisons between studies.

Those reporting less than one full year of observations, or

observations at intervals longer than 1 month, were excluded.

Monthly data from studies reporting more than 12 months of

observations were averaged between years prior to analyses.

We also limited our attention to studies reporting observations

on five or more fleshy fruited species.

Climatic data

We obtained historical monthly averages of three basic climate

variables: mean temperature (TMP), total precipitation (PPT)

and total AET. We chose these variables for several reasons.

First, temperature and precipitation have been suggested to

influence leaf, flower and fruit phenologies in regional studies

(van Schaik

et al

., 1993; Singh & Kushwaha, 2006). Second, AET

is a mechanistically based amalgamation of temperature, precip-

itation and solar radiation, which serves as a proxy for primary

productivity and the water–energy available for reproduction.

TMP and PPT were obtained from the International Panel for

Climate Change global data set, which contains climatic infor-

mation averaged over the years 1961–90 at a 0.5

°

geographical

resolution (New

et al

., 1999). We used the global data layer of

AET developed by Ahn & Tateishi (1994), downloaded from the

United Nations Environment Programme geodata repository

(http://www.grid.unep.ch/data/data.php, dataset gnv_183), also

presented at a 0.5

°

resolution.

Within-site description of annual fruiting and climatic cycles

For each study that fitted the criteria, we began with a simple

characterization of the within-site annual cycle of fruiting,

utilizing two metrics: (1) a measure of central tendency (mean

angle) which identifies the time of year when the greatest number

of species are expected to be in fruit and (2) a measure of disper-

sion (circular standard deviation) describing the degree to which

a variety of fruit species are available throughout the year. The

rationale behind these metrics, and their calculation, is described

in more detail below. The second stage (described later under Global-

scale Analyses) was to model the global trends and patterns of

these fruiting-cycle metrics with respect to latitude and climate.

The raw data consisted of monthly tallies of the number of

species bearing ripe fruits. According to the Gregorian calendar,

December is at the opposite end of the year from January. In

reality, however, there is no natural start or end point to the time

of year and thus quantities that vary on an annual cycle should be

analysed as ‘circular’ variables, where the time difference between

the eleventh and second month is recognized to be 3 months,

not 9 (Batschelet, 1981). With this in mind, the timing of annual

peaks in fruit production were quantified as mean angles (0–

360

°

, roughly equivalent to day of the year) and the dispersion

of fruiting (i.e. the lengths of the fruiting season) were quantified

as circular standard deviations.

Calculations followed Zar (1984) and proceeded as follows.

Each month

i

= (Jan, Feb, ... Dec) was associated with an angle

from the series

a

i

= (15

°

, 45

°

... 345

°

) and the corresponding

number of species bearing ripe fruits,

f

i

. The mean angle (date) of

fruit production,

µ

f

was calculated as:

(eqn 1)

where

µ

µ

f

f

Y

XX

Y

XX

arctan ( )

arctan ( )

=

>

= +

<

when

or

when

0

180 0

X

f a

n

ii

n

i

cos

= =∑

1

http://www.grid.unep.ch/data/data.php

S. Ting

et al.

© 2008 The Authors

650


,

17


n

is the number of groups (i.e. 12 months) and

α

i

is the angle in

degrees from zero.

The second metric, the temporal dispersion of the fruiting

season at each site, was calculated as the circular standard

deviation of monthly variation in the number of species bearing

ripe fruits (

s

):

(eqn 2)

where

r

c

is the relative length of the mean vector, from 0

→

1, corrected

for grouped data.

The two metrics may be visualized as a ‘mean vector’ (Fig. 1)

with a specified length and direction, and where vector length is

an inverse function of standard deviation (equation 2). In the

same way, mean angles and circular standard deviations were

calculated for each of the environmental variables on a site-

by-site basis. Monthly values for temperature were translated

into kelvin to avoid complications caused by negative values.

Global-scale analyses

Length of the fruiting season

First we examined the global relationship between latitude and

the length of fruiting season (measured as a circular standard

deviation), using a three-parameter Gaussian curve fit in Sigma-

Plot (2002). Next, a global-scale climatic prediction of the length

of fruiting seasons was determined using generalized least

squares (GLS) regression (Bailey & Gatrell, 1995; Pinheiro &

Bates, 2000). Sites were our sample unit, and the annual disper-

sion of fruiting measured as a circular standard deviation (

s

fru

)

was our response variable. The dispersion of TMP, PPT and AET,

also as circular standard deviations, formed our set of candidate

predictor variables. The circular standard deviations of TMP and

AET were ln-transformed to improve their normality and

correlations between variables were examined with Pearson’s

correlation test. Circular standard deviations vary from zero to

infinity and therefore can be used as continuous variables in

standard linear models.

GLS models are similar to general linear models, except that

they produce more realistic estimates of standard errors and

type-I errors in the presence of spatially correlated residuals

(Ver Hoef

et al

., 2001; Dormann

et al

., 2007). GLS models also

prevent clusters of sites from exerting undue (pseudo-replicated)

influence on estimates of beta coefficients, which may be an

important consideration if study sites are not uniformly distributed

in space (cf. Hawkins

et al

., 2007).

Y

f a

n

ii

n

i

sin

.= =∑

1

Figure 1 Graphical illustration of how estimates of central tendency (angle of the mean vector) and dispersion (circular standard deviation or vector length) relate to the monthly values of actual evapotranspiration (AET) (left) and the number of species bearing ripe fruits (right). Top, site 4 (Canada); bottom, site 44 (Uganda). In the text we refer to the mean angle as the ‘peak date of fruit production’ and the circular standard deviation as the ‘length of the fruiting season’.

s rc ln= −180

2π

rn

nX Yc

/

sin( / ) .= +

ππ

2 2


© 2008 The Authors


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17


651

To apply a GLS, the spatial correlation of residuals should be

suitably described by a function fitted to an empirical variogram

(Bailey & Gatrell, 1995). We compared the fit of three common

variogram models: the ‘spherical’, ‘Gaussian’ and ‘exponential’

functions as part of a full GLS model containing all three envi-

ronmental predictors and selected the variogram function that

yielded the lowest Akaike information criterion (AIC) (Burnham

& Anderson, 2002). This function was used in all subsequent

model comparisons involving all subsets of one and two environ-

mental predictors. Because of problems of collinearity between

environmental predictors we felt it informative to examine the

predictive capability of each variable on its own, as well as in

combination with one or more additional variables. For compar-

ison, we also fitted versions of the model specifications without

the ‘correction’ for spatial autocorrelation (Hawkins

et al

., 2007).

Finally, we nominated the model with the lowest overall AIC as

the minimum adequate model.

GLS models were fit using the ‘gls’ command of the ‘nlme’

package in R (

R

Development Core Team, 2007). This function

only accepts Cartesian coordinates, therefore spatial separations

were calculated as the linear or chord distance between sites

located on a spherical model of the globe. Chord distances are a

monotonic function of great circle distances, so the main differ-

ence between the two distance descriptors is their effect on the

range parameter of the variogram model.

Timing of peak fruit production

Geographical patterns in annual peaks of fruit production were

assessed with polynomial regression. More specifically, we

evaluated the fit of a sine curve to the relationship between lati-

tude (a bounded ‘linear’ variable) and the mean angle of monthly

fruit production (a ‘circular’ variable) using SigmaPlot (2002).

To determine the best climatic correlate for the timing of

peak fruit production, we used circular–circular correlations

(Batschelet, 1981). Methods for handling more than one circular

‘predictor’ at a time are poorly developed; hence separate corre-

lations were conducted between the mean angle of fruit produc-

tion and mean angles of TMP, PPT and AET. Several alternative

correlation tests have been proposed for circular variables. We

chose the correlation test of Jupp & Mardia (1980, 1981) as it

does not require a uniform distribution of angles in the input

variables.

To obtain a form of residuals appropriate to circular data, we

first calculated the angular difference or ‘lag’ between the time of

peak fruiting and the time at which each of the environmental

predictors peaks, on a site by site basis: lag =

µ

fru

–

µ

env

.

pred

.

The lags were expressed in the range –180

→

+180

°

and the

difference between the lag at site

j

and the overall mean lag

was taken as the residual (residual

j

= mean lag – lag

j

). To our

knowledge, no methods have been developed to account for

spatial autocorrelation in circular–circular correlations.

Nonetheless, we were able to examine the spatial structure of the

‘residuals’ by plotting the

absolute

angular difference (0–180

°

)

between the two residuals of all pairwise combinations of sites

against their separation distance (in this case calculated as a great

circle distance): low angular differences indicating a pair of sites

with similar (and potentially correlated) residuals in their

relationship between time of peak fruiting and the environmental

predictor. The presence of positive spatial autocorrelation in the

residuals was assessed visually by fitting locally weighted scatter-

plot smoothing (LOWESS) trend lines to the plot (with short-

range and medium-range smoother spans) and examining the

lines for a strong upward trend. Any attempt to calculate Moran’s

I

or a GLS model from these data would be misleading because

two residuals of –150

°

and +160

°

would be treated as being 310

°

apart, instead of only 50

°

. As a final test for spatial structure, we

compared the distribution of lags between different tropical and

temperate zones using Watson’s

U

2

test (Batschelet, 1981). Data

and R programming code for the above analyses are available

in Appendices S2 and S3.

RESULTS

Forty-eight studies met the inclusion criteria (see Appendix S1).

Figure 2 graphically illustrates several important components

of our data set, which covered a large geographical extent

(17 counties) and a wide range of climatic conditions.

Arrows represent (a) fruit phenologies, (b) AET and (c) the time

lag between the two. Long arrows in Fig. 2(a) represent the

concentration of fruiting into a short and well-defined period

of time, thus arrow length (

r

) is inversely related to the length

of the fruiting season (circular standard deviation,

s

; equation 2).

Arrow directionality (angle) illustrates the date of peak fruit

production.

Length of fruiting season

Latitudinal changes in the circular standard deviations of annual

fruiting cycles followed a Gaussian distribution (

R

2

= 0.491,

P

< 0.001). Fruiting seasons are shorter at high latitudes and

become progressively longer towards the equator (Fig. 3).

The length of fruiting seasons was also correlated with climatic

variables. When tested separately as single variable models, all

predictors were significantly associated with the circular standard

deviation of fruit production. The best single climatic predictor

was AET (

t

= 6.748,

P

< 0.001,

n

= 48), followed by temperature

(

t

= 6.523,

P

< 0.001,

n

= 48) and then precipitation (

t

= 3.100,

P

= 0.002,

n

= 48). AET and TMP were both strongly correlated

with absolute latitude, and with each other (all

r

> 0.87, all

P

<< 0.001,

n

= 48), while precipitation was not significantly

correlated with latitude or either of the other two environmental

predictors. The minimum adequate model retained terms for

AET and temperature, although only the term for AET was

significant (Table 1). The GLS models described above included

an exponential function to describe the spatial autocorrelation of

residuals (Appendix S4). The most parsimonious aspatial model

explained 55% of the variation in the length of fruiting seasons

using the two variables AET and TMP; it gave similar results

to the GLS though with lower coefficients, a slightly higher AIC

and reduced significance for the environmental predictors

(Appendix S4).

S. Ting

et al.

© 2008 The Authors

652


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Figure 2 Map illustrating the location of study sites and a summary of (a) each site’s community-level fruiting phenology, (b) seasonality of actual evapotranspiration (AET) and (c) the lag between the two (mean angle of fruiting–mean angle of AET). Numbers correspond to study sites listed in Appendix S1. Vectors (arrows) point to annual peaks in fruit production (or AET). Vector length (rc) is inversely related to the length of fruiting seasons (or availability of AET) and inversely related to circular standard deviation (s). The map projection is Plate Carée, lines of latitude indicate the Tropic of Cancer and Tropic of Capricorn.


© 2008 The Authors


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653

Timing of peak fruit production

Latitudinal changes in the mean angle of monthly fruit produc-

tion followed a sine curve (

R

2

= 0.410,

P

< 0.001). Annual peaks

in fruit production occurred approximately 1–3 months after the

summer solstice at high-latitude sites; between September and

October in the Northern Hemisphere and between March and

April in the Southern Hemisphere (Fig. 4). Annual peaks in fruit

production were also correlated with climatic variables. At a

global scale, the timing of peak fruiting showed similar strength

correlations with each of the environmental predictors (PPT,

TMP and AET) (Table 2a). As expected, the correlations were

weak (and non-significant) in tropical regions, but were stronger

in the temperate zones of both hemispheres (Table 2b–d). In

temperate zones, peak fruiting occurred 1–2 months after peaks

in temperature and 2–4 months after peaks in AET. Correlations

between fruiting and precipitation were not as strong and the

relationship between fruiting and precipitation was particularly

weak in the Northern Hemisphere (Table 2b,c). The timing of the

lags did not differ significantly between the three geographically

defined subregions (Watson

U

2

< 0.10,

P

> 0.10). However,

fruiting appeared to peak only 6 weeks after the summer solstice

in the Southern Hemisphere (

c

. 1 February), whereas in the

Northern Hemisphere the peak was approximately 10 weeks

after the summer solstice (

c

. 10 September) (Watson

U

2

= 0.154,

0.05 <

P <

0.10). We note that at a global scale, the seasonal

timing of peaks in AET, TMP and PPT were all highly correlated

with each other, particularly AET and temperature (PPT:TMP

r

2

= 0.333,

P

= 0.003; PPT:AET

r

2

= 0.488,

P

= 0.0001; TMP:AET

r

2

= 0.871,

P

<< 0.0001, all

n

= 48; circular correlation test of

Jupp & Mardia, 1980, 1981). Some evidence for spatial auto-

correlation was found in the angular residuals of the fruiting:

AET correlation, among temperate sites separated by less than

5000 km (Fig. S3 in Appendix S4), suggesting that the signifi-

cance values of the associated correlation test may need to be

viewed with caution.

DISCUSSION

The phenology of fleshy fruits shows consistent geographical

variation across the globe. Equatorial regions have prolonged

fruiting seasons and peak dates of fruit production that occur

throughout the year. Temperate regions have short fruiting

seasons that generally occur in late summer or autumn, 1–3

months after the summer solstice. As a result, annual variation in

fruit phenologies is more uniform in tropical regions, whereas

temperate regions are characterized by pronounced annual peaks

in production followed by periods of fruit scarcity.

Figure 3 Latitudinal variation in the length of fruiting seasons. The circular standard deviation of monthly variation in fruit production is shown on the x-axis. Degree latitude (Southern Hemisphere sites are negative) is shown on the y-axis. A Gaussian curve is fitted to the corresponding relationship (R2 = 0.491, P < 0.001, n = 48).

Table 1 Minimum adequate model (using spatial generalized least squares) for explaining the length of fruiting seasons (i.e. circular standard deviations in monthly distributions of the number of species bearing ripe fruits). Competing models are presented in Appendix S4.

Coefficient Std. Error t P-value

Intercept –593.27 264.29 –2.245 0.030

log(TMP) 94.45 63.98 1.476 0.147

log(AET) 43.43 19.11 2.273 0.028

Akaike information criterion (AIC) = 406.7, estimated using restricted

maximum likelihood (REML), total d.f. = 48, residual d.f. = 44. Spatial

correlation of residuals described using an exponential model with

range = 359.8 km and nugget = 0.00088. TMP, mean temperature;

AET, actual evapotranspiration.

Figure 4 Latitudinal variation in annual peaks in fruit production. The mean angle of monthly variation in fruit production is shown on the x-axis latitude. Months are used as x-axis labels instead of angles (i.e. Jan = 15°, Feb = 45°, ... Dec = 345°). Degree latitude (Southern Hemisphere sites are negative) is shown on the y-axis. A sine curve is fitted to the corresponding relationship (R2 = 0.410, P < 0.001, n = 48).

S. Ting et al.

© 2008 The Authors654 Global Ecology and Biogeography, 17, 648–657, Journal compilation © 2008 Blackwell Publishing Ltd

Variable patterns in dates of peak fruit production in the

tropics are quantitatively linked to the lack of fruiting seasonality

in the tropics. Estimates of a central tendency in circular distri-

butions are more difficult to establish when the underlying data

are uniformly distributed (i.e. platykurtic; see Batschelet, 1981).

Therefore, greater variability of peak dates of fruit production in

the tropics is a reflection of more uniform monthly distributions

in the number of species bearing ripe fruits. Conversely, latitudinal

patterns in peak dates of fruit production in temperate regions

are facilitated by strong seasonality in fruit production. Although

we analysed peak dates of fruit production and the length of

fruiting seasons separately, the two are best interpreted jointly,

given the quantitative connection between them.

Geographical patterns in fruit phenologies were associated

with geographical variation in climate. In general, AET was a better

predictor than temperature, which was a better predictor than

precipitation for both the timing and length of fruiting seasons.

This indicates that geographical locales with longer fruiting

seasons had more uniform monthly AET distributions, while

sites with less uniform AET distributions usually peaked in fruit

production 2–4 months after annual peaks in water–energy

inputs.

Although the mechanism underpinning the time lag between

annual peaks in AET and annual peaks in fruit production is

unclear, it could result from time lags in the process of plant

reproduction. Plant reproduction is a sequential process. Flower

development, pollen dispersal and fruit maturation do not occur

simultaneously, and each requires a substantial period of time to

take place. Time lags in the development of fleshy fruits resulting

from the earlier processes of plant reproduction can have

substantial influence on broad-scale patterns in plant–animal

interactions (Burns, 2002). If the process of plant reproduction is

initiated by annual increases in water–energy inputs, a time lag

between peak AET and fruit maturation would be generated

by the developmental time required for flower and fruit

development. van Schaik et al.’s (1993) global analysis of flower

phenologies supports this interpretation. They found that peak

dates in flower production occurred concomitantly with annual

periods of increased solar radiation.

Previous studies on smaller spatial scales have linked

community-level phenological events to precipitation (Singh &

Kushwaha, 2005; Boulter et al., 2006) and/or temperature

(Williams-Linera, 1997; Berlin et al., 2000). The discrepancy

between these results and those reported here could be explained

by several factors. First, most proxies of productivity (including

AET) are derived from both temperature and precipitation

measurements; hot, wet locales are more productive than cooler,

drier locales. Previous studies on the relationship between plant

reproductive phenologies and climate have often focused on

tropical regions that experience annual periods of drought. In

these locales, the strongest limitation on productivity is likely to

be water stress. Therefore, any relationship between phenologies

and precipitation in tropical dry forests is likely to reflect a

similar relationship with productivity. A second explanation

might be that the processes occurring on smaller, regional scales

may be different from the processes operating at the global scale

(see Blackburn & Gaston, 2002; Aukema, 2004; Burns, 2004;

García & Ortiz-Pulido, 2004).

Several aspects of our study warrant a cautious approach

to interpreting the results. Spatial autocorrelation was not

Table 2 Correlation between the peak date of fruit production (mean lag) and the peak concentration of three environmental variables. P-value determined from nr2 distributed as χ2 with 4 d.f.; r 2 may vary from 0 to 2 (Jupp & Mardia, 1980, 1981).

Environmental predictor r 2 P Timing of fruit production (mean lags)

(a) Global data set, n = 48

Precipitation 0.298** 0.006 4 months before peak PPT

Temperature 0.300** 0.006 2 months after peak TMP

AET 0.330** 0.003 3.3 months after peak AET

(b) Northern temperate, n = 12

Precipitation 0.099 NS 5 months before peak PPT

Temperature 0.639 0.104 2 months after peak TMP

AET 0.757+ 0.059 4 months after peak AET

Calendar date 10 September: 2.6 months after summer solstice

(c) Southern temperate, n = 15

Precipitation 0.772* 0.021 3.3 months before peak PPT

Temperature 0.949** 0.007 1.3 months after peak TMP

AET 0.917** 0.008 2 months after peak AET

Calendar date 1 February: 1.3 months after summer solstice

(d) Tropical, n = 21

Precipitation 0.264 NS 4.7 months before peak PPT

Temperature 0.144 NS 1.5 months after peak TMP

AET 0.051 NS 3.3 months after peak AET

‘NS’ = P > 0.20, +P < 0.1, *P < 0.05, **P < 0.01.AET, actual evapotranspiration; PPT, total precipitation; TMP, mean temperature.


© 2008 The Authors Global Ecology and Biogeography, 17, 648–657, Journal compilation © 2008 Blackwell Publishing Ltd 655

accounted for in analyses of circular variables. There was also a

difference in the scale at which phenological and climatic data

were gathered. Phenological data were collected at particular

points in space that were usually less than several square kilometres

in area, whereas climatic data were estimated for 0.5° grid cells.

We only assessed relationships between phenological parameters,

latitude and climate. However, many other variables, including

soil conditions, elevation and anthropogenic disturbance, may

also be important. The potential effects of plant species richness

were also not assessed. Global patterns in plant species richness

are correlated with the same climatic variables that were used in

our analyses, complicating tests for causal relationships between

variables. Identifying causal relationships between plant species

richness and fruit phenologies represents a major challenge to

future work.

Although our analyses suggest climate is a major driver of

global patterns in fruit phenologies, biotic interactions could

also be important. Animal fruit consumers are a critical compo-

nent of successful reproduction in fleshy fruited plant species

(Herrera, 2002). Therefore, the actions of seed dispersers could

profoundly influence the timing of fruit production, both

ecologically (habitat shaping) or evolutionarily (natural

selection; see Burns, 2004). Tropical regions typically house large

assemblages of fruit-eating animals, many of which are non-

migratory. Protracted fruiting seasons in the tropics might

therefore facilitate seed dispersal by sedentary frugivores.

Conversely, many temperate regions receive massive annual

influxes of migratory fruit-eating birds (Herrera, 1998; Teller’a &

Pérez-Tris, 2003, 2007). Shorter fruiting seasons, which coincide

with autumn migrations of avian seed dispersers that winter

in the tropics, may facilitate the spread of seeds over larger

distances (Thompson & Willson, 1979). Our analyses did not

address this possibility. Identifying the relative influence of

biotic and abiotic processes affecting global patterns in the

phenology of fleshy fruits presents another interesting challenge

to future research.

In conclusion, our results showed consistent geographical

patterns in the phenology of fleshy fruits in plant communities.

High-latitude sites have strong annual peaks in fruit production,

typically in late summer or autumn, in both hemispheres. Tropical

sites have less pronounced annual peaks in fruit production that

occur throughout the year. Fruit phenologies were more strongly

associated with AET than other climatic variables, suggesting

that the timing of reproduction in most plant communities is

initiated by the combination of elevated periods of solar energy

and water availability. A better understanding of global patterns

in plant reproduction hinges on future research into how

pollinators and seed dispersers might interact with climate to

determine global phenological cycles.

ACKNOWLEDGEMENTS

We would like to thank Daniel Burket, Angela Moles, Shirley

Pledger, Hannah Sutton, David Ting, Sue Ting and Gemma

Bowker-Wright for helpful advice and Victoria University of

Wellington for funding.

REFERENCES

Ahn, C.H. & Tateishi, R. (1994) Development of global land

surface evapotranspiration and water balance data sets. Journal

of the Japan Society Photogrammetry and Remote Sensing, 33,

48–61.

Aukema, J.E. (2004) Distribution and dispersal of desert mistletoe

is scale-dependent, hierarchically nested. Ecography, 27, 137–144.

Bailey, T.C. & Gatrell, A.C. (1995) Interactive spatial data analysis.

Longman Scientific and Technical, Harlow, UK.

Batschelet, E. (1981) Circular statistics in biology. Academic Press,

New York.

Berlin, K.E., Pratt, T.K., Simon, J.C., Kowalsky, J.R. & Hatfield,

J.S. (2000) Plant phenology in a cloud forest on the island of

Maui, Hawaii. Biotropica, 32, 90–99.

Blackburn, T.M. & Gaston, K.J. (2002) Scale in macroecology.

Global Ecology and Biogeography, 11, 185–189.

Blackburn, T.M. & Gaston, K.J. (eds) (2003) Macroecology:

concepts and consequences. Blackwell Publishing, Oxford, UK.

Boulter, S.L., Kitching, R.L. & Howlett, B.G. (2006) Family,

visitors and the weather: patterns of flowering in tropical

rain forests of northern Australia. Journal of Ecology, 94, 369–

382.

Bradley, N.L., Leopold, A.C., Ross, J. & Huffaker, W. (1999)

Phenological changes reflect climate change in Wisconsin.

Proceedings of the National Academy of Sciences USA, 96, 9701–

9704.

Brown, J.H. (1999) Macroecology: progress and prospects.

Oikos, 87, 3–14.

Burnham, K.P. & Anderson, D.R. (2002) Model selection and

multimodel inference: a practical information-theoretic approach.

Springer-Verlag, New York, USA.

Burns, K.C. (2002) Seed dispersal facilitation and geographic

consistency in bird–fruit abundance patterns. Global Ecology

and Biogeography, 11, 253–259.

Burns, K.C. (2004) Scale and macroecological patterns in seed

dispersal mutualisms. Global Ecology and Biogeography, 13,

289–293.

Chapman, C.A., Chapman, L.J., Struhsaker, T.T., Zanne, A.E.,

Clark, C.J. & Poulsen, J.R. (2005) A long-term evaluation of

fruiting phenology: importance of climate change. Journal of

Tropical Ecology, 21, 31–45.

Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R.,

Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling,

W.D., Kühn, I., Ohlemüller, R., Peres-Neto, P.R., Reineking, B.,

Schröder, B., Schurr, F.M. & Wilson, R. (2007) Methods to

account for spatial autocorrelation in the analysis of species

distributional data: a review. Ecography, 30, 609–628.

Fisher, D.V. (1962) Heat units and number of days required to

mature some pome and stone fruits in various areas of North

America. Proceedings of the American Society for Horticultural

Science, 80, 114–124.

Franks, S.J., Sim, S. & Weis, A.E. (2007) Rapid evolution of

flowering time by an annual plant in response to a climate

fluctuation. Proceedings of the National Academy of Sciences

USA, 104, 1278–1282.

S. Ting et al.

© 2008 The Authors656 Global Ecology and Biogeography, 17, 648–657, Journal compilation © 2008 Blackwell Publishing Ltd

García, D. & Ortiz-Pulido, R. (2004) Patterns of resource

tracking by avian frugivores at multiple spatial scales: two case

studies on discordance among scales. Ecography, 27, 187–196.

Hawkins, B.A. (2001) Ecology’s oldest pattern? Trends in Ecology

and Evolution, 16, 470.

Hawkins, B.A., Diniz-Filho, J.A.F., Bini, L.M., De Marco, P. &

Blackburn, T.M. (2007) Red herrings revisited: spatial auto-

correlation and parameter estimation in geographical ecology.

Ecography, 30, 375–384.

Herrera, C.M. (1998) Long-term dynamics of Mediterranean

frugivorous birds and fleshy-fruits: a 12-year study. Ecological

Monographs, 68, 511–538.

Herrera, C.M. (2002) Seed dispersal by vertebrates. Plant–animal

interactions: an evolutionary approach (ed. by C.M. Herrera

and O. Pellmyr), pp. 185–208. Blackwell Science, Oxford.

Jupp, P.E. & Mardia, K.V. (1980) A general correlation coefficient

for directional data and related regression problems. Biometrika,

67, 163–173.

Jupp, P.E. & Mardia, K.V. (1981) Amendments and corrections: a

general correlation coefficient for directional data and related

regression problems. Biometrika, 68, 738.

McCarty, J.P. (2001) Ecological consequences of recent climate

change. Conservation Biology, 15, 320–331.

New, M., Hulme, M. & Jones, P. (1999) Representing twentieth-

century space–time climate variability. Part I: development of

a 1961–90 mean monthly terrestrial climatology. Journal of

Climate, 12, 829–856.

Pinheiro, J.C. & Bates, D.M. (2000) Mixed effects models in S and

S-Plus. Springer, New York.

Rosenzweig, M.L. (1995) Species diversity in space and time.

Cambridge University Press, Cambridge, UK.

R Development Core Team (2007). R: a language and environ-

ment for statistical computing. R Foundation for Statistical

Computing, Vienna, Austria. http://www.R-project.org.

van Schaik, C.P., Terborgh, J.W. & Wright, S.J. (1993) The

phenology of tropical forests: adaptive significance and

consequences for primary consumers. Annual Review of

Ecology and Systematics, 24, 353–377.

Sherry, R.A., Zhou, X., Gu, S., Arnone, J.A., Schimel, D.S.,

Verburg, P.S., Wallace, L.L. & Luo, Y. (2007) Divergence of

reproductive phenology under climate warming. Proceedings

of the National Academy of Sciences USA, 104, 198–202.

SigmaPlot (2002) Release 8.02 for Windows. SPSS Inc., Chicago,

USA.

Singh, K.P. & Kushwaha, C.P. (2006) Diversity of flowering and

fruiting phenology of trees in a tropical deciduous forest in

India. Annals of Botany, 97, 265–276.

Tellería, J.L. & Pérez-Tris, J. (2003) Seasonal distribution of a

migratory bird: effects of local and regional resource tracking.

Journal of Biogeography, 30, 1583–1591.

Tellería, J.L. & Pérez-Tris, J. (2007) Habitat effects on resource

tracking ability: do wintering blackcaps Sylvia atricapilla track

fruit availability? Ibis, 149, 18–25.

Thompson, J.N. & Willson, M.F. (1979) Evolution of temperate

fruit/bird interactions: phenological strategies. Evolution, 33,

973–982.

Ver Hoef, J.M., Cressie, N., Fisher, R.N. & Case, T.J. (2001)

Uncertainty and spatial linear models for ecological data.

Spatial uncertainty in ecology: implications for remote sensing

and GIS applications (ed. by C.T. Hunsaker, M.F. Goodchild,

M.A. Friedl and T.J. Case), pp. 214–237. Springer-Verlag, New

York.

Walther, G.R., Post, E., Convey, P., Menzel, A., Parmesank, C.,

Beebee, T.J.C., Fromentin, J.M., Hoegh-Guldberg, O. &

Bairlein, F. (2002) Ecological responses to recent climate

change. Nature, 416, 389–395.

Williams-Linera, G. (1997) Phenology of deciduous and

broadleaved-evergreen tree species in a Mexican tropical lower

montane forest. Global Ecology and Biogeography Letters, 6,

115–127.

Wright, S.J., Carrasco, C., Calderón, O. & Paton, S. (1999) The El

Niño Southern Oscillation, variable fruit production, and

famine in a tropical forest. Ecology, 80, 1632–1647.

Zar, J.H. (1984) Biostatistical analysis. Prentice Hall, Englewood

Cliffs, NJ.

SUPPLEMENTARY MATERIAL

The following supplementary material is available for this article:

Appendix S1 Published studies used in analyses, their biblio-

graphic references and our circular description of phenologies.

Appendix S2 Monthly fruiting data and climatic conditions.

Appendix S3 Programming code for the statistical analyses con-

ducted in R.

Appendix S4 Semivariogram analysis of model residuals, and

non-spatial equivalents to the GLS models.

This material is available as part of the online article from:

http://www.blackwell-synergy.com/doi/abs/10.1111/

j.1466-8238.2008.00408.x

(This link will take you to the article abstract).

Please note: Blackwell Publishing is not responsible for the con-

tent or functionality of any supplementary materials supplied by

the authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.

http://www.R-project.org

http://www.blackwell-synergy.com/doi/abs/10.1111/j.1466-8238.2008.00408.x


© 2008 The Authors Global Ecology and Biogeography, 17, 648–657, Journal compilation © 2008 Blackwell Publishing Ltd 657

Editor: José Alexandre F. Diniz-Filho

BIOSKETCHES

Steven Ting is an honours student at Victoria University of Wellington who is interested in macroecological patterns in plant–

animal interactions.

Stephen Hartley is an assistant professor at Victoria University of Wellington. He is interested in population and community

ecology, conservation biology, invasive species and bioclimatic modelling, with a focus on spatial analysis. Current research topics

include the spread of Argentine ants, patterns of rarity in plants, insect forging behaviour and coastal sand dune communities.

K. C. Burns is an assistant professor at Victoria University of Wellington. His research interests include bird behaviour,

community assembly rules, plant–animal interactions and plant morphology.

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Global patterns in fruiting seasons