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Biological indicators of climate change: evidence
from long-term flowering records of plants along
the Victorian coast, Australia
Libby Rumpff
A,C
, Fiona Coates
B
and John W. Morgan
A
A
Department of Botany, La Trobe University, Bundoora, Vic. 3086, Australia.
B
Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment,
Heidelberg, Vic. 3084, Australia.
C
Corresponding author. School of Botany, University of Melbourne, Parkville, Vic. 3010, Australia.
Email: lrumpff@unimelb.edu.au
Abstract. We investigate the utility of using historical data sources to track changes in flowering time of coastal species in
south-eastern Australia in response to recent climate warming. Studies of this nature in the southern hemisphere are rare,
mainly because of a paucity of long-term data sources. Despite this, we found there is considerable potential to utilise existing
data sourced from herbaria collections and field naturalists’notes and diaries to identify native plant species suitable
as biological indicators of climate change. Of 101 candidate species investigated in the present study, eight were identified as
showing a general trend towards earlier flowering over time, indicating a correlation with increasing temperatures. There was
some evidence to suggest that species which flower in spring and summer may be more sensitive to changes in temperature.
There was a high level of uncertainty regarding the detection of trends, which was a function of the accessibility, abundance
and accuracy of the various data sources. However, this uncertainty could be resolved in future studies by combining the
datasets from the present study with field monitoring of phenological cycles in climatically different locations. Data held by
community groups could be made more accessible if there was a concerted effort to fund collation and digitisation of these
records. This might best be achieved by working with community groups, and facilitated through the recent establishment of
a community phenological observation database in Australia.
Introduction
It has long been recognised that phenological cycles are often
strongly influenced by changes in climate, particularly
temperature and rainfall (de Groot et al.1995; Hughes 2000;
Menzel 2002; Chambers 2006; Penuelas and Filella 2007;
Rosenzweig et al.2008). Attention has been recently directed
towards the challenging task of identifying species which might
indicate how plants are responding to a warmer world (Menzel
et al.2001; Menzel 2002; Walther et al.2002; Dahlgren et al.
2007; Miller-Rushing and Primack 2008; Gallagher et al.2009).
Biological indicator species are those which ‘respond predictably
and sensitively, in ways that are easily observed and quantified’
(Hughes 2003b: 53), and can act as surrogates for understanding
other species’response to climate change in the same ecosystem.
Theoretically, species that respond primarily to temperature
and are less influenced by biotic, edaphic and other climatic
interactions (de Groot et al.1995; Menzel 2002) will be the most
suitable candidates. Other ideal candidates include those species
for which there are a large number of prior records collected
over long time frames, to provide a baseline dataset for future
comparison (Gallagher et al.2009). Species that are also easy to
observe and identify enable cost-effective and repeatable
monitoring (de Groot et al.1995).
A recent method used to investigate environmental change at
multiple spatial and temporal scales is examination of scientific
collections held in herbaria and museums (Anonymous 1998;
Primack et al.2004; Bolmgren and Lonnberg 2005; Coleman
and Brawley 2005; Sparks 2007; Gallagher et al.2009). Many
herbarium specimens date back decades, or even centuries, and
can provide valuable information on species relationships,
distribution and ecology (Anonymous 1998). Herbarium data
are one of the most reliable sources of historical data, are generally
abundant and accessible, and are checked regularly for their
scientific accuracy by specialists (Ponder et al.2001; Delisle
et al.2003). Thus, they have potential to provide insight
into broad-scale, long-term trends that would otherwise be
unachievable because of time (and cost) constraints (Bolmgren
and Lonnberg 2005).
The relevance of such collections to wider ecological,
conservation and biological studies has, until recently, been
largely under-appreciated in Australia (Gallagher et al.2009).
Studies of global changes in phenological events in response to
the recent climate change have come predominantly from the
northern hemisphere (see Walther et al.2002; Parmesan and
Yohe 2003; Root et al.2003; Parmesan 2007; Miller-Rushing and
Primack 2008; Rosenzweig et al.2008). Even then, continuous
CSIRO PUBLISHING
www.publish.csiro.au/journals/ajb Australian Journal of Botany, 2010, 58, 428–439
CSIRO 2010 10.1071/BT10053 0067-1924/10/060428
and systematically collected long-term (decades to centuries)
phenological datasets for a variety of ecosystems are rare
(Ledneva et al.2004) and the majority of studies have relied
on shorter-term local data collections (Root et al.2003; Ledneva
et al.2004). These limitations are likely to be exacerbated in
Australia because there has been a relatively short time since
European settlement to establish quality long-term datasets.
Nonetheless, there is a growing number of studies
investigating the relationship between phenological events and
climatic variability in Australia (e.g. Law et al.2000; Keatley
et al.2002; Hovenden et al.2007,2008; Keatley and Hudson
2007; Jarrad et al.2008; Gallagher et al.2009). For instance,
Gallagher et al.(2009) were able to demonstrate that herbarium
records collected for Australian alpine plants could be used to
identify indicator species. Plants in alpine and subalpine areas are
likely to respond sensitively to changes in seasonal temperatures
(Gallagher et al.2009) and are generally considered to be most
at risk of rising temperatures (Pauli et al.1996; Arft et al.1999).
They have thus attracted a good deal of attention (e.g. Molau et al.
2005; Kudo and Hirao 2006; Gallagher et al.2009). However,
extending studies of phenological patterns to other ecosystems
may provide insights into the effects of a range of other
environmental influences and identify spatial patterns across
landscapes (Primack et al.2009).
Although herbaria are ideal for gathering robust datasets for
phenological studies (Gallagher et al.2009), the availability of
data to infer changes in species behaviour over time extends
beyond the realm of scientific collections and publications. For
centuries people have been collecting and recording information
regarding the timing of life-cycle changes in animals and plants
(Primack et al.2009). For instance, recording phenological
observations is a very popular past-time for amateur field
naturalists (Sparks and Carey 1995). Some of these data are
submitted to herbaria, and there is a potentially much larger source
of information held within the community by individuals and
organisations (Sparks and Carey 1995; Menzel et al.2001;
Vasseur et al.2001; Whitfield 2001; Ledneva et al.2004;
Miller-Rushing and Primack 2008). Scientists are now using
these data sources more frequently (Sparks and Carey 1995;
Whitfield 2001; Ledneva et al.2004). Careful screening of
data is required because there can be problems with the
reliability and accuracy of the data source (Whitfield 2001;
Ledneva et al.2004). However, the utility of these data
sources may be improved when combined with field datasets
or scientific studies of species’phenological cycles (Primack et al.
2004; Miller-Rushing et al.2006; Dahlgren et al.2007; Gallagher
et al.2009). By collating all possible sources of data, consistency
in long-term records and the potential for detecting phenological
changes can be improved significantly (Kagata and Yamamura
2006; Miller-Rushing et al.2006).
Recent Australian studies have provided evidence for
variation in plant phenology with temperature increases
(Keatley and Hudson 2007; Gallagher et al.2009). However,
such studies are uncommon, and it remains to be seen whether
irregularities in data collection can be overcome to find indicator
species which are capable of extrapolating a clear climate-change
signal over multiple Australian ecosystems. The present study
examines the potential for using phenological records of the
coastal flora in south-eastern Australia held within herbaria
and the wider community to detect evidence of recent climate
change. This information will contribute to an understanding of
global trends affecting biodiversity conservation and will provide
valuable baseline data for future scientific and community efforts.
The main aims of the study were to
(1) collate the historical data on flowering times of coastal flora in
south-eastern Australia;
(2) determine which species or functional groups may be most
sensitive to climate change; and
(3) identify additional sources of biological data outside of
herbaria that warrant further investigation.
Materials and methods
Site selection and analysis of climate change
The study area encompassed the Victorian coastline in south-
eastern Australia (Fig. 1). Coastal vegetation communities were
BENDIGO
BALLARAT
Horsham
Hamilton
Portland Warrnambool
MELBOURNE Baimsdale
TRARALGON
N
0 60 km
GEELONG
Fig. 1. Map of the Victorian coastline.
Biological indicators of climate change Australian Journal of Botany 429
chosen to test whether indicator species could be identified at
sea level in comparison to the more commonly studied high-
altitude regions (Molau et al.2005; Kudo and Hirao 2006;
Gallagher et al.2009). Furthermore, coastal areas typically
have a long history of occupation and, hence, botanical data
collection. We also assumed there was a lower chance of natural
phenological variation due to the edaphic and altitudinal
similarities between most coastal areas, and the moderating
influence of the sea on temperature.
The coastal species list
The first step was to obtain a list of plant species on which to base
searches of herbarium collections and records held by field
naturalists. The entire coastal area of the state of Victoria was
initially grouped into nine (pre-defined) bioregions (sensu
Department of Sustainability and Environment 2007), and then
into 20 different coastal vegetation types (see Appendix 1), each
of which was associated with a published list of common species
(Department of Sustainability and Environment 2007). The
distributions of 297 common species were mapped using the
Flora Information System (FIS) (Viridans Biological Databases
2006) held by the Department of Sustainability and Environment
(DSE), which contain over 2 million records of species
occurrences. Species that were not predominantly restricted to
the coast (i.e. <80% of their area of occupancy) were omitted. It
was anticipated that this list would represent a group of common,
coastally restricted species. To obtain a more extensive list of
species, we searched the FIS for locally restricted coastal species
and added another 71 species as candidates (Fig. 2). This would
ensure that the analysis would not overlook rare or locally
restricted species. This resulted in a total of 101 candidate
species for searching the various data sources (see Appendix 2).
These species were then classified broadly according to their
spatial distribution in Victoria as ‘widespread’(if they occupied
more than two-thirds of the coastline), ‘intermediate’(one- to
two-thirds of the coastline) or ‘local’(less than one-third of the
coastline). Plant nomencalture follows Walsh and Stajsic (2007).
Data sources
Mean monthly temperature and precipitation data were
obtained from the Australian Bureau of Meteorology (Bureau
of Meteorology 2007) from the following four Victorian coastal
weather stations for which a complete sequence of long-term data
was available: Cape Otway Lighthouse, Melbourne Regional
Office, Wilsons Promontory Lighthouse and Gabo Island
Lighthouse.
The sources of flowering time used in the study included
herbarium records, FIS data held by the DSE and records of
observations by individuals and community organisations.
Herbarium data were obtained initially from the National
Herbarium of Victoria’s MELISR database. Records that did
not have at least month and year recorded at the time of collection
were omitted, as were records that did not state a coastal location.
Each record was then evaluated according to the accompanying
phenological information. Data were also obtained from the
University of Melbourne and La Trobe University herbaria,
following a similar procedure. However, records were not kept
on a database, so specimens were individually checked for date,
location and phenological status. Duplicate records were omitted.
Searches of the FIS database (Viridans Biological Databases
2006) were made to obtain additional records of species.
Victorian coastal community or field naturalist groups were
contacted for access to phenological information collected by
their members. The organisations consulted were ANGAIR Inc.
(Anglesea field naturalist group), Traralgon Field Naturalists,
Bairnsdale and District Field Naturalists and the South Gippsland
Conservation Society. Additional long-term data records were
collected from a range of other individuals. Some data were also
collected from newsletters of ANGAIR Inc. As above, records
were evaluated according to the suitability of the information
provided on date and phenological information. We were unable
to account for any bias resulting from differences in sampling
methodology, because this information was not available.
However, the information collected from community groups
accounted only for a small portion of the overall data (5%), so
it is assumed the impact of any bias was minimal.
Investigation of phenological trends
by using herbarium records
To investigate the sensitivity of species’flowering times in
relation to climate change, records were sorted according to
the date of collection. These dates were converted to Julian
Victorian
coastline
Bioregion
(9)
Coastal vegetation
communities (20)
Common
species (297)
Common coastally
restricted species
(30)
‘Other’ coastally
restricted species
(71)
Candidate list of
species for herbarium
searches (101)
FIS
FIS
Fig. 2. Illustration of the selection process for determining candidate
species for herbarium searches. ‘FIS’refers to map based searches of the
Flora Information System (Viridans Biological Databases 2006) to obtain
information of the distribution of species.
430 Australian Journal of Botany L. Rumpff et al.
dates (i.e. the numeric day of the year, ranging from 1 to 365 or
366). Records that included only a month and year were
automatically assigned the Julian date equal to the 15th day of
the month (which accounted for ~22% of the herbarium data). The
starting Julian date was later adjusted for each species according
to the estimated time of peak flowering (from the FIS data). For
instance, the first Julian day for species that flowered in spring was
calculated from the start of June or July. When records occurred in
the same year, only the record with the earliest Julian date was
retained (Gallagher et al.2009).
General trends in the data were assessed by plotting flowering,
fruiting or budding dates against time (i.e. year and Julian day).
The data were examined for both trends towards observed
earlier flowering dates over time (which would be indicative of
species responding to warmer temperatures earlier in the season,
a key prediction under global-warming scenarios), and for
‘unexpected’trends towards later flowering (Parmesan and
Yohe 2003; Root et al.2003; Parmesan 2007). Additional data
were obtained from other sources (FIS and community data) and
collated with the herbarium data for further analysis.
Although it might be expected that species with shorter
flowering periods are likely to be more useful as indicator
species (Gallagher et al.2009), we did not initially place any
flowering-time restriction on our selections. First, detection of a
trend is also a function of the number of samples and may be
evident regardless of the length of the flowering time. Second,
there may be variation in the length of flowering times with
location. It was anticipated that this could be determined at a later
stage in the analysis.
Statistical analysis
To examine the strength and consistency of climatic trends,
linear regressions of trends in mean monthly minimum and
maximum temperatures over time were calculated for summer
(December–March) and winter (June–August) periods. The same
analysis was repeated for total summer and winter precipitation
data. Regressions of climate data against time were calculated
for the periods 1910–2006 and 1950–2006 to examine whether
warming trends were strengthening over time (Pittock 2003).
Results were considered statistically significant if P<0.05.
Analyses were carried out using the statistical program R
(R Development Core Team 2006).
General linear regression was used to evaluate the statistical
strength and variability around any detected trend in the
phenological data; however, the trend was only broadly
compared with the trends detected in the climatic data. Again,
results were considered statistically significant if P<0.05,
although we also report species that indicated a trend, but had
P>0.05 (in the event further data become available). Gallagher
et al.(2009) examined the relationship between the date of
flowering observations and the mean annual temperature in
the year of collection. However, the nature of the phenological
Table 1. Trends in mean summer and winter minimum and maximum temperatures (8C year
–1
), and mean summer and winter
precipitation (mm year
–1
) for the periods 1910–2006 and 1950–2006
The trend data are the slopes of linear regression models, and represent the positive (+) or negative (–) change in 8C year
–1
or mm year
–1
. Values
highlighted in bold denote significant (P<0.05) linear trends. To represent the variability along the Victorian coastline, the data were grouped from
four stations: Cape Otway (Station No. 090015, 38.868S, 143.518E, 82 m asl), Gabo Island (Station No. 084016, 37.578S, 149.928E, 15 m asl),
Melbourne (Station No. 086071, 37.818S, 144.978E, 31 m asl) and Wilson’s Promontory (Station No. 085096, 39.138S, 146.428E, 95 m asl)
Site Climate variable 1910–2006 1950–2006
Trend PTrend P
Cape Otway lighthouse Summer max. temperature +0.009 0.004 +0.005 0.471
Summer min. temperature +0.005 0.059 +0.018 0.003
Winter max. temperature +0.004 0.015 +0.009 0.011
Winter min. temperature +0.003 0.234 +0.018 <0.001
Summer precipitation +0.077 0.183 +0.113 0.362
Winter precipitation +0.245 0.009 +0.002 0.992
Gabo Island lighthouse Summer max. temperature +0.014 <0.001 +0.030 <0.001
Summer min. temperature +0.002 0.431 +0.002 0.554
Winter max. temperature +0.008 <0.001 +0.029 <0.001
Winter min. temperature +0.001 0.402 –0.003 0.340
Summer precipitation +0.003 0.978 –0.094 0.699
Winter precipitation –0.136 0.252 –0.736 0.007
Melbourne Summer max. temperature +0.008 0.025 +0.010 0.242
Summer min. temperature +0.021 <0.001 +0.032 <0.001
Winter max. temperature +0.012 <0.001 +0.022 <0.001
Winter min. temperature +0.018 <0.001 +0.030 <0.001
Summer precipitation –0.012 0.886 –0.055 0.765
Winter precipitation –0.038 0.439 –0.188 0.090
Wilsons Promontory lighthouse Summer max. temperature +0.009 0.001 +0.009 0.033
Summer min. temperature +0.008 <0.001 +0.024 <0.001
Winter max. temperature +0.003 0.167 +0.019 <0.001
Winter min. temperature +0.004 0.095 +0.030 <0.001
Summer precipitation +0.016 0.835 +0.082 0.626
Winter precipitation +0.286 0.024 +0.247 0.425
Biological indicators of climate change Australian Journal of Botany 431
data in our study meant that first flowering dates were not
available, so it could not be statistically determined whether
warmer years correlated with earlier flowering.
Trends in flowering were examined according to the growth
form, timing and duration of flowering. Numerous studies have
indicated that species that respond to environmental cues in spring
rather than autumn or summer are more useful for tracking
change (Hughes 2000; Menzel et al.2001); therefore, species
were grouped according to flowering season.
Results and discussion
Phenological and climatic trends
Temperatures have risen along the coastline in south-eastern
Australia since the early 1900s. Although the magnitude of the
trend differs among locations, it is evident that the trend has been
more pronounced since the 1950s, with an average increase in
temperature of 0.77–1.13C over the last 50–60 years (Table 1,
Fig. 3). This is consistent with trends across Victoria for the same
period (Bureau of Meteorology 2010), and has also been
documented in other ecosystems across Australia (see Pittock
2003; Gallagher et al.2009). From 1950, the strongest changes
were detected in minimum temperatures, with the exception
of records from the Gabo Island Lighthouse station, where the
change in maximum temperatures was greatest. During this
period, the average increase in summer and winter mean
minimum temperatures, and winter mean maximum
temperatures ranged from 1.07 to 1.13C. The warming trend
detected in summer mean maximum temperatures was of
smaller magnitude (0.77C year
–1
) and more variable. Over the
entire period (1910–2006), there were significant increases in
winter precipitation at two sites.
Data sourced from herbaria and the general community
demonstrated that the historical timing of flowering in some
coastal species coincided with the increase in average
temperatures, with the majority of earliest flowering times
recorded after 1950. However, this period also contained a
higher number of records. Monitoring the flowering times of
these species may be a useful indicator of a future climatic
variability, and the existing data provide a useful baseline for
which to compare future observations. However, identifying a
large number of suitable candidates for following climate change
for the Victorian coastline was difficult because the nature of the
data meant that any statistically significant trends in flowering
dates were generally difficult to detect.
Of the 101 species investigated, only four rare or threatened
species showed a statistically significant (P<0.05) general trend
towards earlier flowering dates with time (Fig. 4). The strongest
Summer
maximum
18
Temperature (°C)Rainfall (mm)
20
22
24
26
28
30
1950 1960 1970 1980 1990 2000
1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000
1950 1960 1970 1980 1990 2000
1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000
Summer
minimum
10
11
12
13
14
15
16
17
18
Year
0
20
40
60
80
100
120
140
160 Summer
0
50
100
150
200
250
300 Winter
10
11
12
13
14
15
16
17 Winter
maximum
4
5
6
7
8
9
10
11 Winter
minimum
Fig. 3. Variation in monthly temperature or precipitation for summer (December–March) and winter (June–August, 1950–2006)
from the following four Victorian climate stations: Cape Otway (large dashed line), Melbourne (small dashed line), Wilson’s
Promontory (small dotted line) and Gabo Island (continuous line).
432 Australian Journal of Botany L. Rumpff et al.
linear trend towards earlier flowering over time was recorded for
Lepidium foliosum (Fig. 4, Table 2); however, there were only
seven records for this species and the variability explained by the
model is likely to be inflated (R
2
= 0.65). Other species that
showed relatively strong trends over time were Atriplex
paludosa and Logania ovata, both of which had fewer than 25
records (Fig. 4, Table 2). Another species, the rare parasitic shrub
Dendrophthoe vittelina, indicated a strong trend towards earlier
flowering, but had a P-value of 0.10. The widespread Spinifex
sericeus also showed a trend towards earlier flowering, but had
aP-value of 0.18. Another threatened species (Calorophus
elongatus) and one widespread species (Atriplex cinerea)
were identified as showing a general trend towards earlier
flowering; however, these species had P-values of 0.20 and
0.26, respectively. We draw attention to the species that
showed general trends, despite a lack of statistical significance,
and suggest that these species should be considered as potential
indicator species until more data are collected (over a longer time
frame) to indicate otherwise.
The difficulties in using data from herbaria to identify indicator
species in Australia are discussed extensively by Gallagher et al.
(2009). A large degree of variation in flowering period over time
was expected, because flowering observations may be recorded
over the length of the flowering period. These can be of a longer
duration in warmer years (Menzel et al.2001,2006). Species that
have a shorter flowering period may be more useful in minimising
this problem (Gallagher et al.2009), although it was still difficult
to detect the scale at which variability in temperature is important,
given the irregular nature of data collection. In the present study,
the species that indicated a trend, generally flowered across a
period of 3 months. One species (Dendrophthoe vittelina) had a
range of 5 months. It was initially thought that Orchidaceae may
provide useful indicator species because species in this family
generally have a restricted flowering period (<3 months) and
are likely to have a large number of records due to their
popularity among collectors and field naturalists. However, the
species investigated did not appear to indicate that flowering
was correlated with temperatures. In some cases, this was
attributed to a lack of data; however, it is likely that flowering
in terrestrial orchids is also cued by environmental variables
such as fire (Coates et al.2006; Coates and Duncan 2009), or
other climatic variables (including interactions) not analysed in
the present study.
Variability in flowering response was also expected because
of the large spatial scale of the study (Menzel et al.2001). The
distribution of species varied in extent, and if flowering in these
species is cued by temperature, then flowering should also vary in
relation to a geographical temperature gradient. Unfortunately, it
was not possible to quantify (or confirm) this relationship because
information regarding the first-flowering dates was not available.
Even basic grouping of records in relation to location did not
clarify the results; however, again, this was largely limited by a
lack of data and irregularities in data collection.
Beyond detecting species that are cued by temperature it has
been suggested that biological indicator species should also be
(where possible) representative of a group of species so that
generalisations can be made with respect to change over a larger
spatial scale, and be easy to observe so that they are easier
to monitor (de Groot et al.1995). Of the species identified,
perhaps only the rare pea Pultenaea prolifera and the parasitic
plant Dendrophthoe vittelina could fulfil the latter criteria.
Regarding the former criteria, there is no indication at this
stage that species can be grouped according to growth form or
genus to make generalisations about biotic responses to climate
change in coastal areas of Victoria (Table 2). This is not to say
such species do not exist, only that this method was not successful
in identifying a large number of species that indicate clear
R
2
= 0.31
0
50
100
150
200
250
300
350
1840 1860 1880 1900 1920 1940 1960 1980 2000
Atriplex paludosa
R
2
= 0.31
0
50
100
150
200
250
300
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Dendrophthoe vittelina
R
2
= 0.65
0
50
100
150
200
250
300
1840 1860 1880 1900 1920 1940 1960 1980 2000
Lepidium foliosum
R
2
= 0.36
0
50
100
150
200
250
300
350
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Logania ovata
R
2
= 0.18
0
50
100
150
200
250
300
350
1860 1880 1900 1920 1940 1960 1980 2000
Pultenaea prolifera
Julian Year
Julian day
Fig. 4. Linear regression plots of Julian days v. year for flowering
observations over time for five indicator species. Lines of best-fit are shown.
Biological indicators of climate change Australian Journal of Botany 433
phenological trends. However, it was evident that plants which
begin flowering in spring and summer might be more useful
than plants which flower at the start of winter (Table 2). This is
an interesting finding because there were no consistent trends
in the magnitude of change in summer compared with winter
temperatures. This suggests that flowering in winter may be
responsive to other environmental cues, such as daylength or
precipitation. This is consistent with findings in mid- to high-
latitude areas, where the response in phenological events in spring
and summer is greater than that shown in autumn (Menzel et al.
2001,2006; Walther et al.2002).
Issues with the data sources
The majority of the data collected was sourced from herbaria
and only 5% of the data collected came from other sources.
This was primarily due to the availability, accessibility and
accuracy of other data sources. For instance, herbaria records
are considered a reliable data source because specimens are
determined by botanists. Second, herbarium specimens are
retained, so information on the phenological status of the
plant can be obtained or double-checked at a later stage. Third,
herbarium collections are accessible because they are held in
one location and records are (often) digitised. However, although
these issues limited the use of community data sources for the
present study, there is far greater potential to utilise the
information held within the community. One way in which
species may respond to climate change is through a change in
distribution and abundance (Hughes 2003b). The records kept
by individuals and by field naturalist groups constitute very
accurate species lists for specific areas along the Victorian
coast. It is suggested that there might be greater potential to
use these data sources (combined with herbarium records) in the
analysis of species distributions over time, in addition to their
potential as accurate sources of phenological information
(MacDougall et al.1998; Ponder et al.2001). It is crucial that
future work is directed towards digitising this valuable
information so that the data are not lost (Keatley et al.2002).
It must be remembered that the data used in our analyses in
general were collected in an ad hoc manner, often without any
strict sampling design (Ponder et al.2001). For instance, certain
geographic areas may be sampled more intensively than others,
with easily accessible areas (i.e. along or near tracks) representing
a greater proportion of the data than more remote or difficult-to-
access areas (Fagan and Kareiva 1997; Ponder et al.2001). It is
common to find gaps in the data because of variation in the
collection effort over time (MacDougall et al.1998; Primack et al.
2004; Bolmgren and Lonnberg 2005; Miller-Rushing and
Primack 2008). The time of collection can also be a source of
error when trying to collate phenological information.
Collections of species are often made during single trips, and
so the phenological stage of interest (i.e. first buds, first flowers)
for every species will not be represented in the one collection
(Bolmgren and Lonnberg 2005). Moreover, certain species or life
forms will be more popular or obvious to collect than others,
potentially biasing findings.
There was also a decline in the representation of recent records
held in herbaria. This is a result of a shift in the type of taxa of
interest to herbaria and museums. Over a longer time frame,
common species are well represented in collections, whereas
more inconspicuous, rare or weed species are not (Rich and
Woodruff 1992; Delisle et al.2003; Bolmgren and Lonnberg
2005). However, as confidence in taxonomic classification has
improved, the collection of more common species has declined.
This trend will limit the potential for future studies on the
ecological effects of climate change using herbaria records as
the primary source of information. Given the utility of this data
source, this is an issue that needs to be addressed.
Data collected by individuals and community groups is less
likely to decline and making better use of these sources could
ensure a continued source of phenological information for future
study. Phenological observation networks are commonly used
in the northern hemisphere (see Environmental Systems Analysis
Group 2004), and provide an excellent means for publicising
community involvement and knowledge and provide valuable
information for future scientific and community efforts.
More recently, efforts have been made in Australia to establish
a community-based phenological observation network
(Earthwatch Australia 2010), and to compile existing data
from studies that link changes in natural systems with changes
in climate in a national meta database (Bureau of Meteorology
University of Melbourne Macquarie University 2007; Chambers
et al.2007).
Despite these problems, with careful screening of data, there
is potential to further expand and utilise these data sources
to examine relationships between the phenology of plants and
changing climates in Australia (Gallagher et al.2009). Obviously,
larger datasets have a greater chance of detecting biological
changes in species. It is proposed that the uncertainty in
detecting phenological patterns could be in part resolved by
Table 2. Results of the linear regression analysis: flowering dates (Julian day) ~time
Values highlighted in bold denote significant (P<0.05) linear trends. In all cases, the records indicated the specimen was flowering (not marked as ‘in bud’,‘fruit’
or ‘fertile’). The Victorian distribution of species was classified broadly as ‘widespread’(W), ‘intermediate’(I) or ‘local’(L). Range, the time period for which
records were available; n, the total number of records compiled (minus duplicates) from all data sources
Species Family Growth form Flowering
period
Distribution Range nSlope tPR
2
Atriplex paludosa R.Br. Chenopodiaceae Shrub Dec.–Feb. I 1854–1997 14 –0.80 –2.30 0.04 0.31
Dendrophthoe vittelina (F.Muell.) Tiegh. Loranthaceae Parasitic shrub Sept.–Feb. L 1912–1999 10 –1.12 –1.89 0.10 0.31
Logania ovata R.Br. Loganiaceae Shrub Aug.–Nov. I 1858–1994 22 –0.75 –3.34 0.003 0.36
Lepidium foliosum Desv. Brassicaceae Shrub Dec.–Feb. W 1864–1978 7 –0.81 –3.02 0.03 0.65
Pultenaea prolifera H.B.Will. Fabaceae Shrub Sept.–Oct. I 1875–1992 25 –1.07 –2.11 0.05 0.16
Spinifex sericeus R.Br. Poaceae Graminoid Sept.–Feb. W 1886–2000 16 –0.31 –1.40 0.18 0.12
434 Australian Journal of Botany L. Rumpff et al.
combining these datasets with detailed studies of phenological
cycles in climatically different locations (e.g. see Hovenden et al.
2007,2008; Jarrad et al.2008).
In Australia, there is still a great deal of work to be done in
documenting phenological changes and identifying suitable
biological indicators for future monitoring of climate change
across ecosystems (Hughes 2003a; Chambers 2006; Rosenzweig
et al.2008; Gallagher et al.2009). One major concern is that
unless we collate the findings from the many disparate studies
and sources of information that currently exist (e.g. Bureau of
Meteorology University of Melbourne Macquarie University
2007), we perpetuate the problem of not knowing what data
sources exist, which in turn hinders efforts to detect and attribute
changes in phenological events to climate change (Chambers
2006). It is crucial that there is a concerted effort to understand
these effects, because there are likely important implications
for conservation and management of natural ecosystems.
These implications range from the management of rare and
endangered species, pests, or even entire habitats, through to
identifying which species are likely to become extinct in the near
future.
Acknowledgements
We thank Alison Vaughan (RBG Melbourne), Nicole Middleton (Melbourne
University) and Heidi Zimmer (ARI) for assistance with herbarium searches;
Terri Allen (South Gippsland Conservation Society) and Bon Thompson
(Traralgon Field Naturalists) for providing phenological records and general
suggestions regarding this research; Eulalie Hill and other members of
South Gippsland Conservation Society, members of ANGAIR, Sera Cutler
(La Trobe University), and members of the Bairnsdale and District Field
Naturalists Club for general discussion and access to information; Timothy
Forster (Australian Bureau of Meteorology) for providing climate records;
Yung En Chee for assistance with ANUCLIM; and Jane Catford and Joslin
Moore for initial comments on the manuscript. Ian Mansergh initiated the
project, which was funded by the Greenhouse Policy Unit, DSE.
References
Anonymous (1998) 101 uses for a dead bird. Nature 394, 105.
doi:10.1038/27969
Arft AM, Walker MD, Gurevitch J, Alatalo JM, Bret-Harte MS, Dale M,
Diemer M, Gugerli F, Henry GHR, Jones MH, Hollister RD, Jonsdottir IS,
Laine K, Levesque E, Marion GM, Molau U, Molgaard P, Nordenhall U,
Raszhivin V, Robinson CH, Starr G, Stenstrom A, Stenstrom M, Totland
O, Turner PL, Walker LJ, Webber PJ, Welker JM, Wookey PA (1999)
Responses of tundra plants to experimental warming: Meta-analysis of the
international tundra experiment. Ecological Monographs 69, 491–511.
Bolmgren K, Lonnberg K (2005) Herbarium data reveal an association
between fleshy fruit type and earlier flowering time. International
Journal of Plant Sciences 166, 663–670. doi:10.1086/430097
Bureau of Meteorology (2007) ‘Climate statistics for Australian sites.’
(Australian Bureau of Meteorology: Melbourne). Available at www.
bom.gov.au [verified December 2007].
Bureau of Meteorology (2010) ‘Australian climate variability & change –
trend maps.’(Australian Government). Available at www.bom.gov.au/
cgi-bin/climate/change/trendmaps.cgi?map=tmean&area=vic&season=
0112&period=1950 [verified June 2010].
Bureau of Meteorology, University of Melbourne, Macquarie University
(2007) ‘National Ecological Meta Database project.’(Australian
Government). Available at www.bom.gov.au/nemd/ [verified November
2007].
Chambers LE (2006) Associations between climate change and natural
systems in Australia. Bulletin of the American Meteorological Society
87, 201–206. doi:10.1175/BAMS-87-2-201
Chambers LE, Webber E, Mavromatis A, Keatley M, Hughes L (2007)
‘National ecological meta database.’BMRC Report no. 132. (Bureau
of Meteorology: Melbourne)
Coates F, Duncan M (2009) Demographic variation between populations of
Caladenia orientalis –afire-managed threatened orchid. Australian
Journal of Botany 57, 326–339. doi:10.1071/BT08144
Coates F, Lunt ID, Tremblay RL (2006) Effects of disturbance on population
dynamics of the threatened orchid Prasophyllum correctum D.L.Jones
and implications for grassland management in south-eastern Australia.
Biological Conservation 129,59–69. doi:10.1016/j.biocon.2005.06.037
Coleman MA, Brawley SH (2005) Variability in temperature and historical
patterns in reproduction in the Fucus distichus complex
(Heterokontophyta; Phaeophyceae): implications for speciation and the
collection of herbarium specimens. Journal of Phycology 41, 1110–1119.
doi:10.1111/j.1529-8817.2005.00147.x
Dahlgren JP, Zeipel Hv, Ehrlen J (2007) Variation in vegetative and flowering
phenology in a forest herb caused by environmental heterogeneity.
American Journal of Botany 94, 1570–1576.
doi:10.3732/ajb.94.9.1570
de Groot RS, Ketner P, Ovaa AH (1995) Selection and use of bio-indicators
to assess the possible effects of climate change in Europe. Journal of
Biogeography 22, 935–943. doi:10.2307/2845994
Delisle F, Lavoie C, Jean M, Lachance D (2003) Reconstructing the spread of
invasive plants: taking into account biases associated with herbarium
specimens. Journal of Biogeography 30, 1033–1042.
Department of Sustainability and Environment (2007) ‘EVC benchmarks.’
(Department of Sustainability and Environment). Available at www.dse.
vic.gov.au/DSE/nrence.nsf/LinkView/43FE7DF24A1447D9CA256EE
6007EA8788062D358172E420C4A256DEA0012F71C [verified May
2007].
Earthwatch Australia (2010) ‘Climatewatch.’http://www.climatewatch.org.
au/ [verified June 2010].
Environmental Systems Analysis Group (2004) ‘European phenology
network.’(Wageningen University). Available at www.pik-potsdam.
de/~rachimow/epn/html/frameok.html [verified August 2010].
Fagan WF, Kareiva PM (1997) Using compiled species lists to make
biodiversity comparisons among regions: a test case using Oregon
butterflies. Biological Conservation 80, 249–259.
doi:10.1016/S0006-3207(96)00144-9
Gallagher RV, Hughes L, Leishman MR (2009) Phenological trends amoung
Australian alpine species: using herbarium records to identify climate-
change indicators. Australian Journal of Botany 57,1–9.
doi:10.1071/BT08051
Hovenden MJ, Wills KE, Vander Schoor JK, Chaplin RE, Williams AL,
Nolan MJ, Newton PCD (2007) Flowering, seed production and seed mass
in a species-rich temperate grassland exposed to FACE and warming.
Australian Journal of Botany 55, 780–794. doi:10.1071/BT07107
Hovenden MJ, Wills KE, Vander Schoor JK, Williams AL, Newton PCD
(2008) Flowering phenology in a species-rich temperate grassland is
sensitive to warming but not elevated CO
2
.New Phytologist 178,
815–822. doi:10.1111/j.1469-8137.2008.02419.x
Hughes L (2000) Biological consequences of global warming: is the signal
already apparent? Trends in Ecology & Evolution 15,56–61.
doi:10.1016/S0169-5347(99)01764-4
Hughes L (2003a) Climate change and Australia: trends, projections and
impacts. Austral Ecology 28, 423–443.
doi:10.1046/j.1442-9993.2003.01300.x
Hughes L (2003b) Indicators of climate change. In ‘Climate change impacts
on biodiversity in Australia’. (Eds M Howden, L Hughes, M Dunlop,
I Zethoven, D Hilbert, C Chilcott) pp. 58–62. (Biological Diversity
Advisory Committee, Commonwealth of Australia: Canberra)
Biological indicators of climate change Australian Journal of Botany 435
Jarrad FC, Wahren C, Williams RJ, Burgman MA (2008) Impacts of
experimental warming and fire on phenology of subalpine open-heath
species. Australian Journal of Botany 56, 617–629.
doi:10.1071/BT08018
Kagata H, Yamamura K (2006) Special feature: global climate change and the
dynamics of biological communities. Population Ecology 48,3–4.
doi:10.1007/s10144-005-0241-0
Keatley MR, Hudson IL (2007) Shift in flowering dates of Australian
plants related to climate: 1983–2006. In ‘MODSIM 2007 international
congress on modelling and simulation. Land, water and environmental
management: Integrated Systems for Sustainability Modelling and
Simulation Society of Australia and New Zealand’, Christchurch, New
Zealand. (Eds L Oxley, D Kulasiri) pp. 504–510.
Keatley MR, Fletcher TD, Hudson IL, Ades PK (2002) Phenological studies in
Australia: potential application in historical and future climate analysis.
International Journal of Climatology 22, 1769–1780.
doi:10.1002/joc.822
Kudo G, Hirao AS (2006) Habitat-specific responses in the flowering
phenology and seed set of alpine plants to climate variation:
implications for global-change impacts. Population Ecology 48,49–58.
doi:10.1007/s10144-005-0242-z
Law B, Mackowski C, Schoer L, Tweedie T (2000) Flowering phenology
of myrtaceous trees and their relation to climatic, environmental and
disturbance variables in northern New South Wales. Austral Ecology
25, 160–178.
Ledneva A, Miller-Rushing AJ, Primack RB, Imbres C (2004) Climate change
as reflected in a naturalist’s diary, Middleborough, Massachusetts. The
Wilson Bulletin 116, 224–231. doi:10.1676/04-016
MacDougall AS, Loo JA, Clayden SR, Goltz JG, Hinds HR (1998) Defining
conservation priorities for plant taxa in southeastern New Brunswick,
Canada using herbarium records. Biological Conservation 86, 325–338.
doi:10.1016/S0006-3207(98)00031-7
Menzel A (2002) Phenology: its importance to the global change community.
Climatic Change 54, 379–385. doi:10.1023/A:1016125215496
Menzel A, Estrella N, Fabian P (2001) Spatial and temporal variability of the
phenological seasons in Germany from 1951 to 1996. Global Change
Biology 7, 657–666. doi:10.1046/j.1365-2486.2001.00430.x
Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kubler K,
Bissolli P, Braslavska O, Briede A, Chmielewski FM, Crepinsek Z,
Curnel Y, Dahl A, Defila C, Donnelly A, Filella Y, Jatczak K, Mage
F, Mestre A, Nordli O, Penuelas J, Pirinen P, Remisova V, Scheifinger H,
Striz M, Susnik A, Van Vliet AJH, Wielgolaski FE, Zach S, Zust A (2006)
European phenological response to climate change matches the warming
pattern. Global Change Biology 12, 1969–1976.
doi:10.1111/j.1365-2486.2006.01193.x
Miller-Rushing AJ, Primack RB (2008) Global warming and flowering times
in Thoreau’s concord: a community perspective. Ecology 89, 332–341.
doi:10.1890/07-0068.1
Miller-Rushing AJ, Primack RB, Primack D, Mukunda S (2006) Photographs
and herbarium specimens as tools to document phenological changes in
response to global warming. American Journal of Botany 93, 1667–1674.
doi:10.3732/ajb.93.11.1667
Molau U, Nordenhall U, Eriksen B (2005) Onset of flowering and climate
variability in an alpine landscape: a 10-year study from Swedish Lapland.
American Journal of Botany 92, 422–431. doi:10.3732/ajb.92.3.422
Parmesan C (2007) Influences of species, latitudes and methodologies on
estimates of phenological response to global warming. Global Change
Biology 13, 1860–1872. doi:10.1111/j.1365-2486.2007.01404.x
Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change
impacts across natural systems. Nature 421,37–42.
doi:10.1038/nature01286
Pauli H, Gottifried M, Grabherr G (1996) Effects of climate change on
mountain ecosystems –upward shifting of alpine plants. World
Resource Review 8, 382–390.
Penuelas J, Filella I (2007) Responses to a warming world. Science 294,
793–795. doi:10.1126/science.1066860
Pittock B (2003) ‘Climate change: an Australian guide to the science and
potential impacts.’(Australian Greenhouse Office: Canberra)
Ponder WF, Carter GA, Flemons P, Chapman RR (2001) Evaluation of
museum collection data for use in biodiversity assessment. Conservation
Biology 15, 648–657. doi:10.1046/j.1523-1739.2001.015003648.x
Primack D, Imbres C, Primack RB, Miller-Rushing AJ, Del Tredici P (2004)
Herbarium specimens demonstrate earlier flowering times in response to
warming in Boston. American Journal of Botany 91, 1260–1264.
doi:10.3732/ajb.91.8.1260
Primack RB, Higuchi H, Miller-Rushing AJ (2009) The impact of climate
change on cherry trees and other species in Japan. Biological Conservation
142, 1943–1949. doi:10.1016/j.biocon.2009.03.016
R Development Core Team (2006) ‘R: a language and environment for
statistical computing.’(R Foundation for Statistical Computing: Vienna)
Rich TCG, Woodruff ER (1992) Recording bias in botanical surveys.
Watsonia 19,73–95.
Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003)
Fingerprints of global warming on wild animals and plants. Nature 421,
57. doi:10.1038/nature01333
Rosenzweig C, Karoly D, Vicarelli M, Neofotis P, Wu Q, Casassa G,
Menzel A, Root TL, Estrella N, Seguin B, Tryjanowski P, Liu C,
Rawlins S, Imeson A (2008) Attributing physical and biological
impacts to anthropogenic climate change. Nature 453, 353–357.
doi:10.1038/nature06937
Sparks TH (2007) Lateral thinking on data to identify climate impacts. Trends
in Ecology & Evolution 22, 169–171. doi:10.1016/j.tree.2007.01.003
Sparks TH, Carey PD (1995) The responses of species to climate over two
centuries: an analysis of the Marsham phenological record, 1736–1947.
Journal of Ecology 83, 321–329. doi:10.2307/2261570
Vasseur L, Guscott RL, Mudie PJ (2001) Monitoring of spring flower
phenology in Nova Scotia: comparison over the last century.
Northeastern Naturalist 8, 393–402.
Viridans Biological Databases (2006) ‘Flora information system.’(Viridians
Pty Ltd: Melbourne)
Walsh NG, Stajsic V (2007) ‘A census of the vascular plants of Victoria.’
Eighth Edn. (Royal Botanic Gardens: Melbourne)
Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC,
Fromentin JM, Hoegh-Guldberg O, Bairlein F (2002) Ecological
responses to recent climate change. Nature 416, 389–395.
doi:10.1038/416389a
Whitfield J (2001) The budding amateurs. Nature 414, 578–579.
doi:10.1038/414578a
Manuscript received 23 February 2010, accepted 19 July 2010
436 Australian Journal of Botany L. Rumpff et al.
Appendix 1. The coastal vegetation types used to obtain an initial list of common species
Each group corresponds to an Ecological Vegetation Class (EVC), each of which has a published list
of common species (Department of Sustainability and Environment 2007)
EVC number Group
1 Coastal dune scrub mosaic
2 Coastal banksia woodland
3 Damp sands herb-rich woodland
5 Coastal sand heathland
6 Sand heathand
154 Bird-colony shrubland
155 Bird-colony succulent herbland
160 Coastal dune scrub
161 Coastal headland scrub
163 Coastal tussock grassland
181 Coastal gully thicket
233 Wet sands thicket
309 Calcareous swale grassland
311 Berm grassy shrubland
652 Lunette woodland
665 Coastal mallee scrub
858 Coastal alkaline scrub
876 Spray-zone coastal shrubland
879 Coastal dune grassland
898 Cane grass–lignum halophytic herbland
Biological indicators of climate change Australian Journal of Botany 437
Appendix 2. List of 101 candidate species used for searching the various data sources
The data sources used for each species are listed in the table. Key: 1 = National Herbarium of Victoria (MELISR database), 2 = University of
Melbourne Herbarium, 3 = Community sources (various), 4 = Department of Sustainability and Environment databases, 5 = La Trobe University
Herbarium, na = no data available
Species Family Flowering period Data source
Acacia retinodes =Acacia uncifolia (J.M.Black) O’Leary MIMOSACEAE Oct.–Nov. 1, 2
Acrotriche cordata (Labill.) R.Br. EPACRIDACEAE July–Oct. 1
Actites megalocarpus (Hook.f.) Lander ASTERACEAE Sept.–June 1
Allocasuarina media L.A.S.Johnson CASUARINACEAE Mar., Dec. 1, 2
Alyxia buxifolia R.Br. APOCYNACEAE Oct.–Feb. 1, 2, 3
Amphibolis antarctica (Labill.) Sond. & Asch. ex Asch. CYMODOCEACEAE Sept.–Feb. 1
Apium insulare P.S.Short APIACEAE Oct.–Feb. 1
Atriplex cinerea Poir. CHENOPODIACEAE Sept.–Dec. 1, 2
Atriplex paludosa R.Br. subsp. paludosa CHENOPODIACEAE Dec.–Feb. 1, 5
Australina pusilla (Poir.) Gaudich. subsp. pusilla URTICACEAE Oct.–Jan. 1, 2, 3
Austrofestuca littoralis (Labill.) E.B.Alexeev POACEAE Sept.–Oct. 1, 2
Austrostipa stipoides (Hook.f.) S.W.L.Jacobs & J.Everett POACEAE Oct.–Mar. 1, 2
Avicennia marina (Forssk.) Vierh. subsp. australasica (Walp.) J.Everett VERBENACEAE Mar.–Aug. 1
Banksia croajingolensis Molyneux & Forrester PROTEACEAE Mar.–Aug. na
Banksia integrifolia L.f. subsp. integrifolia PROTEACEAE Jan.–June 1, 2
Baumea laxa (Nees) Boeck. CYPERACEAE Sept.–Nov. 1
Bossiaea ensata Sieber ex DC. FABACEAE Sept.–Oct. 1, 2
Caladenia aurantiaca (R.S.Rogers) Rupp ORCHIDACEAE Oct. 3
Caladenia calcicola G.W.Carr ORCHIDACEAE Oct. 1, 4
Caladenia latifolia R.Br. ORCHIDACEAE Aug.–Dec. 3
Caladenia valida (Nicholls) M.A.Clem. & D.L.Jones ORCHIDACEAE Sept.–Oct. 1, 4
Calorophus elongatus Labill. RESTIONACEAE Feb.–July 1
Carex pumila Thunb. CYPERACEAE Sept.–Nov. 1, 2
Colobanthus apetalus (Labill.) Druce var. apetalus CARYOPHYLLACEAE Nov.–Feb. 1
Correa alba var. pannosa Paul G.Wilson RUTACEAE Sept.–Feb. 1, 2
Correa backhouseana Hook. var. backhouseana RUTACEAE June–Feb. 1, 2
Corybas despectans D.L.Jones & R.C.Nash ORCHIDACEAE July–Aug. 1
Corybas fimbriatus (R.Br.) Rchb.f. ORCHIDACEAE June–Aug. 3
Corybas unguiculatus (R.Br.) Rchb.f. ORCHIDACEAE May–July 3
Darwinia camptostylis B.G.Briggs MYRTACEAE Aug.–Nov. 1
Dendrophthoe vitellina (F.Muell.) Tiegh. LORANTHACEAE Sept.–Feb. 1, 2, 3
Exocarpos syrticola (F.Muell. ex Miq.) Stauffer SANTALACEAE Sept.–Nov. 1, 2
Galium compactum Ehrend. & McGill. RUBIACEAE Sept.–Dec. 1
Grevillea infecunda McGill. PROTEACEAE Oct.–Dec. 1
Hakea decurrens R.Br. subsp. platytaenia W.R.Barker PROTEACEAE May-Sept. 1
Halophila australis Doty & B.C.Stone HYDROCHARITACEAE Nov.–Dec. 1, 2
Hemichroa pentandra R.Br. AMARANTHACEAE Nov.–Feb. 1, 3
Hibbertia hirticalyx Toelken DILLENIACEAE Aug.–Jan. na
Hibbertia pallidiflora Toelken DILLENIACEAE Aug.–Jan. 1
Hibbertia truncata Toelken DILLENIACEAE Aug.–Jan. 1
Hybanthus vernonii (F.Muell.) F.Muell. subsp. vernonii VIOLACEAE June–Oct. 1
Ixodia achillaeoides subsp. arenicola Copley ASTERACEAE Nov.–Jan. 1
Lasiopetalum schulzenii (F.Muell.) Benth. STERCULIACEAE Sept.–Dec. 1, 2
Lepidium desvauxii Thell. BRASSICACEAE Sept.–May 1
Lepidium foliosum Desv. BRASSICACEAE Dec.–Feb. 1
Lepidosperma elatius Labill. var. ensiforme Rodway CYPERACEAE Sept.–Feb. 1
Lepidosperma gladiatum Labill. CYPERACEAE Sept.–Feb. 1, 2
Lepilaena marina E.L.Robertson ZANNICHELLIACEAE July–Dec. 1
Leptecophylla juniperina (J.R.Forst. & G.Forst.) C.M.Weiller
subsp. oxycedrus (Labill.) C.M.Weiller
EPACRIDACEAE Aug.–Nov. 1, 2
Leptospermum laevigatum (Gaertn.) F.Muell. MYRTACEAE Aug.–Nov. 1, 2, 3, 5
Leucophyta brownii Cass. ASTERACEAE Sept.–Feb. 1, 2
Leucopogon esquamatus R.Br. EPACRIDACEAE Aug.–Sept. 1, 5
Leucopogon parviflorus (Andrews) Lindl. EPACRIDACEAE Sept.–Nov. 1, 2
Limonium australe (R.Br.) Kuntze PLUMBAGINACEAE Jan.–Apr. 1, 2, 5
Logania ovata R.Br. LOGANIACEAE Aug.–Nov. 1, 2, 4
Logania pusilla R.Br. LOGANIACEAE Sept.–Nov. 1
438 Australian Journal of Botany L. Rumpff et al.
Appendix 2. (continued )
Species Family Flowering period Data source
Malva preissiana Miq. MALVACEAE July–Feb. 1
Microlepidium pilosulum F.Muell. BRASSICACEAE Sept.–Nov. 1, 4
Mitrasacme polymorpha R.Br. LOGANIACEAE Sept.–Feb. 1
Monotoca elliptica (Sm.) R.Br. EPACRIDACEAE June–Sept.
Muehlenbeckia adpressa (Labill.) Meisn. POLYGONACEAE Sept.–Jan. 1, 2
Olearia axillaris (DC.) Benth. ASTERACEAE Feb.–Apr. 1, 2
Olearia glutinosa (Lindl.) Benth. ASTERACEAE Dec.–Feb. 1, 2
Olearia ramulosa (Labill.) Benth. var. ramulosa ASTERACEAE Sept.–May 1
Olearia sp. 2 sensu Fl. Victoria 4 : 903 (1999) ASTERACEAE Jan.–Mar. 1
Olearia viscosa (Labill.) Benth. ASTERACEAE Nov.–Dec. 1, 2
Oxalis rubens Haw. OXALIDACEAE Nov.–Feb. 1
Ozothamnus turbinatus DC. ASTERACEAE Dec.–May 1, 2
Poa poiformis (Labill.) Druce POACEAE Sept.–Jan. 1, 2
Poa poiformis (Labill.) Druce var. indet. POACEAE Sept.–Jan. 1, 2
Poa poiformis (Labill.) Druce var. poiformis POACEAE Sept.–Jan. 1, 2
Poa poiformis (Labill.) Druce var. ramifer D.I.Morris POACEAE Sept.–Jan. 1
Pomaderris oraria F.Muell. ex Reissek subsp. oraria RHAMNACEAE Sept.–Nov. 1, 2
Pomaderris paniculosa F.Muell. ex Reissek subsp. paralia N.G.Walsh RHAMNACEAE Sept.–Oct. 1
Potamogeton sulcatus A.Benn. POTAMOGETONACEAE Sept.–Apr. na
Prostanthera hirtula Benth. var. hirtula LAMIACEAE Sept.–Nov. na
Pseudoraphis paradoxa (R.Br.) Pilg. POACEAE Feb.–Apr. 1, 2, 4
Pterostylis pedoglossa Fitzg. ORCHIDACEAE Mar.–July 3
Pterostylis tenuissima Nicholls ORCHIDACEAE Oct.–Mar. 1
Pultenaea canaliculata F.Muell. FABACEAE Sept.–Nov. 1, 2, 4
Pultenaea prolifera H.B.Will. FABACEAE Sept.–Oct. 1, 2
Ruppia tuberosa J.S.Davis & Toml. RUPPIACEAE Sept.–Nov. 1
Salsola tragus L. subsp. pontica (Pall.) Rilke CHENOPODIACEAE May–Oct. 1, 2
Sclerostegia arbuscula (R.Br.) Paul G.Wilson CHENOPODIACEAE July–Sept. 1, 3
Senecio pinnatifolius A.Rich. var. lanceolatus (Benth.) I.Thomps. ASTERACEAE Aug.–Nov. 1, 2, 3
Senecio pinnatifolius A.Rich. var. maritimus (Ali) I.Thomps. ms ASTERACEAE Aug.–Nov. 1, 2
Senecio spathulatus A.Rich. var. latifructus I.Thomps. ASTERACEAE Aug.–Nov. 1, 2
Senecio spathulatus A.Rich. var. attenuatus I.Thomps. ASTERACEAE Aug.–Nov. 1
Senecio spathulatus A.Rich. var. spathulatus ASTERACEAE Aug.–Nov. 1
Spinifex sericeus R.Br. POACEAE Sept.–Feb. 1, 2
Spyridium vexilliferum (Hook.) Reissek var. vexilliferum RHAMNACEAE Sept.–Jan. 1, 2
Stackhousia spathulata Sieber ex Spreng. STACKHOUSIACEAE July–Jan. 1, 3
Swainsona lessertiifolia DC. FABACEAE Aug.–Jan. 1, 2, 3
Taraxacum cygnorum Hand.-Mazz. ASTERACEAE Oct.–Dec. 1
Tetragonia implexicoma (Miq.) Hook.f. AIZOACEAE Aug.–Nov. 1, 2, 3, 5
Thelychiton speciosus (Sm.) M.A.Clem. & D.L.Jones ORCHIDACEAE Sept.–Nov. na
Veronica hillebrandii F.Muell. SCROPHULARIACEAE Dec.–Feb. 1
Xanthosia huegelii (Benth.) Steud. APIACEAE Oct.–Mar. 1
Xerochrysum papillosum (Labill.) R.J.Bayer ASTERACEAE Nov.–Feb. na
Zostera capricorni Asch. ZOSTERACEAE Oct.–Mar. 1
Zostera tasmanica G.Martens ex Asch. ZOSTERACEAE Sept.–Feb. 1
Biological indicators of climate change Australian Journal of Botany 439
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