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Biological indicators of climate change: Evidence from long-term flowering records of plants along the Victorian coast, Australia


Abstract and Figures

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.
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Biological indicators of climate change: evidence
from long-term owering records of plants along
the Victorian coast, Australia
Libby Rumpff
, Fiona Coates
and John W. Morgan
Department of Botany, La Trobe University, Bundoora, Vic. 3086, Australia.
Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment,
Heidelberg, Vic. 3084, Australia.
Corresponding author. School of Botany, University of Melbourne, Parkville, Vic. 3010, Australia.
Abstract. We investigate the utility of using historical data sources to track changes in owering 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 eld naturalistsnotes 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 identied as
showing a general trend towards earlier owering over time, indicating a correlation with increasing temperatures. There was
some evidence to suggest that species which ower 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 eld 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.
It has long been recognised that phenological cycles are often
strongly inuenced 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 quantied
(Hughes 2003b: 53), and can act as surrogates for understanding
other speciesresponse to climate change in the same ecosystem.
Theoretically, species that respond primarily to temperature
and are less inuenced 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 scientic
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
scientic 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 Australian Journal of Botany, 2010, 58, 428439
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 inuences 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 scientic 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 eld
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; Whiteld 2001; Ledneva et al.2004;
Miller-Rushing and Primack 2008). Scientists are now using
these data sources more frequently (Sparks and Carey 1995;
Whiteld 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 (Whiteld 2001;
Ledneva et al.2004). However, the utility of these data
sources may be improved when combined with eld datasets
or scientic studies of speciesphenological 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 signicantly (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 nd 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 ora 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 scientic and community efforts.
The main aims of the study were to
(1) collate the historical data on owering times of coastal ora 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
Portland Warrnambool
0 60 km
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 identied 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
inuence of the sea on temperature.
The coastal species list
The rst step was to obtain a list of plant species on which to base
searches of herbarium collections and records held by eld
naturalists. The entire coastal area of the state of Victoria was
initially grouped into nine (pre-dened) 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 classied 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
Ofce, Wilsons Promontory Lighthouse and Gabo Island
The sources of owering 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 Victorias 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 eld naturalist groups were
contacted for access to phenological information collected by
their members. The organisations consulted were ANGAIR Inc.
(Anglesea eld 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 speciesowering times in
relation to climate change, records were sorted according to
the date of collection. These dates were converted to Julian
Coastal vegetation
communities (20)
species (297)
Common coastally
restricted species
‘Other’ coastally
restricted species
Candidate list of
species for herbarium
searches (101)
Fig. 2. Illustration of the selection process for determining candidate
species for herbarium searches. FISrefers 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 owering (from the FIS data). For
instance, the rst Julian day for species that owered 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 owering,
fruiting or budding dates against time (i.e. year and Julian day).
The data were examined for both trends towards observed
earlier owering 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
unexpectedtrends towards later owering (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
owering periods are likely to be more useful as indicator
species (Gallagher et al.2009), we did not initially place any
owering-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 owering time. Second,
there may be variation in the length of owering 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
(DecemberMarch) and winter (JuneAugust) periods. The same
analysis was repeated for total summer and winter precipitation
data. Regressions of climate data against time were calculated
for the periods 19102006 and 19502006 to examine whether
warming trends were strengthening over time (Pittock 2003).
Results were considered statistically signicant 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 signicant 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
owering 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
), and mean summer and winter
precipitation (mm year
) for the periods 19102006 and 19502006
The trend data are the slopes of linear regression models, and represent the positive (+) or negative () change in 8C year
or mm year
. Values
highlighted in bold denote signicant (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 Wilsons Promontory (Station No. 085096, 39.138S, 146.428E, 95 m asl)
Site Climate variable 19102006 19502006
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 rst owering dates were not
available, so it could not be statistically determined whether
warmer years correlated with earlier owering.
Trends in owering were examined according to the growth
form, timing and duration of owering. 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 owering 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 0.771.13C over the last 5060 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
) and more variable. Over the
entire period (19102006), there were signicant increases in
winter precipitation at two sites.
Data sourced from herbaria and the general community
demonstrated that the historical timing of owering in some
coastal species coincided with the increase in average
temperatures, with the majority of earliest owering times
recorded after 1950. However, this period also contained a
higher number of records. Monitoring the owering 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 difcult because the nature of the
data meant that any statistically signicant trends in owering
dates were generally difcult to detect.
Of the 101 species investigated, only four rare or threatened
species showed a statistically signicant (P<0.05) general trend
towards earlier owering dates with time (Fig. 4). The strongest
Temperature (°C)Rainfall (mm)
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
160 Summer
300 Winter
17 Winter
11 Winter
Fig. 3. Variation in monthly temperature or precipitation for summer (DecemberMarch) and winter (JuneAugust, 19502006)
from the following four Victorian climate stations: Cape Otway (large dashed line), Melbourne (small dashed line), Wilsons
Promontory (small dotted line) and Gabo Island (continuous line).
432 Australian Journal of Botany L. Rumpff et al.
linear trend towards earlier owering 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 inated (R
= 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
owering, but had a P-value of 0.10. The widespread Spinifex
sericeus also showed a trend towards earlier owering, but had
aP-value of 0.18. Another threatened species (Calorophus
elongatus) and one widespread species (Atriplex cinerea)
were identied as showing a general trend towards earlier
owering; 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 signicance,
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 difculties 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 owering period over time
was expected, because owering observations may be recorded
over the length of the owering period. These can be of a longer
duration in warmer years (Menzel et al.2001,2006). Species that
have a shorter owering period may be more useful in minimising
this problem (Gallagher et al.2009), although it was still difcult
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 owered 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 owering period (<3 months) and
are likely to have a large number of records due to their
popularity among collectors and eld naturalists. However, the
species investigated did not appear to indicate that owering
was correlated with temperatures. In some cases, this was
attributed to a lack of data; however, it is likely that owering
in terrestrial orchids is also cued by environmental variables
such as re (Coates et al.2006; Coates and Duncan 2009), or
other climatic variables (including interactions) not analysed in
the present study.
Variability in owering 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 owering in these
species is cued by temperature, then owering should also vary in
relation to a geographical temperature gradient. Unfortunately, it
was not possible to quantify (or conrm) this relationship because
information regarding the rst-owering 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 identied,
perhaps only the rare pea Pultenaea prolifera and the parasitic
plant Dendrophthoe vittelina could full 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
= 0.31
1840 1860 1880 1900 1920 1940 1960 1980 2000
Atriplex paludosa
= 0.31
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Dendrophthoe vittelina
= 0.65
1840 1860 1880 1900 1920 1940 1960 1980 2000
Lepidium foliosum
= 0.36
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Logania ovata
= 0.18
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 owering
observations over time for ve indicator species. Lines of best-t are shown.
Biological indicators of climate change Australian Journal of Botany 433
phenological trends. However, it was evident that plants which
begin owering in spring and summer might be more useful
than plants which ower at the start of winter (Table 2). This is
an interesting nding because there were no consistent trends
in the magnitude of change in summer compared with winter
temperatures. This suggests that owering in winter may be
responsive to other environmental cues, such as daylength or
precipitation. This is consistent with ndings 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 eld naturalist groups constitute very
accurate species lists for specic 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 difcult-to-
access areas (Fagan and Kareiva 1997; Ponder et al.2001). It is
common to nd 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. rst buds, rst owers)
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 ndings.
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 condence in taxonomic classication 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 scientic 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: owering dates (Julian day) ~time
Values highlighted in bold denote signicant (P<0.05) linear trends. In all cases, the records indicated the specimen was owering (not marked as in bud,fruit
or fertile). The Victorian distribution of species was classied 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
Distribution Range nSlope tPR
Atriplex paludosa R.Br. Chenopodiaceae Shrub Dec.Feb. I 18541997 14 0.80 2.30 0.04 0.31
Dendrophthoe vittelina (F.Muell.) Tiegh. Loranthaceae Parasitic shrub Sept.Feb. L 19121999 10 1.12 1.89 0.10 0.31
Logania ovata R.Br. Loganiaceae Shrub Aug.Nov. I 18581994 22 0.75 3.34 0.003 0.36
Lepidium foliosum Desv. Brassicaceae Shrub Dec.Feb. W 18641978 7 0.81 3.02 0.03 0.65
Pultenaea prolifera H.B.Will. Fabaceae Shrub Sept.Oct. I 18751992 25 1.07 2.11 0.05 0.16
Spinifex sericeus R.Br. Poaceae Graminoid Sept.Feb. W 18862000 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 ndings 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
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.
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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 grasslignum 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) OLeary MIMOSACEAE Oct.Nov. 1, 2
Acrotriche cordata (Labill.) R.Br. EPACRIDACEAE JulyOct. 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 JuneFeb. 1, 2
Corybas despectans D.L.Jones & R.C.Nash ORCHIDACEAE JulyAug. 1
Corybas mbriatus (R.Br.) Rchb.f. ORCHIDACEAE JuneAug. 3
Corybas unguiculatus (R.Br.) Rchb.f. ORCHIDACEAE MayJuly 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 pallidiora Toelken DILLENIACEAE Aug.Jan. 1
Hibbertia truncata Toelken DILLENIACEAE Aug.Jan. 1
Hybanthus vernonii (F.Muell.) F.Muell. subsp. vernonii VIOLACEAE JuneOct. 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 JulyDec. 1
Leptecophylla juniperina (J.R.Forst. & G.Forst.) C.M.Weiller
subsp. oxycedrus (Labill.) C.M.Weiller
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 parviorus (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 JulyFeb. 1
Microlepidium pilosulum F.Muell. BRASSICACEAE Sept.Nov. 1, 4
Mitrasacme polymorpha R.Br. LOGANIACEAE Sept.Feb. 1
Monotoca elliptica (Sm.) R.Br. EPACRIDACEAE JuneSept.
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 MayOct. 1, 2
Sclerostegia arbuscula (R.Br.) Paul G.Wilson CHENOPODIACEAE JulySept. 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 JulyJan. 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
... However, we know much less about how southern hemisphere species, particularly plants, have responded to warming climates (Chambers et al., 2013(Chambers et al., , 2016. We currently have data for long-term flowering phenology shifts through time for just 195 of the ~21,000 (Chapman, 2009) flowering plant species in Australia (Gallagher et al., 2009;Keatley et al., 2004;Keatley & Hudson, 2007;Rawal et al., 2014;Rumpff et al., 2010). These data come from just five studies in four different ecosystems (alpine, Eucalypt woodland, sclerophyll woodland and coastal vegetation). ...
... These data come from just five studies in four different ecosystems (alpine, Eucalypt woodland, sclerophyll woodland and coastal vegetation). These studies in the southern hemisphere show relatively few species advancing their flowering and some species delaying their flowering (Gallagher et al., 2009;Rumpff et al., 2010). The present study complements these existing studies by providing new data for 37 species from 19 families from sclerophyll woodland in northern Sydney, testing the hypothesis that Australian species have shifted to earlier flowering times over the last 177 years. ...
... Differences in both the rate of warming (Friedman et al., 2013) and the taxa (Box, 2002;Sanmartín & Ronquist, 2004) between the two hemispheres could drive differences in responses between northern and southern hemisphere species. Studies in the southern hemisphere have found limited shifts in species' flowering phenology (Gallagher et al., 2009;Rumpff et al., 2010), and we hypothesised that species may be less likely to show shifts in flowering timing than northern hemispheric species. Thus, our final aim was to determine whether a lower proportion of species have advanced their flowering phenology in the southern hemisphere than in the northern hemisphere. ...
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Shifts in flowering phenology have been studied in detail in the northern hemisphere and are a key plant response to climate change. However, there are relatively fewer data on species’ phenological shifts in the southern hemisphere. We combined historic field data, data from herbarium specimens dating back to 1842, and modern field data for 37 Australian species to determine whether species were flowering earlier in the year than they had in the past. We also combined our results with data compiled in the southern and northern hemispheres respectively, to determine if southern hemisphere species are showing fewer advances in flowering phenology through time. Across our study species, we found that 12 species had undergone significant shifts in flowering time, with four species advancing their flowering and eight species delaying their flowering. The remaining 25 species showed no significant shifts in their flowering phenology. These findings are important because delays or lack of shifts in flowering phenology can lead to mismatches in trophic interactions between plants and pollinators or seed dispersers, which can have substantial impacts on ecosystem functioning and primary productivity. Combining our field results with data compiled from the literature showed that only 58.5% of southern hemisphere species were advancing their flowering time, compared with 81.6% of species that were advancing their flowering time in the northern hemisphere. Our study provides further evidence that it is not adequate for ecologists to assume that southern hemisphere ecosystems will respond to future climate change in the same way as ecosystems north of the Equator. Synthesis. Field data and data from the literature indicate that southern hemisphere species are showing fewer advances in their flowering phenology through time, especially in comparison to northern hemisphere species.
... The recent observations have confirmed the changes in time and duration of different life cycle events [5][6][7]. Long-term monitoring of phenological patterns provides required scientific data to interpret the causes of such pattern, and, to assess response of community and species to changing climate [8][9][10]. There are studies based on satellite observations at large landscape level [11] to ground observation of a community [12][13][14][15][16][17][18][19][20]. ...
... The recent observations have confirmed the changes in time and duration of different life cycle events [5][6][7]. Long-term monitoring of phenological patterns provides required scientific data to interpret the causes of such pattern, and, to assess response of community and species to changing climate [8][9][10]. There are studies based on satellite observations at large landscape level [11] to ground observation of a community [12][13][14][15][16][17][18][19][20]. ...
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Phenology of two seasonally dry tropical forests examined the influence of rainfall and temperature with different phenophases and common dominant species phenology and seasonality. All the woody individual of 277 reproductively matured trees belonging to 45 species in Bhadra and a total of 335 reproductively matured trees belonging to 55 species in Mudumalai were monitored. We report the phenology patterns observed for 60 months. Observations were made monthly for leafing, flowering and fruiting phenophases. The pattern of influence of environmental factors on different phenophases was analysed. Seasonality was computed to know the strength of phenophases in both forests. The duration of the dry season is relatively longer in Bhadra than in Mudumalai. Trees in Mudumalai remained leafless between December to February, while in Bhadra maximum number of species remained leafless in the month of March. Flower initiation was in the month of March in Mudumalai and in April in Bhadra. Fruit initiation in Mudumalai with two peaks one at May (end of the dry season) and the other one during July (mid-wet season), whereas in Bhadra, there were two distinct peaks, one in May (minor) and another in November (major). The various phenophases seasonality shows leaf senescence in bhadra has strong seasonality, pollinating flower and initiating fruit in Mudumalai. We analyzed the phenological pattern and seasonality in 11 species that were common between Mudumalai and Bhadra. Our results show the differences and overlapping of leafing, flowering and fruiting phenophases and the influencing factor at community and seasonality of common species and their relationship between forest types, probably this study will serve as a baseline data for ecologists to check for phenophases at community and common species to coordinate with their phenology and climate to address future consequences of changing climate. The phenological studies of two forest types with known and unknown factors for the different species with available literature play a vital role in restoration of forest ecosystem in India and elsewhere.
... Current flower detection and monitoring methods are expensive, difficult to apply across space and time , and do not capture important spatial and temporal variation. Many studies use ground-based surveys (Potts et al., 2006), long-term monitoring programs (Menzel et al., 2006;Rumpff et al., 2010) and phenology cameras (Kosmala et al., 2016). They often rely on broad estimates, presenceabsence observations (Kosmala et al., 2016), or spatially limited estimates that fail to capture nuances between and within seasons and across landscapes. ...
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Knowledge of flowering phenology is essential for understanding the condition of forest ecosystems and responses to various anthropogenic and environmental drivers. However, monitoring the spatial and temporal variability in forest flowering at landscape scales is challenging (e.g. current monitoring is often highly localized and in-situ or for single dates). This study presents a method that combines drone and satellite images (Plan-etScope) that can produce landscape-scale maps of flowering dynamics. This method is demonstrated in forest landscapes dominated by the eucalypt Corymbia calophylla (red gum or marri) in Western Australia. Drone-derived images of flowering eucalypt canopies, available for restricted temporal and spatial extents, are used to label satellite image pixels with the proportion of a pixel footprint that is flowering. The pixels labelled with flowering proportion, the response variable, are combined with various metrics that characterize time series of spectral indices sensitive to the presence of green vegetation and cream-colored flowers, the predictor variables. A machine learning model then predicts daily pixel-level flowering proportions. The model is trained with data from two sites and is tested with data from three sites and various dates throughout the Corymbia calophylla season. The model is able to accurately predict pixel-level flowering proportion throughout the flowering season (RMSE <4% across all sites and dates), across sites with dense to sparse canopy, different background soil covers, and is robust to not detecting false positive flowering when no flowering events are occurring. Due to the spatiotemporal coverage of satellite images, this model can be deployed to generate regional maps of flowering dynamics in forest ecosystems that can be used for monitoring forest ecosystem condition and supporting research into drivers of eucalypt forest phenology.
... Unlike animals, plant survival may rely primarily on in situ responses to the novel conditions presented by a rapidly changing climate because plants may not have the ability to migrate fast enough to keep up with the current pace of climate change (Jump andPenuelas 2005, Corlett andWestcott 2013). Most of our understanding of plant responses to climate change is based on species distribution models (Brown et al. 2015), analyses of historic records, such as traits measured from herbarium specimens (Gallagher et al. 2009, Rumpff et al. 2010) and experimental studies of plant responses to climate change (De Boeck et al. 2010, Wellstein et al. 2017). However, models can be difficult to ground-truth and they have a level of uncertainty (Beale andLennon 2012, Sánchez-Mercado et al. 2017), and many parts of the world do not have sufficient historic records to allow researchers to determine how much plant traits have changed through time. ...
Studies assessing the biological impacts of climate change typically rely on long‐term, historic data to measure trait responses to climate through time. Here, we overcame the problem of absent historical data by using resurrected seeds to capture historic plant trait data for a number of plant regeneration and growth traits. We collected seed and seedling trait measurements from resurrected historic seeds and compared these with modern seed and seedling traits collected from the same species in the same geographic location. We found a total of 43 species from South‐Eastern Australia for which modern/historic seed pairs could be located. These species were located in a range of regions that have undergone different amounts of climate change across a range of temperature, precipitation and extreme measures of climate. There was a correlation between the amount of change in climate metrics, and the amount of change in plant traits. Using stepwise model selection, we found that for all regeneration and growth trait changes (except change in stem density), the most accurate model selected at least two measures of climate change. Changes in extreme measures of climate such as heatwave duration and changes in climate variability were more strongly related to changes in regeneration and growth traits than changes in mean climate metrics. Across our species, for every 5% increase in temperature variability, there was a three‐fold increase in the probability of seed viability and seed germination success. An increase of one day in the maximum duration of dry spells through time led to a 1.5‐fold decrease in seed viability and seeds became 30% flatter/thinner. Regions where the maximum heatwave duration had increased by ten days saw a 1.35 cm decrease in seedling height and a 1.04 g decrease in seedling biomass. Rapid responses in plant traits to changes in climate may be possible, however, it is not clear whether these changes will be fast enough for plants to keep pace with future climate change.
... Early flowering has been reported by several authors in response to global warming (Defila and Clot, 2001 ;Menzel et al., 2005Menzel et al., , 2006Hovenden et al., 2008 ;Gallagher et al., 2009 ;Rumpff et al., 2010 ;Chmielewski et al. 2011;Anderson et al., 2012 ;Chambers et al., 2013 ;Rawal, 2014;Burghardt et al. 2016;Rather et al., 2018). Bakke (1936), had observed a comparable precocity to that of E. resinifera, at Euphorbia esulaL.1753, ...
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Article Skvortsovia ISSN 2309-6497 (Print) ISSN 2309-6500 (Online) Skvortsovia: 6(4): 1-84 (2020) Cultivated woody plants as indicators of ongoing climate change in St. Petersburg, Russia Abstract In the city of St. Petersburg, in the last three decades a substantial increase has been observed in the mean annual air temperature and in annual precipitation. The response of cultivated plants to the ongoing climate change remain poorly understood. The article reports results of a comparison of the tolerance of woody plants to climate change in St. Petersburg at the beginning of the 21 st century (2001-2018) with the data compiled by E.L. Wolf (1917) for the period 1886-1916. This research was carried out for 593 species and infraspecific taxa from 164 genera belonging to 61 families. Most of the taxa (342 species and infraspecific taxa, 57.5%) perform better in the current climate than in the earlier period, the performance of 232 (39%) species and infraspecific taxa were unchanged, and only 21 species (3.5%) were less able to cope with the current climate. The trend towards a warmer climate makes it possible to expand from Wolf's time the range of geographic regions from which new species of woody plants may be successfully introduced into St. Petersburg. Thus, the number of species and forms that are promising in their winter tolerance for landscaping in St. Petersburg and other cities and towns of Northwest European Russia is expanding.
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Our health is closely related to our environment, such that a healthy environment brings healthy living and vice versa. Pollution due to air is a prime environmental aspect contributing to the burden of different diseases in human and also has considerable economic impact. The total air pollution accounts approximately 7 million deaths globally. Pollutants produced as combustion of particulate matter have demonstrated a time-series effect on human health. The size of inhalable particulate matter (PM10 and PM2.5) affects the mortality and morbidity upon short- and long-term exposure among all population, with highest effect on elderly individuals. Exposure to these pollutants produces the pathological alteration, such as increased inflammatory response, systemic oxidative stress, cardiovascular stress, and change in pulmonary autonomous nervous system activity. These molecular pathological events trigger several pulmonary and cardiovascular manifestations in human. From epidemiology point of view, it has been explored that among different air pollutants, particulate matter, ozone, carbon monoxide, nitrogen dioxide and sulfur dioxide are the major ones. The highest mortality is mainly observed in Asian populations as compared to Europeans and Americans. The top ten countries with the highest mortality are China, India, Pakistan, Bangladesh, Nigeria, the United States, Russia, Brazil, and Philippines, respectively. In this chapter, we reviewed different PM exposure-based epidemiological studies with more focus on high ambient Total Suspended Particulate (TSP) levels. It has also been found that overall absolute risk for mortality due to PM exposure is higher for cardiovascular compared to pulmonary disorders in case of both acute and chronic exposures.
Uncontrolled emission of greenhouse gases (GHGs) leads to global warming and climate change. It is progressively changing at an alarming rate in the coming future. Increasing global warming is responsible for the difference in temperature, frequency of precipitation, drought events, and heat waves. By the end of the twenty-first century, the CO2 crosses the concentration more than 600–1000 ppm, and it increases the temperature by 1–2 °C in tropical and subtropical countries. It is anticipated that food grain production would decline up to 30% depending on the plant group (C3 and C4 plant). This chapter deals with how C3 and C4 crop plant responds to elevated CO2 and higher temperature. Increasing concentration of atmospheric CO2 and higher temperature will promote or decrease crop growth period, development, quality, and yield. The various physiological processes like photosynthesis, respiration, and stomatal conductance are the sole mechanisms for endorsing crop growth. C3 crops grown from ambient (360 ppm) to high (720 ppm) CO2 concentrations initially enhances the net CO2 fixation and growth by nearly 30% but later on it reduced in photorespiration processes. Hence, CO2 acclimation lowers down the overall shoot nitrogen concentrations. Later on, this led to a reduction in protein content and ultimately affected the plant growth rate and biomass, whereas even under the ambient CO2, the C4 plant assimilation capability becomes saturated. The higher temperature will be responsible for heat shock injury as well as biochemical and physiological changes. Subsequently, it reduced grain production and yield depending on the geographical place. The higher temperature influences and maintains the equilibrium between C3 photosynthetic carbon assimilation and photorespiration process. It is predicted that after the interaction of atmospheric CO2 and temperature under experimental conditions, C3 plants more favored under elevated CO2 whereas, C4 plant more favored under higher temperature. There is a need for mitigation and adaptation strategies to improve agricultural crop production and minimizes the production risk for sustainable development.
Technical Report
Climate change represents a major threat to coastal ecosystems and communities. In many areas around the Australian coast, the combined projected threats of sea level rise, increased temperatures and reduced rainfall will place unprecedented stress on species, ecosystems and human settlements and industries. Given that even the strictest climate change mitigation policies are unlikely to halt or reduce the threat that climate change currently poses to coastal ecosystems, consideration is needed as to how species, ecosystems and human communities might be able to adapt to anticipated changes. The Coastal Ecosystems Responses to Climate Change Synthesis (CERCCS) Project represents a major Synthesis and Integration project commissioned by the National Climate Change Adaptation Research Facility (NCCARF) and undertaken by staff at Griffith University, the University of the Sunshine Coast, James Cook University and CSIRO. The focus of the project was on conducting a broad-scale assessment of climate change threats to coastal ecosystems of Australia and identifying potential adaptation pathways to inform decision-making and future research.
Conference Paper
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The majority of phenological studies have been carried out in the northern hemisphere (IPCC 2007; Parmesan and Yohe 2003; Root et al. 2003). This paucity of published work on natural systems in Australia, and other parts of the southern hemisphere is attributable to a rarity of natural datasets of 20 years or longer; the duration required to detect such trends (IPCC 2007; Keatley et al. 2002; Sparks and Menzel 2002). This study, therefore, uses a rare dataset from Victoria to examine the date of first flowering (DOFF) of 65 species via linear regression to determine whether there had been a change in commencement between 1983 and 2006. Linear and forward stepwise regressions were also used to determine whether there was any significant relationship between DOFF and annual rainfall and annual mean minimum, maximum and mean daily temperatures, monthly rainfall and monthly mean minimum, maximum and daily temperatures, as well as seasonal (spring, summer, autumn and winter) rainfall and seasonal mean minimum, maximum and daily temperatures. Each of the climate variables were examined by linear regression to determine whether there was a change in local temperature or rainfall over the 24 years that flowering was observed. All 65 species examined had shifts in their flowering commencement date. The overall shift in all species examined was to earlier flowering (8.81 days over the study period or 0.37 days per year (d/y)), although the shift was not statistically significant (P = 0.125). When only the 38 species which flowered earlier were examined the mean advancement was 0.9 days per year (d/y) (21.7 days over the 24 years). Whilst this shift was significant (P = 0.001), the mean delay in flowering of 0.8 d/y (19 days) for the remaining species was not (P = 0.452). The reported range in significant and non-significant changes in phenological phases of plants is between 0.08 and 0.51 days earlier per year (IPCC 2007; Parmesan and Yohe 2003; Root et al. 2003). The average shift to earlier flowering of 0.37 days per year (d/y) for the species examined in this study sits within this range. Thirteen species had a significant (P ≤ 0.05) shift in their date of first flowering. Eight species flowered earlier (x ¯ = 1.7 d/y (40.3 days over the 24 years), range 0.7 to 3.3 d/y (16.4 to 78.2 days)) and 5 species flowered later (x ¯ = 1.8 d/y (43.2 days)), range 1.0 to 2.9 d/y (23.4 to 69.0).
Examples of changes in distribution and phenology of some W European species, which are possibly related to a changing climate, are given. The preliminary results of a pilot study on the selection of indicator species for the Netherlands are presented and discussed. The paper concludes with a discussion on the need of, and possibilities for, more systematic long-term ecological research and monitoring of the effects of climate change on species and ecosystems in Europe. -from Authors
Herbarium phenology data were evaluated and then applied in a phylogenetically independent contrast study in which flowering times were compared between fleshy and nonfleshy-fruited plants growing in the north-temperate provinces of Uppland and Södermanland, southeastern Sweden (59°-60°N). To evaluate herbarium phenology data, flowering-time information taken from herbarium specimens in the Swedish Natural History Museum (S) was compared with two independent field phenology data sets. Herbarium collections and the field studies were restricted to the province of Uppland. Flowering times derived from herbarium specimens correlated equally well with each of the two field-phenology data sets as the field phenology data sets did to each other. Differences between flowering times derived from field and herbarium collections were not affected by the number of herbarium specimens used. However, these differences in flowering times were affected by flowering season such that herbarium-derived flowering times were later for early spring-flowering species and earlier for late summer-flowering species when compared with flowering times derived from field data. In the phylogenetically independent contrast study of mean flowering times in fleshy- compared with nonfleshy-fruited plants, herbarium data were compiled for 77 species in 17 phylogenetically independent contrasts. Flowering time was found to be earlier for fleshy-fruited taxa, illustrating the evolutionary interdependence between flowering and fruiting phases and the constraining effects of a north-temperate climate on phenology evolution. This study shows that herbaria are reliable and time-saving data sources for comparative phenology studies and allow for studies at large phylogenetic and geographic scales that would otherwise be impossible.
Natural-history collections in museums contain data critical to decisions in biodiversity conservation. Collectively, these specimen-based data describe the distributions of known taxa in time and space. As the most comprehensive, reliable source of knowledge for most described species, these records are potentially available to answer a wide range of conservation and research questions. Nevertheless, these data have shortcomings, notably geographic gaps, resulting mainly from the ad hoc nature of collecting effort. This problem has been frequently cited but rarely addressed in a systematic manner. We have developed a methodology to evaluate museum collection data, in particular the reliability of distributional data for narrow-range taxa. We included only those taxa for which there were an appropriate number of records, expert verification of identifications, and acceptable locality accuracy. First, we compared the available data for the taxon of interest to the “background data,” comprised of records for those organisms likely to be captured by the same methods or by the same collectors as the taxon of interest. The “adequacy”of background sampling effort was assessed through calculation of statistics describing the separation, density, and clustering of points, and through generation of a sampling density contour surface. Geographical information systems (GIS) technology was then used to model predicted distributions of species based on abiotic (e.g., climatic and geological) data. The robustness of these predicted distributions can be tested iteratively or by bootstrapping. Together, these methods provide an objective means to assess the likelihood of the distributions obtained from museum collection records representing true distributions. Potentially, they could be used to evaluate any point data to be collated in species maps, biodiversity assessment, or similar applications requiring distributional information.
Ecosystems at high latitudes and altitudes are particularly sensitive to climate change. As an effect of global warming, upward shifting of plant species in high mountain systems was predicted for the near future. In consequence the habitats of the alpine and nival vegetation could be restricted drastically, which might result in extinctions, particular of summit floras. Evidence of upward movement of vascular plants in high mountains was recently empirically determined in the European Alps. In 1992 and 1993, data on the flora of 30 high summits were collected. A comparison of the recent investigations with historical records from the same peaks indicated a distinct increase of species richness at 70% of the summits. A stagnation or a slight decrease of species richness was recorded at 9 summits, but one of them showed an increase in species abundance. The change of species richness is correlated with the geomorphological situation, whereas no significant difference could be found by comparing siliceous and carbonate summits. Approximate moving rates for common alpine plants were calculated to be between 0 and 4 meters per decade. This evidence of upward shifting of high mountain plants may already be a measurable result of global warming since the 19th century.
In the 2001 Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report numerous studies of processes and species associated with regional temperature change were listed for the Northern Hemisphere (107 in North America, 458 in Europe, and 14 in Asia), but only a handful of studies for the Southern Hemisphere and, sadly, none for Australia were included. This article looks at the progress that Australia has made in addressing these knowledge gaps during the last three years. The article highlights the need for a national approach to the study of the associations between climate change and natural systems and suggests ways in which this could be achieved.
Biological databases are needed for the development of ecologically sensitive land management strategies. Quantitative information that would serve this purpose is typically unavailable or limited to a few species. An alternative is qualitative herbarium data. While often collected unsystematically, herbarium records are usually available for many taxa. We explored the use of herbarium records for defining conservation priorities for plant taxa found in southeastern New Brunswick, Canada. Our objectives were: (1) to identify rare plant taxa collected in the study area; and (2) to group these taxa by habitat affinity, and refine their conservation status based on the vulnerability of the habitats to current and anticipated land use. The temporal and geographical variations in the collection of the herbarium records are described. A total of 351 herbarium records were found, representing 161 different taxa from 46 families. Nine habitat types were identified. Two of these habitats, rich tolerant hardwood forest and wet Thuja occidentalis forest, were classified as endangered. Collections were concentrated near settlements, in areas with road access, or in known species-rich hotspots that were repeatedly revisited. The number of collections varied through time, depending on the presence of botanists working within the study area. Despite limitations, herbarium data served as a valuable first step in identifying species of conservation concern and highlighting information gaps requiring further investigation.