Southern hemisphere plants show more delays than advances in flowering phenology

This dataset is from the manuscript 'Southern hemisphere plants show
more delays than advances in flowering phenology' whereby historic
field data, modern field data and herbarium specimen data were used to
determine if plants in Sydney, Australia were
flowering earlier in the year through time. This dataset also contains
meteorological data used to analyse the relationship between temperature
or precipitation and flowering time shifts to work out if these long-term
changes in flowering phenology were impacted by seasonal climate. Finally,
this data also includes a data compilation from previous studies to
determine whether species were advancing or regressing their flowering
through time.

We collected flowering phenology data from the northern Sydney
region, NSW, Australia. This region was selected as detailed flowering
time data from the early 1960s were available for over 400 species in the
dataset - Price, 1963: Contributions to the NSW Herbarium.
Price (1963) recorded flowering at weekly intervals giving a high
degree of sensitivity in assessing flowering time shifts. We set Price’s
data to 1961, the most likely median sample date for species from her
study. Price’s data indicated the timing of “abundant
flowering” or the “main flush of flowering” (Price, 1963 p. 171) and we
interpreted and used these data as “full flowering” from this period. Only
full flowering data are presented throughout this paper. However, we also
recorded the initiation of flowering and end of flowering. Analyses of
these variables yields results that are quantitatively very similar to
those for full flowering (Supporting Information S3: Tables
S3a,b,c). We collected modern field data in sites
selected to try to match the localities and ecosystems that would have
been monitored by Price (1963). Fieldwork occurred from August-February
(end of southern hemisphere winter, through to spring and summer) in 2010,
2011, 2018 and 2019 for a subset of 37 species, in the northern Sydney
region from the Price (1963) dataset. These species were selected from a
range of families and growth forms and chosen based on their presence in
the northern Sydney region and abundance at the locations sampled
(typically we monitored > 30 individuals, however, for some species
there were lower numbers of individuals). For each species, we monitored
flowering at weekly intervals to match sampling methods in Price (1963).
Each species was determined to be in full flower if at least 50% of
individual plants were in flower. We supplemented the
historic data and field data with flowering phenology data collected from
herbarium specimens from the John T. Waterhouse Herbarium (UNSW, Sydney,
Australia), The Downing Herbarium (Macquarie University, Australia), the
John Ray Herbarium (The University of Sydney, Australia) and the National
Herbarium of New South Wales (Royal Botanic Gardens, Sydney, Australia).
Herbarium specimens have been shown to yield similar results to field data
and can be used in conjunction to increase sample sizes and time frames
(Jones & Daehler, 2018). Herbarium specimens were only included if
they had sufficient geo-location information for us to be certain that
they were collected from the sample region. We scored each specimen for
flowering status in accordance with field data scoring and species were in
full flower when >75% of the specimen’s reproductive organs/buds
had turned to flowers. Although previous studies have used 50% as the
threshold for full flowering, we believe that 75% more accurately captured
full flowering in our species: as each specimen was only one section of a
plant, a higher threshold for full flowering was necessary for comparison
with our field data. Most specimens had date data resolved to the exact
day of observation. We also included specimens with dates resolved to at
least the month of flowering and for these specimens we arbitrarily
appointed the 15th day of the month (the median of
the month) as the specimen observation date. We
conducted a literature search in December 2020 using Web of Science and
Google Scholar with keywords ‘phenology’, ‘plants’ in combination with
‘climate change’, ‘temperature’ or ‘global warming’. This yielded
flowering phenology shift data (days per decade) from studies worldwide,
including reviews, meta-analyses and other literature. We only included
studies that explicitly quantified shifts in phenology through time, had
at least 10 years of data, quantified shifts in full flowering or first
flowering date and were samples of native, non-agricultural species (i.e.
no crop species). Species were classified as advancing or delaying in
their flowering time independent of the significance of this relationship.
We also included flowering shift data from the 27 native species in our
current study. Our search yielded data for 830 species from seven studies
in the northern hemisphere (562 of which originated from a recent
meta-analysis) and 118 species across four studies (including the present
study) from the southern hemisphere.

To use these data sets, see the cleaning and manipulation of the
data, or run the same analyses all code is freely available at: https://github.com/SEveringham/flowering-phenology-changes-in-Sydney.