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 15^th 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.