DOI: 10.5061/dryad.gmsbcc319
title: Data from: iEcology as a tool to uncover geographic and genetic influences on the flowering phenology of invasive <em>Carpobrotus</em> taxa

description: Understanding the flowering phenology of invasive alien plants is
 essential for predicting their potential impacts on invaded ecosystems and
 developing effective management strategies. However, achieving this on a
 global scale poses significant challenges, especially for widespread
 invasive species. Digital data offers an efficient and scalable solution
 to studying the flowering phenology of plants across diverse regions and
 environments. Here, we apply this approach to one of the most problematic
 groups of invasive plants in coastal areas worldwide, to some taxa in the
 genus Carpobrotus. We collected geotagged photographs from widely used
 online platforms (i.e., Google Maps, iNaturalist, and Instagram) at key
 tourist sites in six countries spanning native (South Africa) and
 non-native (Argentina, New Zealand, Portugal, Spain, and the United States
 of America) regions. These records were analysed to document the flowering
 phenology of Carpobrotus plants in different regions linked to their
 genetic lineages (clusters), focusing on the start, end, and peak
 flowering periods. Our results show that broad floristic region and
 sampling locality, rather than genetic lineage, are the primary
 determinants of flowering phenology in the Carpobrotus taxa studied.
 Non-native populations often displayed extended flowering periods compared
 to native populations, potentially enhancing their pollen availability and
 seed production, contributing thus to increased propagule pressure or seed
 bank. Apparent differences among genetic clusters in single-model analyses
 were not retained once a site‐level random effect was included, indicating
 that observed cluster contrasts reflect local environmental and sampling
 variation more than intrinsic genetic differences. This study highlights
 the use of digital data to address critical knowledge gaps in the
 flowering phenology of invasive plants across native and non-native
 ranges. By identifying extended flowering periods and their potential
 contribution to increased propagule pressure through prolonged seed
 production and subsequent accumulation in the soil seed bank, our findings
 provide valuable insights for developing targeted management strategies,
 such as optimizing intervention timing to coincide with flowering peaks.
description: <strong>1. Data collection</strong>
 To study the flowering phenology of
 <em>Carpobrotus</em> taxa, we selected 29 sites (Table 1 in
 the associated manuscript) that represent populations from all three
 genetic clusters identified by Novoa et al. (2023) across both their
 native and non-native ranges. Overall, we selected locations from
 different world floristic regions (Liu et al. 2023): the Neotropic
 (Argentina: cluster Admixed), the Novozealandic (New Zealand: cluster A),
 the Holarctic (Portugal, Spain, and the United States: clusters A, B, and
 Admixed), and the African (South Africa: clusters A and C). Within the
 Holarctic realm, we further distinguished the Southern European (e.g., the
 Azores, the Portuguese volcanic islands in Macaronesia, and peninsular
 Spain) from the Californian subregion (western United States).
 For data collection, we utilized a multi-platform approach
 including Instagram, iNaturalist, and Google Maps, with a focus on
 photographs uploaded between 2017 and 2022 (Fig. 1 in the associated
 manuscript). Records from Instagram were obtained by manually searching
 for images geotagged to the selected locations where
 <em>Carpobrotus</em> taxa were visible. To ensure accuracy in
 dating, we excluded images posted retroactively (e.g., not taken the day
 when the post was created), such as when users indicated in the text
 caption that this was in the past. For this reason, we also excluded
 commercial photography accounts since photoshoots are often posted at a
 later date. In regions where Instagram provided limited information, this
 was complemented by photographs obtained from iNaturalist and Google Maps,
 where we gathered user-submitted observations of
 <em>Carpobrotus</em> taxa. For each image, we recorded the
 location, date, presence or absence of flowers, colours of petaloid
 staminodes (which mimic petals), and density of flower presence, which
 were classified into five levels: "mass flowering (&gt;50)",
 "many flowers (20–49)", "some flowers (3–19)",
 "few flowers (1–2)", and "no flowers" (Fig. 2 in the
 associated manuscript). The classification of flower density and other
 phenological parameters were performed by multiple authors. To try to keep
 the classification consistent, authors received guidance on how to
 evaluate the images, and the flower counts were binned into broad
 categories to reduce potential inaccuracies in interpretation. It is also
 important to note that the recorded density of flowers was influenced in
 part by whether the photographs were taken as close-ups or wide-view
 shots. For example, a record of 'few flowers' may indicate that
 only a small number of flowers were blooming, or it could reflect a
 close-up photograph of a plant during mass flowering. Either way, we were
 able to gather both landscape photographs and records of flower presence
 or absence, providing valuable data on flowering patterns.
 <strong>Analyses</strong> All
 statistical analyses were conducted in R 4.2.2 (R Core Team, 2022). To
 address our two research question: (i) whether flowering phenology differs
 among broad floristic regions and (ii) whether it differs among
 population-genetic clusters; we modelled the probability that a photograph
 contained flowers (0/1) with binomial generalised additive models (GAMs).
 Each model included a cyclic cubic spline for day-of-year, s(doy), to
 capture the seasonal flowering cycle. We fitted and compared four
 candidate models. The baseline model (M0) contained
 only the seasonal spline. Model M1 added three fixed effects that address
 the first and second research questions simultaneously: observation year
 (fyear, 2017–2022) to capture inter-annual variability, floristic region
 (Africa/South Africa, Holarctic–California, Holarctic–Europe,
 Novozealandic) to test for geographic differences, and cluster (A, B, C or
 Admixed) to test the effect of genetic lineage. Model M2 retained the
 seasonal spline but accounted for uneven sampling effort by introducing a
 single random intercept for site. Lastly, the full model (M3) combined the
 fixed effects of M1 with the site random effect of M2, thereby testing
 regional and genetic predictors while simultaneously accounting for uneven
 sampling effort. All GAMs were fitted with the
 'mgcv' R package (v 1.9-1) using maximum-likelihood estimation
 of smoothing parameters, which allows Akaike's Information Criterion
 (AIC) to be applied directly to rank competing models (Wood 2011). Records
 from Argentina (Neotropic realm) were removed a-priori because the
 available sample (n=28) was below our minimum threshold of 30 observations
 per region. Diagnostic checks (i.e., residual-versus-fitted plots,
 QQ-plots, dispersion statistics and variance-inflation factors) indicated
 no over-dispersion, no influential residual structure and low collinearity
 among the fixed predictors. Reference categories were South Africa for
 region (native range), cluster A for genetic cluster and 2017 for
 observation year; all coefficients are interpreted relative to these
 baselines. In separate analyses, we plotted
 petaloid-staminode colour and flower density as 100% stacked bar charts by
 genetic cluster (Fig. 5A in the associated manuscript). In the field,
 petaloid-staminode colour have been used to distinguish
 <em>Carpobrotus</em> taxa and could therefore provide an
 additional line of evidence for cluster identity and potential
 hybridization patterns.
description: # Data from: iEcology as a tool to uncover geographic and genetic
 influences on the flowering phenology of invasive *Carpobrotus* taxa
 Dataset DOI: [10.5061/dryad.gmsbcc319](10.5061/dryad.gmsbcc319) ##
 Description of the data and file structure This dataset was compiled to
 investigate the flowering phenology of invasive Carpobrotus taxa across
 their global distribution using an iEcology approach. We collected
 geotagged photographs from three online platforms (Instagram, iNaturalist,
 and Google Maps) to document the presence, absence, and density of flowers
 throughout the year. The data collection focused on 29 sites across six
 countries, encompassing both the native range (South Africa) and
 non-native invaded regions (Argentina, New Zealand, Portugal, Spain, and
 USA). Each photographic record was systematically evaluated to determine
 flowering status, flower density, and floral color characteristics. The
 dataset represents observations from 2017-2022 and includes records that
 were used to analyze how flowering phenology varies across different
 genetic lineages (clusters A, B, C, and Admixed) and geographic regions.
 ### Files and variables #### File: data.xlsx **Description:** Photographic
 records of Carpobrotus taxa flowering phenology collected from online
 platforms (Instagram, iNaturalist, Google Maps) across 29 sites in 6
 countries from 2017-2022. Contains observations used to analyze flowering
 patterns across genetic clusters and geographic regions. #####
 **Variables:** * **photo_id** - Unique identifier for each photographic
 record (integer) * **day** - Day of month when photograph was taken
 (integer: 1-31) * **month** - Month when photograph was taken (integer:
 1-12) * **year** - Year when photograph was taken, abbreviated (integer:
 17-22, representing 2017-2022) * **country** - Country and region where
 photograph was taken (text: "Argentina", "New
 Zealand", "Portugal: Azores", "South Africa",
 "Spain", "USA: California") * **site** - Site name
 within country (text: e.g., "A Lanzada", "Foxton",
 "Mackerricher") * **platform** - Source platform of photograph
 (text: "Instagram", "iNaturalist", "Google
 Maps") * **flower_num** - Flower count/density observed in photograph
 (integer: 0, 1, 2, 3-19, 20-49, 50-100) * **flower.bin** - Binary flower
 presence indicator (integer: 0 = no flowers, 1 = flowers present) *
 **flower.group** - Categorical flower density description (text: "no
 flowers", "few flowers (1-2)", "some flowers
 (3-19)", "many flowers (20-49)", "mass flowering
 (50+)") * **petal_col** - Color of petaloid staminodes (text:
 "pink", "yellow", "mixed", or NA when no
 flowers) * **fruits** - Number of fruits visible (integer or NA when no
 fruits visible) * **latitude** - Site latitude in decimal degrees
 (numeric: -43.659 to 42.433) * **longitude** - Site longitude in decimal
 degrees (numeric: -123.795 to 175.217) * **Cluster** - Genetic cluster
 assignment per Novoa et al. 2023 (text: "A", "B",
 "C", "Admixed") * **Site_code** - Abbreviated site
 identifier per Novoa et al. 2023 (text: country/region code + number,
 e.g., "CA1", "NZ2", "SE3") ## Code/software
 No specialized software is required to view this dataset. The data is
 provided as a comma-separated values (CSV) file that can be opened with: *
 Any spreadsheet software (e.g., Microsoft Excel, LibreOffice Calc, Google
 Sheets) * Any text editor (e.g., Notepad, TextEdit) * Statistical software
 such as R, Python, SPSS, or SAS * Programming languages with CSV parsing
 capabilities ## Access information Other publicly accessible locations of
 the data: * Not applicable - this is the primary repository for this
 dataset Data was derived from the following sources: * Instagram
 ([www.instagram.com](http://www.instagram.com/)) - User-generated
 photographs with geotags at study locations * iNaturalist
 ([www.inaturalist.org](http://www.inaturalist.org/)) - Citizen science
 observations of Carpobrotus taxa * Google Maps (maps.google.com) -
 User-submitted photographs at study locations **License information:** The
 original photographs remain the property of their respective contributors
 on each platform. This derived dataset contains only extracted
 phenological and location information, not the original images. Users were
 not directly contacted; only publicly available geotagged images were
 analyzed. The data extraction and compilation was conducted in accordance
 with each platform's terms of service for research purposes.