Data from: iEcology as a tool to uncover geographic and genetic influences on the flowering phenology of invasive Carpobrotus taxa [Dataset]
1. Data collection To study the flowering phenology of Carpobrotus 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 locat...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | conjunto de datos |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/406702 |
| Acceso en línea: | http://hdl.handle.net/10261/406702 |
| Access Level: | acceso abierto |
| Palabra clave: | Invasive species Natural sciences iEcology Phenology |
| Sumario: | 1. Data collection To study the flowering phenology of Carpobrotus 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 Carpobrotus 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 Carpobrotus 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 (>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. Analyses 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 Carpobrotus taxa and could therefore provide an additional line of evidence for cluster identity and potential hybridization patterns. |
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