Detecting change in an Australian flowering record: Comparisons of linear regression and cumulative sum analysis change point analysis

Abstract

Linear regression and cumulative sum analysis (CUSUM) change point analyses were used to determine whether there had been a significant change in the first flowering date between 1983 and 2006 for 65 species. Both methods agreed that the first flowering date of 47 species did not change and that eight species had a significant change (P < 0.05) in their flowering. Three species shifted to later flowering and five species to earlier. Over the observation period, each method found that the average shift to later flowering was greater (37.4 days or approx. 1.56 days per year for CUSUM change point analysis and 51.4 days or 2.14 days per year for linear regression) than that to earlier flowering (28.4 days or approx. 1.20 days per year for change point analysis and 46.5 days or 1.97 days per year for linear regression). For the remaining 10 species the results of linear regression and change point methods differed. Each method found five species (three earlier flowering and two later) to have a significantly changed first flowering date over their observation period, where the other method did not. Some of these differences can be attributed to the fact that the CUSUM method can detect multiple change points whereas linear regression can not. Significant change points in first flowering date were identified for 13 species between the years 1987 to 1998. The most frequent year identified as a change point year was 1995.The two methods, although not interchangeable, had strong agreement (84.6%) in detecting shifts. This gives greater confidence that a change in flowering has occurred for eight species and equally importantly, that no change in first flowering date has occurred for 47 species.

Continental-scale patterns and climatic drivers of fruiting phenology: A quantitative Neotropical review

“…Also, Bayesian techniques allow surmounting the pitfalls of linear regression (Dose and Menzel, 2004, Mendoza et al unpublished) and can be especially helpful for detecting change points and rates of these changes in long-term series (Schleip et al, 2008). However, we warn that different statistical methods applied to phenology are typically not interchangeable and they can show differences in rates of change or even species responses (Keatley and Hudson, 2012).…”

“…This complexity implies temporal autocorrelation, non-linearity, non-stationary properties (which means that time series can vary over time), and an excess of zeros due to the frequent non-occurrence of the phenophase of interest. Some of the possibilities for overcoming such problems include the use of Cumulative Sum Analysis (CUSUM) for detecting change-point in phenological data (Keatley and Hudson, 2012), Generalized Additive Models for Location, Scale and Shape (GAMLSS) Polansky and Boesch, 2013), cross-correlations (Wright and Calderón, 2006), and their extension by means of wavelet cross-correlations of bivariate time series (Hudson et al, 2011). Although these methods are rarely used in tropical studies, they ensure that drivers of phenology can be identified from multiple predictors and account for the non-linearity of time series and their complexity.…”

Investigating the Association between Flowering Time and Defense in the Arabidopsis thaliana-Fusarium oxysporum Interaction

“…oxysporum , which may represent the situation in nature, can accelerate flowering time in Arabidopsis. Evidence suggests that global warming has already affected the flowering time of many plant species [ 96 – 98 ]. Simulated future seasonal warming accelerated flowering and even prompted switching of life history strategies from ‘winter’ to ‘rapid cycling’ in A .…”

Citizen science and expert opinion working together to understand the impacts of climate change

“…Current trends of rising maximum and mean temperatures decreases in rainfall along the coasts (expect from the northern coast) and cloud cover (i.e. leading to increasing solar exposure) [ 56 ], will continue to result in phenological shifts in Australia [ 5 , 14 , 35 ], as has been documented in northern hemisphere [ 2 , 36 ]. Average temperatures in all stations sampled in this study for example, will increase by approximately 3°C and the number of extreme hot days (>35°C) will double or triple by 2090 [ 54 ].…”