Societal adaptation Options to Changes in Phenology

Vliet, A.J.H. van

doi:10.1007/978-90-481-3335-2_4

Cited by 9 publications

(7 citation statements)

References 70 publications

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“…If the phenology of the different varieties is affected to varying extents, then the harvest period can be either compressed or spread out. If DOY M is advanced more in later ripening varieties than earlier ripening varieties a compression of the harvest period may occur (Webb et al, 2007;Van Vliet, 2010). This would create problems for winegrape intake scheduling in wineries at harvest time, and make it more difficult to process each batch of fruit at the time when grape quality in the vineyard is deemed 'optimal', having important implications for planning of infrastructure and staffing during this time (Webb et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, if changes to phenology in response to climatic shifts do not affect all varieties in the same way a compression or expansion of the harvest period may occur. A compression would occur if later ripening varieties are more sensitive to climatic changes than earlier ripening varieties causing the harvest window to reduce, consequently impacting vineyard logistics, intake scheduling and winery infrastructure (Webb et al, 2007;Van Vliet, 2010). Measurement of the spread of winegrape maturity dates across regions and time series from this analysis is explored to determine whether or not a compression of the harvest period has occurred over the time period of these observations.…”

Section: Introductionmentioning

confidence: 99%

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Observed trends in winegrape maturity in Australia

Webb

Whetton

Barlow

2011

Global Change Biology

133

102

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An extensive assessment of historical trends in winegrape maturity dates from vineyards located in geographically diverse winegrape growing regions in Australia has been undertaken. Records from 44 vineyard blocks, representing a range of varieties of Vitis vinifera L., were accessed. These comprise 33 short-term datasets (average 17 years in length) and 11 long-term datasets, ranging from 25 to 115 years in length (average 50 years). Time series of the day of the year grapes attain maturity were assessed. A trend to earlier maturity of winegrapes was observed in 43 of the 44 vineyard blocks. This trend was significant for six out of the 11 long-term blocks for the complete time period for which records were available. For the period 1993-2009, 35 of the 44 vineyard blocks assessed displayed a statistically significant trend to earlier maturity. The average advance in the phenology was dependent on the time period of observation, with a more rapid advance over more recent decades. Over the more recent 1993-2009 period, the average advance was 1.7 days year, whereas for the period 1985-2009 the rate of advance was 0.8 days yr À1 on average in the 10 long-term vineyard blocks assessed for cross-regional comparison. The trend to earlier maturity was associated with warming temperature trends for all of the blocks assessed in the study.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observed trends in winegrape maturity in Australia

Webb

Whetton

Barlow

2011

Global Change Biology

133

102

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“…). Changes in global climate will inevitably have long‐term impacts on phenology (Parmesan ) with knock‐on effects for ecosystems and people (van Vliet ). It is also clear that there will be feedbacks between changing phenology and climate, but they are poorly characterised by current climate models (IPCC ).…”

Section: Introductionmentioning

confidence: 99%

Fourier analysis to detect phenological cycles using long‐term tropical field data and simulations

et al. 2016

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* Changes in phenology are an inevitable result of climate change, and will have wide-reaching impacts on species, ecosystems, human society and even feedback onto climate. Accurate understanding of phenology is important to adapt to and mitigate such changes. However, analysis of phenology globally has been constrained by lack of data, dependence on geographically limited, non-circular indicators and lack of power in statistical analyses. \ud * To address these challenges, especially for the study of tropical phenology, we developed a flexible and robust analytical approach - using Fourier analysis with confidence intervals - to objectively and quantitatively describe long-term observational phenology data even when data may be noisy. We then tested the power of this approach to detect regular cycles under different scenarios of data noise and length using both simulated and field data. \ud * We use Fourier analysis to quantify flowering phenology from newly available data for 856 individual plants of 70 species observed monthly since 1986 at Lopé National Park, Gabon. After applying a confidence test, we find that 59% of the individuals have regular flowering cycles, and 88% species flower annually. We find time series length to be a significant predictor of the likelihood of confidently detecting a regular cycle from the data. Using simulated data we find that cycle regularity has a greater impact on detecting phenology than event detectability. Power analysis of the Lopé field data shows that at least six years of data are needed for confident detection of the least noisy species, but this varies and is often greater than 20 years for the most noisy species. \ud * There are now a number of large phenology datasets from the tropics, from which insights into current regional and global changes may be gained, if flexible and quantitative analytical approaches are used. However consistent long-term data collection is costly and requires much effort. We provide support for the importance of such research and give suggestions as to how to avoid erroneous interpretation of shorter length datasets and maximize returns from long-term observational studies

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“…2008). This is not only because phenological phases are significantly influenced by temperature (Menzel 2002) and rainfall (Keatley & Hudson 2007), but also because changes in phenological processes have significant consequences for human health, biodiversity, forestry and agriculture (Fitter & Fitter 2002; Beggs 2004; van Vliet 2010). Hence, determining whether or not there have been significant changes in the timing of phases has been a primary focus of phenological research since the mid 1990s (Cleland et al .…”

Section: Introductionmentioning

confidence: 99%

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

Keatley

Hudson

2012

Austral Ecology

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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.

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Societal adaptation Options to Changes in Phenology

Cited by 9 publications

References 70 publications

Observed trends in winegrape maturity in Australia

Observed trends in winegrape maturity in Australia

Fourier analysis to detect phenological cycles using long‐term tropical field data and simulations

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

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