Details

Autor(en) / Beteiligte

• HODGSON, J. A.
• THOMAS, C. D.
• OLIVER, T. H.
• ANDERSON, B. J.
• BRERETON, T. M.
• CRONE, E. E.

Titel

Predicting insect phenology across space and time

Ist Teil von

• Global change biology, 2011-03, Vol.17 (3), p.1289-1300

Ort / Verlag

Oxford, UK: Blackwell Publishing Ltd

Erscheinungsjahr

2011

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Many species appear to be undergoing shifts in phenology, arising from climate change. To predict the direction and magnitude of future changes requires an understanding of how phenology depends on climatic variation. Species show large‐scale spatial variation in phenology (affected by differentiation among populations) as well as variation in phenology from year‐to‐year at the same site (affected predominantly by local plasticity). Teasing apart spatial and temporal variation in phenology should allow improved predictions of phenology under climate change. This study is the first to quantify large‐scale spatial and temporal variation in the entire emergence pattern of species, and to test the relationships found by predicting future data. We use data from up to 33 years of permanent transect records of butterflies in the United Kingdom to fit and test models for 15 butterfly species. We use generalized additive models to model spatial and temporal variation in the distribution of adult butterflies over the season, allowing us to capture changes in the timing of emergence peaks, relative sizes of peaks and/or number of peaks in a single analysis. We develop these models using data for 1973–2000, and then use them to predict phenologies from 2001 to 2006. For six of our study species, a model with only spatial variation in phenology is the best predictor of the future, implying that these species have limited plasticity. For the remaining nine species, the best predictions come from a model with both spatial and temporal variation in phenology; for four of these, growing degree‐days have similar effects over space and time, implying high levels of plasticity. The results show that statistical phenology models can be used to predict phenology shifts in a second time period, suggesting that it should be feasible to project phenologies under climate change scenarios, at least over modest time scales.