4.7 Article

Citizen science helps predictions of climate change impact on flowering phenology: A study on Anemone nemorosa

期刊

AGRICULTURAL AND FOREST METEOROLOGY
卷 325, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.agrformet.2022.109133

关键词

Bioclimatic variables; Flowering offset; Flowering onset; Forest understory; iEcology; Wood anemone

资金

  1. Institute of Dendrology, Polish Academy of Sciences
  2. Foundation for Polish Science (FNP) from START scholarship
  3. Czech Academy of Sciences [67985939]

向作者/读者索取更多资源

This study assessed the usefulness of citizen science databases in phenological research on temperate forest understory species. By analyzing photos and predicting future climate, the study found that flowering onset of Anemone nemorosa will occur earlier, but the duration of flowering will not significantly change. The study demonstrated that citizen science databases provide valuable data for developing reliable phenology models.
Rapidly increasing resources of citizen science databases (CS) collecting information on species occurrence are increasingly useful as a data source for global biodiversity research. The photos attached to records allow to verify the species identification and identify its phenological phase. We assessed CS data's usefulness in large-scale phenological research on temperate forest understory species, using a common and widely distributed in Europe: Anemone nemorosa. We analyzed 9804 photos from CS databases. We found 177 15' grid cells with >= 10 observations of flowering plants for bootstrap estimation of flowering onset and offset. We predicted flowering dates for the present and future climate according to Shared Socioeconomic Pathways averaged over four global circulation models for 2040-60 and 2060-80 across A. nemorosa natural range. The estimated magnitude of change in the flowering phenology for both future periods is comparable. The estimated flowering onset median was 24-41 days earlier while flowering offset median was 19-34 days earlier than predicted for the current climate. We estimated a flowering length median of up to 7 days longer than for current climatic conditions. The predicted changes in the phenology of flowering will not significantly change the duration of flowering but will accelerate onset of this phenophase by about one month. Our study showed that CS might provide a valuable dataset that allows for developing reliable models of plant phenology. It was possible due to a large sample size, resulting from species characteristics: flowering when wider audience is interested in searching spring indicators, easy identification and abundant occurrence. We demonstrated that using dataset of such spatiotemporal extent can cautiously be used for development of future predictions. Such approach allows for evaluating flowering phenology in the understory and to improve understanding the consequences of climate change for biodiversity and functioning of temperate ecosystems.

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