4.8 Article

Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities

期刊

ECOLOGY LETTERS
卷 25, 期 9, 页码 1974-1985

出版社

WILEY
DOI: 10.1111/ele.14070

关键词

community ecology; complexity; ecological forecasting; empirical dynamic modelling; experiment; food webs; interaction strength; microbial community; prediction; temperature

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资金

  1. Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung [310030_188431]
  2. University of Zurich
  3. Swiss National Science Foundation
  4. Swiss National Science Foundation (SNF) [310030_188431] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

The potential for forecasting ecological systems is currently unclear due to their complexity. In this study, a microbial system was monitored to investigate the forecasting of species abundances. The results showed that species with more interactions had weaker interactions but better abundance prediction. Forecast skill increased with model size, but greater system complexity decreased it for some species. These insights could improve species forecasting and monitoring.
The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.

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