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- Kaneryd, Linda, et al.
(författare)
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Species-rich ecosystems are vulnerable to cascading extinctions in an increasingly variable world
- 2012
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Ingår i: Ecology and Evolution. - : Wiley-Blackwell. - 2045-7758. ; 2:4, s. 858-874
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Tidskriftsartikel (refereegranskat)abstract
- Global warming leads to increased intensity and frequency of weather extremes. Such increased environmental variability might in turn result in increased variation in the demographic rates of interacting species with potentially important consequences for the dynamics of food webs. Using a theoretical approach, we here explore the response of food webs to a highly variable environment.We investigate how species richness and correlation in the responses of species to environmental fluctuations affect the risk of extinction cascades. We find that the risk of extinction cascades increases with increasing species richness, especially when correlation among species is low. Initial extinctions of primary producer species unleash bottom-up extinction cascades, especially in webs with specialist consumers. In this sense, species-rich ecosystems are less robust to increasing levels of environmental variability than species-poor ones. Our study thus suggests that highly speciesrich ecosystems such as coral reefs and tropical rainforests might be particularly vulnerable to increased climate variability.
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| 2. |
- Bae, Juhee, et al.
(författare)
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Towards a methodological framework to address data challenges in lake water quality predictions
- 2024
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Ingår i: 3rd International Conference on Water Management in Changing Conditions. - : European Water Association; IFAT. ; , s. 5-8
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Konferensbidrag (refereegranskat)abstract
- Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.
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