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On the importance of time-series when making inferences on species interactions

This article discusses the significance of time-series data in studying species interactions. The space-for-time substitution method is explored, along with famous examples and its influence in ecology. The challenges of using static spatial data and the need for analyzing time-series data in non-stationary environments are highlighted. The importance of testing for interspecific interactions and measuring competitive interactions is also discussed, along with various population models. The article concludes by examining methods for measuring competitive interactions in natural and semi-natural plant communities, such as the pin-point method and the use of drones, images, and LIDAR technology.

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On the importance of time-series when making inferences on species interactions

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  1. On the importance of time-series when making inferences on species interactions Christian Damgaard Bioscience, Aarhus University

  2. Space-for-time substitution (SFT) • Space-for-time substitution is a method for studying relatively slow ecological processes • Different sites are assumed to be at different stages of development • Independent determination of a chrono-sequence

  3. Space-for-time - famous examples • SFT has played a large role in the development of ecology as a scientific discipline • Darwin - atolls, fringing reefs and barrier reefs (1842) • Warming – succession in plant communities (1985) • Cowles - development on sand dunes (1899) • Tansley - from open water to bog forest (1949) • The chrono-sequences reported by both Cowles and Tansley have later been criticized

  4. SFT and ecological processes • SFT is often used implicitly for testing hypotheses on ecological processes in static spatial data • Spatial regression models • Since ecological processes occur in time this practice is problematic, especially in non-stationary environments • More processes may lead to the same spatial pattern

  5. Testing for the importance of interspecific interactions Literature search - 44 studies of interspecific interaction • 30 analyzed manipulated experiments (8 agriculture) • 10 based conclusion on spatial analysis of static data • 0 analysis of time-series from natural ecosystems

  6. Measuring competitive interactions • Measuring competitive growth • time-series in permanent plots • non-destructive measurements of plant size

  7. Measuring competitive interactions • Which mechanisms? • competition for limiting resource (Goldberg et al.) • competition in the broad sense (Harper, Weiner) • frequency dependency (e.g. Connell et al.) • Which population model? • Lotka – Volterra • maximum entropy (Neill et al.) • small scale meta-population model (Levins) • other

  8. Which population model?

  9. Measuring competitive interactions

  10. Measuring competitive interactions • Natural and semi-natural plant communities are dominated by spatially structured perennial species with variable life histories • Often difficult to count individual plants • Large size variation among individuals of the same species. • The ecological success may be assessed using the pin-point method: • cover(= relative area that the species cover) • vertical density (= 3D space occupancy ~ biomass, plant volume, and LAI)

  11. Measuring competitive interactions Drones, images and LIDAR Lill – dry heathland Grazing and nitrogen Kalø – grassland Glyphosate and nitrogen

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