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Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble.

Icíar Civantos-Gómez1,2, Javier García-Algarra3, David García-Callejas4,5

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Predicting species abundance is challenging. Data-driven models show strong spatial accuracy for plant communities using simple field data, but temporal predictions require longer time series.

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Area of Science:

  • Ecology
  • Data Science
  • Ecological Modeling

Background:

  • Predicting fine-scale species composition is complex due to simultaneous abiotic and biotic factors.
  • Species' intrinsic performance and interactions significantly influence local abundances.
  • Data-driven models offer a promising approach to predict species abundances.

Purpose of the Study:

  • To develop and apply a sequential data-driven modeling approach for predicting species abundances.
  • To assess the spatial and temporal predictive accuracy of these models using easy-to-obtain field data.
  • To identify potential improvements for mechanistic models by integrating data-driven insights.

Main Methods:

  • A sequential data-driven modeling approach was proposed.
  • The first step predicted potential species abundances based on abiotic variables.
  • The second step modeled realized abundances, accounting for species competition.

Main Results:

  • Models achieved remarkable spatial predictive accuracy for a diverse annual plant community using simple field variables.
  • Predictive power decreased significantly when temporal dynamics were included.
  • Longer time series are suggested as necessary for capturing sufficient variability in temporal predictions.

Conclusions:

  • Data-driven models can accurately predict spatial species composition with accessible data.
  • Temporal prediction of species abundance requires longer time series data.
  • These models can guide the enhancement of mechanistic ecological models by highlighting missing variables.