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Spatiotemporal exploratory models for broad-scale survey data.

Daniel Fink1, Wesley M Hochachka, Benjamin Zuckerberg

  • 1Cornell Lab of Ornithology, Ithaca, New York 14850, USA. df36@cornell.edu

Ecological Applications : a Publication of the Ecological Society of America
|January 27, 2011
PubMed
Summary
This summary is machine-generated.

The Spatiotemporal Exploratory Model (STEM) accurately describes dynamic animal population distributions by incorporating spatial and temporal factors. This new modeling approach improves predictions for migratory species compared to traditional methods.

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Area of Science:

  • Ecology
  • Computational Biology
  • Biostatistics

Background:

  • Animal population distributions are dynamic, influenced by biotic, abiotic, and anthropogenic factors.
  • Accurate modeling of species distributions requires accounting for spatiotemporal variation.
  • Existing species distribution models often lack robust spatiotemporal structure.

Purpose of the Study:

  • Introduce a flexible semiparametric framework, the Spatiotemporal Exploratory Model (STEM), for analyzing dynamic species occurrence and abundance.
  • Incorporate essential spatiotemporal structure into species distribution modeling.
  • Explore distributional dynamics arising from various ecological processes.

Main Methods:

  • Developed STEM, a semiparametric model with a multi-scale strategy to differentiate local and global spatiotemporal structure.
  • Utilized a user-specified species distribution model for local patterns, scaled up via ensemble averaging.
  • Applied STEM to eBird citizen science data for migratory (Tree Swallow) and nonmigratory (Northern Cardinal) species.

Main Results:

  • STEM provided more accurate descriptions of monthly Tree Swallow distribution changes compared to conventional bagged decision tree models.
  • STEM demonstrated no loss of predictive power when modeling the distribution of the nonmigrory Northern Cardinal, which exhibited minimal spatiotemporal variation.
  • The model effectively captures both local and global-scale spatiotemporal patterns.

Conclusions:

  • STEM offers a powerful and flexible framework for modeling dynamic species distributions, particularly for migratory species.
  • The model's multi-scale approach enhances the understanding of ecological processes driving population shifts.
  • STEM represents a significant advancement in species distribution modeling, leveraging citizen science data effectively.