Identifying multispecies synchrony in response to environmental covariates

Area of Science:

• Ecology
• Ecological modeling
• Population dynamics

Background:

• Understanding complex ecological interactions requires multispecies models.
• Recent advancements allow exploration of demographic synchrony across multiple species.
• Species in similar environments face common factors, but respond differently.

Purpose of the Study:

• To group species by their response to environmental covariates using multispecies models.
• To analyze garden bird data from the British Trust for Ornithology's Garden Bird Feeding Survey.
• To test hypotheses about similarities and synchrony in species sharing an environment.

Main Methods:

• Fitting a multispecies model to garden bird data.
• Employing the reversible jump algorithm to estimate posterior model probabilities.
• Comparing models with varying subsets of shared regression coefficients among species.

Main Results:

• Good agreement found between species with small random effects and those with shared coefficients, but not always.
• Increased uncertainty in sharing regression coefficients when species groups were less correlated.
• Detection of multispecies responses to covariates using posterior model probabilities.

Conclusions:

• The developed methods effectively test hypotheses on species synchrony in shared environments.
• Posterior model probabilities identify multispecies responses to measured covariates.
• The approach accounts for synchrony and unexplained variation via random effects, allowing for species-specific parameter measurement.