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Related Experiment Video

Updated: Jun 19, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Modeling survival at multi-population scales using mark-recapture data.

V Grosbois¹, M P Harris, T Anker-Nilssen

¹Laboratoire "Biométrie et Biologie Evolutive" UMR 5558, Bâtiment Gregor Mendel Université Claude Bernard Lyon 1, 43 Boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. vladimirgrosbois@hotmail.com

|November 6, 2009

Summary

This summary is machine-generated.

Large-scale environmental factors synchronize vertebrate survival, while local factors can synchronize or desynchronize populations. Sea surface temperature impacts Atlantic Puffin survival, acting as both a synchronizing and desynchronizing agent.

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Published on: October 11, 2016

Area of Science:

Ecology
Population Dynamics
Environmental Science

Background:

Vertebrate population demography is influenced by both large-scale synchronizing processes and local, independent processes.
Understanding the interplay between these scales is crucial for effective population management and conservation.

Purpose of the Study:

To develop a statistical model to differentiate temporal variation in survival into large-scale and local components.
To assess the influence of environmental factors on these survival components.
To quantify the synchrony among populations and the role of environmental factors in this synchrony.

Main Methods:

Developed a statistical model for analyzing multi-population, individual monitoring data.
Partitioned survival variation into global (large-scale) and local components.
Quantified the contribution of environmental factors, specifically sea surface temperature (SST), to survival variation.

Main Results:

67% of between-year survival variance in Atlantic Puffins was explained by a global spatial-scale component, indicating high synchrony.
Local sea surface temperature (SST) influenced both global and local survival components, acting as both a synchronizing and desynchronizing agent.
Survival variation unexplained by local SST remained highly synchronized, suggesting other environmental factors also play a synchronizing role.

Conclusions:

Environmental factors, like SST, significantly impact population synchrony.
A substantial portion of survival variation is driven by large-scale processes, highlighting the importance of spatial scale in demographic studies.
Further research combining demographic analysis with population size dynamics is recommended for a comprehensive understanding of multi-population dynamics.