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SPREC Report Summary

Project ID: 337785
Funded under: FP7-IDEAS-ERC
Country: Switzerland

Mid-Term Report Summary - SPREC (Demographic and Phenotypic Signals of Population Responses to Environmental Change)

A major goal in biodiversity conservation is to predict responses of biological populations to environmental change. To achieve this, we must identify early warning signals of the demographic changes that underlie population changes. In this project, we develop a predictive framework by identifying the demographic and phenotypic statistics that can be used as early warning signals of demographic regime shifts. So far, we have identified problems common to currently available generic early warning signals and proposed important improvements over these methods. To construct more reliable early warning signals, we have reviewed currently available frameworks for quantifying the evolutionary and plastic components of trait and demographic change. We have investigated links between ecological and evolutionary processes in changing environments using unique long-term individual-based datasets from several mammalian populations. While doing so, we have also addressed important methodological issues common to trait-based demographic analysis of such individual-based data. These analyses have identified key species-specific life-history processes and allowed us to construct trait-based demographic models of each population. Our next task is to use these trait-based models and perturb key parameters to simulate population and trait dynamics under multiple environmental scenarios. The simulations will yield time-series data from which we will estimate demographic and phenotypic statistics. We will test the ability of these statistics to predict demographic changes using a novel decision algorithm framework. At the same time, using laboratory microcosms, we experimentally proved the ability of demographic and phenotypic statistics to predict population responses to environmental change. By combining fitness-related trait information with abundance-based early warning signals, our generalisable approach provided a novel method for predicting how wildlife populations respond to environmental change.


Seyfi Arpat Ozgul, (Professor)
Tel.: +41 44 6354746
Record Number: 189113 / Last updated on: 2016-09-20
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