Distributional limits at large and small scales (range limits and aggregation edges) are natural locations for the study of ecology and evolution (Holt 2005). Despite this there are few examples of mechanistically well described distributional limits along continuous gradients. Mechanistic descriptions of such limits provide insight into limits on adaptation, causes of extinction, the scale dependence of causal factors, and the impacts of climate change on populations. To better understand these and other issues I am parameterizing demographic models (Integral Projection Models) at multiple scales across multiple distributional limits in the species Mytilus californianus. Simultaneous monitoring of recruitment, growth, survival, and size structure followed by model parameterization and sensitivity analyses, has elucidated specific causes of changes in population growth rate across distributional limits at the scales of tens of kilometers, tens of meters, and meters. Concurrent thermal monitoring allows some demographic parameters to be modeled as functions of thermal environment, facilitating prediction of the impacts of environmental change on this and similar species.
Results/Conclusions
At the scale of tens of kilometers recruitment and size structure vary significantly, while growth and survival appear consistent across sites at this scale. This indicates that recruitment limitation is likely a driving factor in reducing population density at two of the field sites near the edge of a longitudinal range limit. At smaller scales meaningful variation is detected in survival, growth, recruitment, and size structure indicating that a more complex combination of factors determines aggregation edges. A clear relationship between intertidal high temperatures and mussel growth rate indicates that thermal environment is a key dimension of the niche of this organism. The unimodal relationship between intertidal high temperature and growth rate makes it possible to develop a model based on local thermal environment which predicts local population growth rate.
