Demographic models provide insight into which vital rates and life stages contribute most to population growth. Integral projection models (IPMs) offer flexibility in matching model structure to a species’ demography. For many rare species, data are lacking for key vital rates, and uncertainty might dissuade researchers from attempting to build a demographic model. We present work that highlights how the implications of uncertainties and unknowns can be explored by building and analyzing alternative models. We constructed IPMs for the threatened giant gartersnake (Thamnophis gigas) based on published studies to determine where management efforts could be targeted to have the greatest effect on population persistence and what unknowns remain for future research. Given uncertainty in the survival of snakes during their first year, and in the form of the size‐survival relationship, we modeled a range of scenarios and evaluated where models agree about factors influencing population growth and where discrepancies exist. For most scenarios, the survival of large adult females had the greatest influence on population growth, but the relative importance of juvenile versus adult somatic growth for population growth was dependent on the recruitment probability and the shape of the size‐survival function. More data on temporal variation and covariance among vital rates would improve stochastic models for the giant gartersnake. This paper demonstrates the effectiveness of IPMs for studying the demography of reptiles and the value of the model‐building process for formalizing what is known and unknown about the demography of rare species. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
|Publication Subtype||Journal Article|
|Title||Demographic factors affecting population growth in giant gartersnakes|
|Series title||Journal of Wildlife Management|
|Publisher||The Wildlife Society|
|Contributing office(s)||Coop Res Unit Seattle, Western Ecological Research Center|
|Other Geospatial||Sacramento Valley|
|Google Analytic Metrics||Metrics page|