Tidal marsh habitat is at high risk of severe loss and degradation as a result of human uses, sea-level rise, changes in salinity, and more frequent and extreme storms projected by climate models. Availability of habitat is a prerequisite for long-term viability of marsh bird populations and this has been modeled in a companion California Landscape Conservation Cooperative project (Veloz et al. 2011). However, habitat alone will ensure neither resilience nor recovery of depleted and threatened populations. To provide management guidance to reduce species’ vulnerability and recover depleted populations, we developed interactive population dynamic models for four key marsh species: Black Rail, Clapper Rail, Common Yellowthroat, and Song Sparrow. The basic model can be used to assess known and potential risks, and to evaluate the efficacy of proposed management actions to counteract threats to long-term viability. For example, a 5% change in Black Rail juvenile survival alters growth rates by 2.5%. For tidal marsh Song Sparrows, detailed demographic information enabled us to develop a stochastic model to project the effects of changes in temperature, precipitation and tides on future population viability (R code files here). We incorporated the same climate change and sea-level rise parameters from the Veloz et al. (2011) study, and the same assumptions of sedimentation and organic matter accumulation from a previously developed model of marsh accretion for the San Francisco Estuary (Stralberg et al. 2011). Extreme high tides were the most significant factor threatening long-term viability of Song Sparrows, due to nest loss to flooding. However, short-term management actions can effectively arrest and even reverse anticipated declines due to sea-level rise and high tides. Our findings show that increasing nest survival through reduction of nest predation is an effective option for managers to help tidal marsh birds to adapt to climate change, and that, more generally, management actions targeting specific threats identified through demographic models will reduce the impacts of climate change on wildlife populations.
