Ecologists now widely recognize that the timing of life cycle events is shifting in response to directional (non-stationary) environmental change. However, it is not clear whether these shifts generally benefit or reduce population viability. Researchers will combine historical data and experimental manipulations to determine how non-stationary environments and shifting interactions affect population viability of at-risk species. Using butterflies and their interacting species as a model system, researchers will investigate (1) the extent of recent changes in phenology, (2) the correspondence between new phenologies of interacting species, and (3) the importance of these changes for population viability. This work will focus on general trends across at-risk butterfly taxa, and on the demographic effects of phenological shifts for interactions for three focal species with higher and lower trophic levels. This research with three focal species will quantify shifts in the relative phenology of these species and their ecologically-important interacting species, and use experiments to quantify the impacts of observed phenological shifts on specific vital rates and subsequent population viability. These focal species are not currently federally listed, but are under consideration for listing at various levels, so might become a concern to Department of Defense (DoD) land managers if conditions change. By integrating vital rates across the life cycle into demographic models, researchers will contribute to understanding management of these species and to a general framework to highlight conditions under which phenological changes have positive, negative, or negligible effects on population dynamics.

Technical Approach

To quantify whether changes in phenology correspond to trends in abundance across many species, researchers will compile time-series of abundance data collected by local land managers for state and federally listed butterfly species on or near DoD lands. Researchers will quantify trends in phenology (based on timing of occurrence) and abundance to test whether phenological shifts in butterfly occurrence are associated with increases or declines in abundance and abundance-derived estimates of population growth. This project will also test whether phenological shifts correspond to changes in temperature. To determine how changes in the phenology of species’ interactions affect population dynamics, researchers will quantify the effect of phenological shifts on population viability of the three focal butterfly species (Baltimore checkerspot (Euphydryas phaeton), Puget blue (Plebejus icarioides blackmorei) and monarch (Danaus plexippus)), relative to ecologically important interacting species. For each focal species and its key interactors, researchers will, first, quantify the rate of phenological change in the recent past (~15-40 years depending on available data). Researchers will then experimentally quantify how contemporary shifts in phenology affect population growth rates. They will evaluate the relative importance of shifts in each interaction by combining vital rates measured under varying phenological conditions into demographic life cycle models for each species, and project changes in population viability.


This work will provide a crucial test of whether phenological shifts generally enhance or reduce population viability in nonstationary environments. In so doing, researchers will build general modeling tools that can be applied to other taxa. For managers of at-risk species, researchers will produce one-page fact sheets for the ~ 20 species included in the multispecies analysis, including trends in abundance and documented shifts in phenology over time. In addition, this work will create a Decision-Support Framework for focal species of interest that can be applied broadly to interacting species with asynchronous phenological responses to climate change. This framework will be used to assess the importance of species-specific phenology and key interactions for designing management strategies in light of non-stationary environments.