Elucidating how organismal survival depends on the environment is a core component of ecological and evolutionary research. To reconcile high-frequency covariates with lower-frequency demographic censuses, many statistical tools involve aggregating environmental conditions over long periods, potentially obscuring the importance of fluctuating conditions in driving mortality. Here, we introduce a flexible model designed to infer how survival probabilities depend on changing environmental covariates. Specifically, the model (1) quantifies effects of environmental covariates at a higher frequency than the census intervals, and (2) allows partitioning of environmental drivers of individual survival into acute (short-term) and chronic (accumulated) effects. By applying our method to a long-term observational data set of eight annual plant species, we show we can accurately infer daily survival probabilities as temperature and moisture levels change. Next, we show that a species' water use efficiency, known to mediate annual plant population dynamics, is positively correlated with the importance of "chronic stress" inferred by the model. This suggests that model parameters can reflect underlying physiological mechanisms. This method is also applicable to other binary responses (hatching, phenology) or systems (insects, nestlings). Once known, environmental sensitivities can be used for ecological forecasting even when the frequency or variability of environments are changing.
1.Duke Univ, Program Ecol, Biol Sci Bldg,130 Sci Dr, Durham, NC 27708 USA 2.Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA 3.Duke Univ, Dept Biol, Biol Sci Bldg,130 Sci Dr, Durham, NC 27708 USA 4.2000 W Lincoln St, Mt Prospect, IL 60056 USA 5.Univ Minnesota, Dept Plant & Microbial Biol, 1479 Gortner Ave, St Paul, MN 55108 USA
Recommended Citation:
Tomasek, Bradley J.,Burghardt, Liana T.,Shriver, Robert K.. Filling in the gaps in survival analysis: using field data to infer plant responses to environmental stressors[J]. ECOLOGY,2019-01-01