Speaker
Description
Understanding biodiversity change often relies on estimating temporal trends to quantify the direction and magnitude of population and community change. These trend estimates shape our conclusions about which assemblages are changing most rapidly and guide conservation decisions about where limited resources should be directed. Yet some populations and communities that appear to follow directional trends over a decade or two may, given more time, return toward a stable mean, reflecting underlying regulatory processes rather than irreversible change. Distinguishing such mean reverting dynamics from genuinely persistent change requires time series that are long enough to reveal whether apparent trends are transient fluctuations or components of long term trajectories. A critical question that therefore speaks to the robustness of biodiversity-change inferences is what length of time series is necessary to ensure that estimated trends reliably reflect long term dynamics rather than short term noise. Here, we first demonstrate a general approach to addressing this question using simulated time series, and then explore applications to empirical data for birds, freshwater fishes, invertebrates, and trees. These analyses provide a theoretically and empirically grounded basis for judging whether existing time series are long enough to support inferences about long term dynamics with trends, and offer practical benchmarks for designing future biodiversity monitoring programs.
| Status Group | Postdoctoral Researcher |
|---|---|
| FOR TALKS: Poster Presentation Option | Yes, I’m willing to present as a poster. |