Abstract
Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-for-time substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable.
Highlights
Ecological forecasting uses scientific data to model how environmental scenarios will affect future ecosystems, ecosystem services, and natural capital [1]
The predictive power of these models depends on the quantity and quality of the data used to determine the statistical relationship between an environmental driver and the ecosystem response
The fit of Mod Waters models to the data they were created from is shown in figures S1A and S2A. Note that these values are lower than what is typically found in Florida Everglades management scenarios because we limited our sample size to 100 observations per replicate
Summary
Ecological forecasting uses scientific data to model how environmental scenarios will affect future ecosystems, ecosystem services, and natural capital [1]. The predictive power of these models depends on the quantity and quality of the data used to determine the statistical relationship between an environmental driver and the ecosystem response. Researchers sometimes substitute spatial data for temporal data in their models, with the assumption that the spatial relationship between the environmental driver and the response variable can be used as a proxy for the temporal relationship. Collecting data with large spatial coverage over a short period of time allows researchers to increase the range and quantity of data points used to determine the relationship between an environmental driver and the response of the ecological variable of interest without the constraint of waiting for many years of data to be collected
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
