Effects of temporal bias on the assessment of an ecological perturbation: a case study of the Prestige oil spill

Abstract

The impacts of unpredictable ecological perturbations are often assessed via measurements of environmental change only after the event has occurred. Temporal series of satellite images provide a cost-effective way to gather information before ecological perturbations occur. However, in previous studies, the disturbances have neither been always centred in time in the series of the focal environmental variable nor has the relevance of the temporal coverage been explicitly tested through factorial designs. In this study, we manipulated the temporal coverage and the position of the disturbance event in the temporal series to examine whether and how the assessment is affected. Specifically, we tested the effect of the Prestige oil spill on monthly sea chlorophyll concentration and net primary productivity along the north-western Spanish coast. We designed planned comparisons through factorial analyses to test two alternative hypotheses: (1) the spill has negative consequences on phytoplankton activity and/or abundance due to physiological constraints or (2) it has positive consequences on phytoplankton abundance as a result of changes in biotic interactions. The relevance of the statistical effects was critically dependent on the temporal coverage and the position of the spill event in the temporal series. Short periods (three years) were insufficient to cover the range of variability even if the disturbance was centred in the time series. Similarly, results from longer time series (up to eight years) in which the event was temporally biased (at the beginning of the time series) also differed from those that were centred in the entire time window. Temporal series for the study of ecological impacts should be as long as necessary to encompass the temporal variability of the study systems (up to nine years in our study case), and the disturbance event should be centred in the time series to reduce potential spurious effects of temporal autocorrelation. However, our results revealed that each one of these requirements alone was not sufficient to encompass all of the natural variability, and thus both requirements should be met. For impact assessments we encourage the use of unbiased satellite data series to complement in situ measurements.

Use of satellite images to characterize the spatio-temporal dynamics of primary productivity in hotspots of endemic Iberian butterflies

“…Changes can be studied from a global scale to more local scales and from a geological time scale to shorter ecological time scales (e.g., the past few decades) in which society can perceive such changes directly (Wilbanks and Kates, 1999). It has been argued that variations in abiotic factors (e.g., climate change) have a greater impact at large scales (Pearson and Dawson, 2003) and that biotic factors can even reverse their spatio-temporal dynamics depending on the time frame and spatial scale considered (Piao et al, 2011;Wang et al, 2011;Gao et al, 2012a,b;Aragón et al, 2015). Over the past several decades, analyses of spatio-temporal trends in a global change context have often only considered specific abiotic variables, such as temperature or precipitation, because data on these variables were the easiest to obtain.…”

“…However, recent reviews on the subject have argued that the use of satellite images for conservation purposes is still far from reaching its full potential (Cabello et al, 2012;Pettorelli et al, 2014;Rose et al, 2015). The vegetation indices generated from satellite imagery can be useful in detecting the indirect effects of climate change, impacts of ecological disturbances, or changes in land use (Hansen et al, 2000;Lucht et al, 2002;Aragón et al, 2015).…”