Spatial variogram estimation from temporally aggregated seabird count data

Seabird abundance is an important indicator for assessing impact of human activities on the marine environment. However, data collection at sea is time consuming and surveys are carried out over several consecutive days for efficiency reasons. This study investigates the validity of aggregating those data over time to estimate a spatial variogram that is representative for spatial correlation in species abundance. For this purpose we simulate four-day surveys of seabird count data that contain spatial and temporal correlation arising from temporal changes in the spatial pattern of environmental conditions. Estimates of the aggregated spatial variogram are compared to a variogram that would arise when data were collected over a single day. The study reveals that, under changing environmental conditions over surveys days, aggregating data over a four-day survey increases both the non-spatial variation in the data and the scale of spatial correlation in seabird data. Next, the effect of using an aggregated variogram on the statistical power to test the significance of an impact is investigated. The impact concerns a case of establishing an offshore wind farm resulting in seabird displacement. The study shows that both overestimation and underestimation of statistical power occurs, with power estimates differing up to a factor of two. We conclude that the spatial variation in seabird abundance can be misrepresented by using temporally aggregated data. In impact studies, such misrepresentation can lead to erroneous assessments of the ability to detect impact.     

Spatial factors affecting statistical power in testing marine fauna displacement

Impacts of offshore wind farms on marine fauna are largely unknown. Therefore, one commonly adheres to the precautionary principle, which states that one shall take action to avoid potentially damaging impacts on marine ecosystems, even when full scientific certainty is lacking. We implement this principle by means of a statistical power analysis including spatial factors. Implementation is based on geostatistical simulations, accommodating for zero-inflation in species data. We investigate scenarios in which an impact assessment still has to be carried out. Our results show that the environmental conditions at the time of the survey is the most influential factor on power. This is followed by survey effort and species abundance in the reference situation. Spatial dependence in species numbers at local scales affects power, but its effect is smaller for the scenarios investigated. Our findings can be used to improve effectiveness of the economical investment for monitoring surveys. In addition, unnecessary extra survey effort, and related costs, can be avoided when spatial dependence in species abundance is present and no improvement on power is achieved.