Incorporating fine-scale environmental heterogeneity into broad-extent models

Area of Science:

• Ecology
• Spatial Analysis
• Environmental Science

Background:

• Ecological research often faces challenges with predictor variables measured at finer resolutions than the ecological response.
• Existing data aggregation methods can lead to information loss regarding fine-grain spatial heterogeneity.

Purpose of the Study:

• To present a novel data aggregation method that minimizes information loss from fine-grain spatial structure in environmental heterogeneity.
• To provide a tool for integrating variables acting at different scales into ecological models.

Main Methods:

• Define analysis scales: predictor grain, response grain, and scale-of-effect.
• Utilize a moving window to calculate environmental variability at the scale-of-effect.
• Aggregate variability calculations to the response grain resolution.

Main Results:

• Simulations demonstrate that the method captures spatial structure information lost in direct aggregation.
• The approach is particularly effective in landscapes with spatial autocorrelation and small scales-of-effect relative to response grain.
• A case study successfully modeled the interactive effects of land cover diversity and temperature on Eurasian jay abundance.

Conclusions:

• The developed data aggregation method, available as the 'grainchanger' R package, effectively integrates multi-scale variables with limited information loss.
• This method has broad applications in spatial analyses, enabling better estimation of interacting drivers and examination of scale-dependent effects on biodiversity.
• It is crucial for predicting the impacts of global environmental change by accounting for varying scales of ecological drivers.