Making virtual species less virtual by reverse engineering of spatiotemporal ecological models

Abstract

AbstractThe virtual species (VS) and virtual ecologist (VE) approaches are useful tools that allow testing different methodological aspects of species distribution modelling. However, methods used to generate VS so far lack solutions that can ensure a high degree of biological realism, taking into account spatial and temporal variability of population densities.We have developed a method for generating dynamic VS that can reconstruct their living prototypes in a realistic way. The framework consists of fitting a spatiotemporal model to real abundance data, generating a VS population from that model over the entire study area and spanning the whole study period, calibrating the VS, and obtaining the VE data by sampling from the VS. The effectiveness of the developed approach has been illustrated by data from large‐scale and long‐term bird abundance monitoring, using the whinchatSaxicola rubetraas a study system. We evaluated how well the spatiotemporal model can reconstruct the ‘true’ system by comparing response curves and population trends between those used to generate the VS (i.e. what constitutes the ‘truth’) and those estimated from the replicated instances of VE data. In addition, we performed a sensitivity analysis to test how the varying sampling effort affects the accuracy of trend estimation.The synthetic VE data thoroughly reconstructed the real monitoring data. Response curves from generalized additive mixed models (GAMMs), fitted to these two types of data, showed high concordance, as indicated by the 95% confidence intervals of coverage probability of 87.7%–99.8% (mean 96.9%). The population trend estimated from the VE data accurately reconstructed the ‘true’ trend calculated from VS (coverage probability: 82.3%).The proposed method for generating VS and VE data by reverse engineering of the spatiotemporal ecological model reproduces well the properties of the original system, substantially increasing the ecological realism of simulation results. The method may have further applications in evaluating various modelling techniques used to study species range dynamics, where real‐world properties are of particular importance, like conservation and invasion biology or climate change impact assessment.