Spurious correlations and inference in landscape genetics

Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causal-modelling with partial Mantel tests in individual-based landscape genetic analysis. We used a spatially explicit simulation model to generate genetic data across a spatially distributed population as functions of several alternative gene flow processes. This allowed us to stipulate the actual process that is in action, enabling formal evaluation of the strength of spurious correlations with incorrect models. We evaluated the degree to which naive correlational approaches can lead to incorrect attribution of the driver of observed genetic structure. Second, we evaluated the power of causal modelling with partial Mantel tests on resistance gradients to correctly identify the explanatory model and reject incorrect alternative models. Third, we evaluated how rapidly after the landscape genetic process is initiated that we are able to reliably detect the effect of the correct model and reject the incorrect models. Our analyses suggest that simple correlational analyses between genetic data and proposed explanatory models produce strong spurious correlations, which lead to incorrect inferences. We found that causal modelling was extremely effective at rejecting incorrect explanations and correctly identifying the true causal process. We propose a generalized framework for landscape genetics based on analysis of the spatial genetic relationships among individual organisms relative to alternative hypotheses that define functional relationships between landscape features and spatial population processes.