Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.

Metapopulation dynamics - patch occupancy, colonization and extinction - are the result of complex processes at both local (e.g. environmental conditions) and regional (e.g. spatial arrangement of habitat patches) scales. A large body of work has focused on habitat patch area and connectivity (area-isolation paradigm). However, these approaches often do not incorporate local environmental conditions or fully address how the spatial arrangement of habitat patches (and resulting connectivity) can influence metapopulation dynamics. Here, we utilize long-term data on a classic metapopulation system - the Glanville fritillary butterfly occupying a set of dry meadows and pastures in the angstrom land islands - to investigate the relative roles of local environmental conditions, geographic space and connectivity in capturing patch occupancy, colonization and extinction. We defined connectivity using traditional measures as well as graph-theoretic measures of centrality. Using boosted regression tree models, we find roughly comparable model performance among models trained on environmental conditions, geographic space or patch centrality. In models containing all of the covariates, we find strong and consistent evidence for the roles of resource abundance, longitude and centrality (i.e. connectivity) in predicting habitat patch occupancy and colonization, while patch centrality (connectivity) was relatively unimportant for predicting extinction. Relative variable importance did not change when geographic coordinates were not considered and models underwent spatially stratified cross-validation. Together, this suggests that the combination of regional-scale connectivity measures and local-scale environmental conditions is important for predicting metapopulation dynamics and that a stronger integration of ideas from network theory may provide insight into metapopulation processes.