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Macroevolutionary changes such as variation in habitat use or diet are often associated with convergent, adaptive changes in morphology. However, it is still unclear how small-scale morphological variation at the population level can drive shifts in ecology as observed at a macroevolutionary scale. Here, we address this question by investigating how variation in cranial form and feeding mechanics relate to rapid changes in diet in an insular lizard (Podarcis siculus) after experimental introduction into a new environment. We first quantified differences in the skull shape and jaw muscle architecture between the source and introduced population using three-dimensional geometric morphometrics and dissections. Next, we tested the impact of the observed variation in morphology on the mechanical performance of the masticatory system using computer-based biomechanical simulation techniques. Our results show that small differences in shape, combined with variation in muscle architecture, can result in significant differences in performance allowing access to novel trophic resources. The confrontation of these data with the already described macroevolutionary relationships between cranial form and function in these insular lizards provides insights into how selection can, over relatively short time scales, drive major changes in ecology through its impact on mechanical performance.

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.