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BackgroundUnderstanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients.Methodology/Principal FindingsWe applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation<750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation>950 mm).Conclusions/SignificanceWe quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.

Interactions between multiple anthropogenic environmental changes can drive non-additive effects in ecological systems, and the non-additive effects can in turn be amplified or dampened by spatial covariation among environmental changes. We investigated the combined effects of night-time warming and light pollution on pea aphids and two predatory ladybeetle species. As expected, neither night-time warming nor light pollution changed the suppression of aphids by the ladybeetle species that forages effectively in darkness. However, for the more-visual predator, warming and light had non-additive effects in which together they caused much lower aphid abundances. These results are particularly relevant for agriculture near urban areas that experience both light pollution and warming from urban heat islands. Because warming and light pollution can have non-additive effects, predicting their possible combined consequences over broad spatial scales requires knowing how they co-occur. We found that night-time temperature change since 1949 covaried positively with light pollution, which has the potential to increase their non-additive effects on pea aphid control by 70% in US alfalfa. Our results highlight the importance of non-additive effects of multiple environmental factors on species and food webs, especially when these factors co-occur.

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