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  5. We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values formore »the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.« less
  6. Many coastal foundation plant species thrive across a range of environmental conditions, often displaying dramatic phenotypic variation in response to environmental variation. We characterized the response of propagules from six populations of the foundation species Rhizophora mangle L. to full factorial combinations of two levels of salinity (15 ppt and 45 ppt) reflecting the range of salinity measured in the field populations, and two levels of nitrogen (N; no addition and amended at approximately 3 mg N per pot each week) equivalent to comparing ambient N to a rate of addition of 75 kg per hectare per year. The response to increasing salinity included significant changes, i.e., phenotypic plasticity, in succulence and root to shoot biomass allocation. Propagules also showed plasticity in maximum photosynthetic rate and root to shoot allocation in response to N amendment, but the responses depended on the level of salinity and varied by population of origin. In addition, propagules from different populations and maternal families within populations differed in survival and all traits measured except photosynthesis. Variation in phenotypes, phenotypic plasticity and propagule survival within and among R. mangle populations may contribute to adaptation to a complex mosaic of environmental conditions and response to climate change.