Amanda M. Schwantes
Duke University
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Publication
Featured researches published by Amanda M. Schwantes.
Trends in Plant Science | 2015
Nate G. McDowell; Pieter S. A. Beck; Jeffrey Q. Chambers; Chandana Gangodagamage; Jeffrey A. Hicke; Cho-ying Huang; Robert E. Kennedy; Dan J. Krofcheck; Marcy E. Litvak; Arjan J. H. Meddens; Jordan Muss; Robinson I. Negrón-Juárez; Changhui Peng; Amanda M. Schwantes; Jennifer J. Swenson; Louis James Vernon; A. Park Williams; Chonggang Xu; Maosheng Zhao; Steven W. Running; Craig D. Allen
Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.
Ecology | 2013
Katherine L. Tully; Tana E. Wood; Amanda M. Schwantes; Deborah Lawrence
The removal of nutrients from senescing tissues, nutrient resorption, is a key strategy for conserving nutrients in plants. However, our understanding of what drives patterns of nutrient resorption in tropical trees is limited. We examined the effects of nutrient sources (stand-level and tree-level soil fertility) and sinks (reproductive effort) on nitrogen (N) and phosphorus (P) resorption. We evaluated resorption efficiency (percentage of original nutrients removed during senescence) and resorption proficiency (indicated by senesced-leaf nutrient concentrations) in a symbiotic N-fixing tree species, Pentaclethra macroloba, common to tropical forests in Costa Rica. Although tree-level soil P alone did not drive patterns in nutrient resorption, P efficiency and proficiency declined with increasing tree-level soil P when reproductive status was also considered. Nutrient resorption declined with increasing tree-level soil P in trees that were actively fruiting or that experienced high seedfall the year prio...
Remote Sensing | 2018
Patrick C. Gray; Justin T. Ridge; Sarah K. Poulin; Alexander C. Seymour; Amanda M. Schwantes; Jennifer J. Swenson; David W. Johnston
Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView’s fine spatial resolution versus RapidEye’s temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.
New Phytologist | 2018
Amanda M. Schwantes; Anthony J. Parolari; Jennifer J. Swenson; Daniel M. Johnson; Jean-Christophe Domec; Robert B. Jackson; Norman Pelak; Amilcare Porporato
As climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability varies regionally and locally through landscape position. Also, most models used in forecasting forest responses to heat and drought do not incorporate relevant spatial processes. In order to improve spatial predictions of tree vulnerability, we employed a nonlinear stochastic model of soil moisture dynamics accounting for landscape differences in aspect, topography and soils. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei, and projected future dynamic water stress through the 21st century. Modeled dynamic water stress tracked spatial patterns of remotely sensed drought-induced canopy loss. Accuracy in predicting drought-impacted stands increased from 60%, accounting for spatially variable soil conditions, to 72% when also including lateral redistribution of water and radiation/temperature effects attributable to aspect. Our analysis also suggests that dynamic water stress will increase through the 21st century, with trees persisting at only selected microsites. Favorable microsites/refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of an heterogeneous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.
Remote Sensing of Environment | 2016
Amanda M. Schwantes; Jennifer J. Swenson; Robert B. Jackson
Plant Cell and Environment | 2018
Daniel M. Johnson; Jean-Christophe Domec; Z. Carter Berry; Amanda M. Schwantes; Katherine A. McCulloh; David R. Woodruff; H. Wayne Polley; Remí Wortemann; Jennifer J. Swenson; D. Scott Mackay; Nate G. McDowell; Robert B. Jackson
Global Change Biology | 2017
Amanda M. Schwantes; Jennifer J. Swenson; Mariano González-Roglich; Daniel M. Johnson; Jean-Christophe Domec; Robert B. Jackson
Environmental Research Letters | 2017
Kemen G. Austin; Mariano González-Roglich; Danica Schaffer-Smith; Amanda M. Schwantes; Jennifer J. Swenson
Archive | 2018
Daniel M. Johnson; J-C Domec; Z. Carter Berry; Amanda M. Schwantes; Katherine A. McCulloh; Woodruff; H. Wayne Polley; Remí Wortemann; Jennifer J. Swenson; D. Scott Mackay; Nate G. McDowell; Robert B. Jackson
Environmental Research Letters | 2017
Kemen G. Austin; Mariano González-Roglich; Danica Schaffer-Smith; Amanda M. Schwantes; Jennifer J. Swenson