Geology topics

Laura Feher

Publications and source records attributed to Laura Feher.

22 records · Page 2Linked to original sources

USGS2019

Temperature thresholds for black mangrove (Avicennia germinans) freeze damage, mortality, and recovery in North America: Refining tipping points for range expansion in a warming climate

Near the tropical‐temperate transition zone, warming winter temperatures are expected to facilitate the poleward range expansion of freeze‐sensitive tropical organisms. In coastal wetlands of eastern and central North America, freeze‐sensitive woody plants (mangroves) are expected to expand northward into regions currently dominated by freeze‐tolerant herbaceous salt marsh plants. To advance understanding of mangrove range expansion, there is a need to refine temperature thresholds for mangrove freeze damage, mortality, and recovery. We integrated data from 38 sites spread across the mangrove range edge in the Gulf of Mexico and Atlantic coasts of North America, including data from a regional collaborative network — the Mangrove Migration Network. In 2018, an extreme freeze event affected 60% of these sites, with minimum temperatures ranging from 0 to ‐7°C. We used temperature and vegetation data from before and after the freeze to quantify temperature thresholds for leaf damage, mortality, and biomass recovery of the black mangrove (Avicennia germinans) — the most freeze‐tolerant mangrove species in North America. For A. germinans individuals near their northern range limit, our results indicate that temperature thresholds for leaf damage are close to ‐4°C, but temperature thresholds for mortality are closer to ‐7°C. Thresholds are expected to be warmer for more southern A. germinans individuals and for the other two common mangrove species in the region (Laguncularia racemosa and Rhizophora mangle). Regenerative buds allowed A. germinans to resprout and recover quickly from aboveground freeze damage. Hence, biomass recovery levels during the first post‐freeze growing season were 90, 78, 62, and 45% for temperatures of ‐4, ‐5, ‐6, and ‐7°C, respectively. Due to a combination of vigorous resprouting and new recruitment from propagules, we expect full recovery at most sites within 1‐3 years, assuming no further freeze events. Synthesis To improve predictions of tropical range expansion in response to climate change, there is a need to better understand tropical species’ responses to winter temperature extremes. Collectively, our results refine temperature thresholds for A. germinans freeze damage, mortality, and recovery, which can improve predictions of mangrove range expansion and coastal wetland ecological transformations in a warming climate.

Florida, Mississippi, Louisiana, Texas↗

USGS2019

Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss

Background and Aims Coastal wetlands have evolved to withstand stressful abiotic conditions through the maintenance of hydrologic feedbacks among vegetation production and flooding. However, disruption of these feedbacks can lead to ecosystem collapse, or a regime shift from vegetated wetland to open water. To prevent the loss of critical coastal wetland habitat, we must improve understanding of the abiotic-biotic linkages among flooding and wetland stability. The aim of this research was to identify characteristic landscape patterns and thresholds of wetland degradation that can be used to identify areas of vulnerability, reduce flooding threats, and improve habitat quality. Methods We measured local- and landscape-scale responses of coastal wetland vegetation to flooding stress in healthy and degrading coastal wetlands. We hypothesized that conversion of Spartina patens wetlands to open water could be defined by a distinct change in landscape configuration pattern, and that this change would occur at a discrete elevation threshold. Key Results Despite similarities in total land and water cover, we observed differences in the landscape configuration of vegetated and open water pixels in healthy and degrading wetlands. Healthy wetlands were more aggregated, and degrading wetlands were more fragmented. Generally, greater aggregation was associated with higher wetland elevation and better drainage, compared to fragmented wetlands, which had lower elevation and poor drainage. The relationship between vegetation cover and elevation was non-linear, and the conversion from vegetated wetland to open water occurred beyond an elevation threshold of hydrologic stress. Conclusions The elevation threshold defined a transition zone where healthy, aggregated, wetland converted to a degrading, fragmented, wetland beyond an elevation threshold of 0.09 m NAVD88 (0.27 m MSL), and complete conversion to open water occurred beyond 0.03 m NAVD88 (0.21 m MSL). This work illustrates that changes in landscape configuration can be used as an indicator of wetland loss, with specific elevation thresholds to inform restoration and conservation planning to maximize wetland stability in anticipation of flooding threats.

Louisiana, Texas↗

USGS2019

Modeling barrier island habitats using landscape position information

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

Remote Sensing↗

USGS2019

Advancing barrier island habitat mapping using landscape position information

Barrier islands are dynamic ecosystems that change gradually from coastal processes, including currents and tides, and rapidly from episodic events, such as storms. These islands provide many important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. Habitat maps, developed by scientists, provide a critical tool for monitoring changes to these dynamic ecosystems. Barrier island monitoring often requires custom habitat maps due to several factors, including island size and the classification of unique geomorphology-based habitats, such as beach, dune, and barrier flats. In this study, we reviewed barrier-island-specific habitat mapping efforts and highlighted common habitat class types, source data, and mapping approaches. We also developed a framework for mapping geomorphology-based barrier island habitats using a rule-based, geographic object-based image analysis approach, which included the use of field data, tide data, high-resolution orthophotography, and lidar data. This framework integrates several barrier island mapping advancements with regard to the use of landscape position information for automated dune extraction and the use of Monte Carlo analyses for the treatment of elevation uncertainty for elevation-dependent habitats. Specifically, we used the uncertainty analyses to refine automated dune delineation based on elevation relative to extreme storm water levels and to increase the accuracy of intertidal and supratidal/upland habitat delineation. We found that dune extraction results were enhanced when elevation relative to storm water levels and visual interpretation were also applied. This framework could also be applied to beach–dune systems found along a mainland.

Alabama↗