Antecedent Drought in Mangrove Response and Recovery to Hurricane Irma

Overview

Mangroves are important forests that provide numerous ecosystem and economic services. Storms place pressure on mangroves, and when compounded with disturbances such as drought, potentially decrease ecosystem resilience. However, little is currently known about the impact of drought on hurricane damage and associated post-storm recovery. Airborne LiDAR measurements, satellite imagery, modeled wind speeds, and drought data was used to perform high resolution mangrove mapping across the Caribbean and characterize coastline vegetation damage, revealing over 80,000 hectares of dieback within the over 1 million hectares of mangroves identified. Pixel-based time series modeling revealed drought caused previous overestimation of hurricane damage. We found that mangrove dieback in 2017 actually occurred in two waves, the first driven by drought and the second caused by storm. The initial, drought-driven dieback accounted for nearly 7% of all 2017 damage. The second, hurricane-driven dieback was more widespread and severe, causing higher levels of immediate and long-term damage, and affecting taller trees. Mangroves damaged in the second wave were on average 2.7 meters taller than mangroves damaged in the first wave. A mangrove drought history index is proposed that successfully captures hydrological vulnerability due to current and historical drought conditions. Drought is demonstrated to decrease resilience and lower recovery rates, prolonging recovery times. Although mangroves are known to be resilient to hurricanes, the results suggest the increasing frequency and intensity of drought spells within the Caribbean present an opportunity for cross-disturbance damage exacerbation, negatively implicating mangrove response. MISSISSIPPI Identifying Genetic Biomarkers for Essential Tremor Diagnosis Nicholas Djedjos Mississippi School for Mathematics and Science, Columbus, MS Nearly seven million individuals in the U.S. have Essential Tremor (ET), making it one of the most common neurological disorders. Current ET research aligns it with a Purkinje cell disorder in the cerebellum, the motor control center of the brain. ET is associated with life-threatening neurological diseases such as Parkinson’s and dementia, yet still remains understudied. This study uses the raw RNA-seq data from 55 post-mortem cerebellum samples to understand the genetic background of ET. The genetic data were used to develop machine learning models for prognosis and further identification of ET genetic biomarkers. Differential Gene Expression (DGE) identified 86 differentially expressed transcribed gene transcripts (p <0.001, FDR < 0.25). The gene transcripts were then input into Gene Set Enrichment Analysis (GSEA) where five pathways were identified as dysregulated after comparisons with the Hallmark and KEGG gene sets: Fatty Acid Metabolism, Cholesterol Metabolism, Ribosome, Axonal Guidance, and Parkinson’s Disease. The gene transcripts were also input in Random Forest and Logistic Regression models for further analyses. After filtering the 86 genes to 32 with Random Forest optimization, the classification model predicted ET and control accurately 85% of the time. Logistic Regression was utilized to analyze the 32 genes individually, and 8 genes had a higher accuracy than 80%: SFTPA2, NLRP14, PLCD1, SCRG1, ANKZF1, INPPFD, EVA1C, and BTN3A1Identifying the aforementioned biomarkers both advanced and corroborated with existing scientific literature and could be used to diagnose ET. The addition of machine learning models with higher statistical power and a larger dataset would strengthen the genetic findings.

Competition history

• JSHS 2022 Category not listed

Resources

• National JSHS abstract book ↗

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