Climate-driven regime shifts in a mangrove-salt marsh ecotone over the past 250 years.

Climate change is driving the tropicalization of temperate ecosystems by shifting the range edges of numerous species poleward. Over the past few decades, mangroves have rapidly displaced salt marshes near multiple poleward mangrove range limits, including in northeast Florida. It is uncertain whether such mangrove expansions are due to anthropogenic climate change or natural climate variability. We combined historical accounts from books, personal journals, scientific articles, logbooks, photographs, and maps with climate data to show that the current ecotone between mangroves and salt marshes in northeast Florida has shifted between mangrove and salt marsh dominance at least 6 times between the late 1700s and 2017 due to decadal-scale fluctuations in the frequency and intensity of extreme cold events. Model projections of daily minimum temperature from 2000 through 2100 indicate an increase in annual minimum temperature by 0.5 °C/decade. Thus, although recent mangrove range expansion should indeed be placed into a broader historical context of an oscillating system, climate projections suggest that the recent trend may represent a more permanent regime shift due to the effects of climate change

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales

Evaluating regional resiliency of coastal wetlands to sea level rise through hypsometry-based modeling.

Sea level rise (SLR) threatens coastal wetlands worldwide, yet the fate of individual wetlands will vary based on local topography, wetland morphology, sediment dynamics, hydrologic processes, and plant-mediated feedbacks. Local variability in these factors makes it difficult to predict SLR effects across wetlands or to develop a holistic regional perspective on SLR response for a diversity of wetland types. To improve regional predictions of SLR impacts to coastal wetlands, we developed a model that addresses the scale-dependent factors controlling SLR response and accommodates different levels of data availability. The model quantifies SLR-driven habitat conversion within wetlands across a region by predicting changes in individual wetland hypsometry. This standardized approach can be applied to all wetlands in a region regardless of data availability, making it ideal for modeling SLR response across a range of scales. Our model was applied to 105 wetlands in southern California that spanned a broad range of typology and data availability. Our findings suggest that if wetlands are confined to their current extents, the region will lose 12% of marsh habitats (vegetated marsh and unvegetated flats) with 0.6 m of SLR (projected for 2050) and 48% with 1.7 m of SLR (projected for 2100). Habitat conversion was more drastic in wetlands with larger proportions of marsh habitats relative to subtidal habitats and occurred more rapidly in small lagoons relative to larger sites. Our assessment can inform management of coastal wetland vulnerability, improve understanding of the SLR drivers relevant to individual wetlands, and highlight significant data gaps that impede SLR response modeling across spatial scales. This approach augments regional SLR assessments by considering spatial variability in SLR response drivers, addressing data gaps, and accommodating wetland diversity, which will provide greater insights into regional SLR response that are relevant to coastal management and restoration efforts

Evaluating regional resiliency of coastal wetlands to sea level rise through hypsometry-based modeling.

Sea level rise (SLR) threatens coastal wetlands worldwide, yet the fate of individual wetlands will vary based on local topography, wetland morphology, sediment dynamics, hydrologic processes, and plant-mediated feedbacks. Local variability in these factors makes it difficult to predict SLR effects across wetlands or to develop a holistic regional perspective on SLR response for a diversity of wetland types. To improve regional predictions of SLR impacts to coastal wetlands, we developed a model that addresses the scale-dependent factors controlling SLR response and accommodates different levels of data availability. The model quantifies SLR-driven habitat conversion within wetlands across a region by predicting changes in individual wetland hypsometry. This standardized approach can be applied to all wetlands in a region regardless of data availability, making it ideal for modeling SLR response across a range of scales. Our model was applied to 105 wetlands in southern California that spanned a broad range of typology and data availability. Our findings suggest that if wetlands are confined to their current extents, the region will lose 12% of marsh habitats (vegetated marsh and unvegetated flats) with 0.6 m of SLR (projected for 2050) and 48% with 1.7 m of SLR (projected for 2100). Habitat conversion was more drastic in wetlands with larger proportions of marsh habitats relative to subtidal habitats and occurred more rapidly in small lagoons relative to larger sites. Our assessment can inform management of coastal wetland vulnerability, improve understanding of the SLR drivers relevant to individual wetlands, and highlight significant data gaps that impede SLR response modeling across spatial scales. This approach augments regional SLR assessments by considering spatial variability in SLR response drivers, addressing data gaps, and accommodating wetland diversity, which will provide greater insights into regional SLR response that are relevant to coastal management and restoration efforts

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Abstract Coastal wetland biomass is an important indicator of wetland productivity, carbon storage, health, and vulnerability to climate change. The ability to estimate aboveground biomass (AGB) in wetlands at ecologically relevant scales is complicated by the spatial variability inherent to patterns in wetland vegetation and the biogeomorphic processes that help create them. Remote sensing provides an approach for mapping wetland biomass, but the spatial resolutions of satellite and airborne imagery often constrain the types of ecological patterns and processes that can be detected. Unmanned Aerial Vehicles (UAVs) have previously been used to capture fine‐scale (≤1 m) variability in AGB in coastal wetland settings. However, it remains unclear if a UAV approach to estimating wetland biomass is transferrable across diverse wetland sites or how it compares to commonly used satellite‐based approaches. Here, we test the capabilities of UAVs in remotely quantifying AGB and compare biomass estimation using UAV and Landsat satellite imagery (30 m resolution) in several wetland sites in Southern California. Field surveys highlight significant spatial variability in wetland plant community AGB and height that influence remote biomass estimation. Relationships between UAV vegetation indices and AGB were site‐specific and influenced by vegetation types. Biomass estimation using UAVs (r2 = 0.40, RMSE = 534.6 g m−2) showed better correlation with NDVI than a Landsat‐based approach (r2 = 0.26, RMSE = 596.8 g m−2). We found combining high‐resolution UAV AGB maps and Landsat NDVI to develop AGB models showed the highest correlation (r2 = 0.45, RMSE = 659.7 g m−2) and provided additional spatial information to aid scaling field data to satellite imagery. Overall, UAVs captured more spatial complexity in aboveground biomass at finer scales than is possible with moderate‐resolution Landsat pixels, indicating that UAVs can be used to characterize patterns of within‐marsh variability resulting from local‐scale (≤ 100s of meters) ecological processes