Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (\u3e10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (\u3e1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones

Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma

Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10?m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760?ha), triggered by Irma. We found evidence that the combination of low elevation (median?=?9.4?cm?asl), storm surge water levels (>1.4?m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.ECU Open Access Publishing Support Fun

Mapping the extent of mangrove ecosystem degradation by integrating an ecological conceptual model with satellite data

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.</jats:p

Species traits and geomorphic setting as drivers of global soil carbon stocks in seagrass meadows

Our knowledge of the factors that can influence the stock of organic carbon (OC) that is stored in the soil of seagrass meadows is evolving, and several causal effects have been used to explain the variation of stocks observed at local to national scales. To gain a global-scale appreciation of the drivers that cause variation in soil OC stocks, we compiled data on published species-specific traits and OC stocks from monospecific and mixed meadows at multiple geomorphological settings. Species identity was recognized as an influential driver of soil OC stocks, despite their large intraspecific variation. The most important seagrass species traits associated with OC stocks were the number of leaves per seagrass shoot, belowground biomass, leaf lifespan, aboveground biomass, leaf lignin, leaf breaking force and leaf OC plus the coastal geomorphology of the area, particularly for lagoon environments. A revised estimate of the global average soil OC stock to 20 cm depth of 15.4 Mg C ha−1 is lower than previously reported. The largest stocks were still recorded in Mediterranean seagrass meadows. Our results specifically identify Posidonia oceanica from the Mediterranean and, more generally, large and persistent species as key in providing climate regulation services, and as priority species for conservation for this specific ecosystem service

A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation

Mangrove forests provide many ecosystem services but are among the world's most threatened ecosystems. Mangroves vary substantially according to their geomorphic and sedimentary setting; while several conceptual frameworks describe these settings, their spatial distribution has not been quantified. Here, we present a new global mangrove biophysical typology and show that, based on their 2016 extent, 40.5% (54,972 km2) of mangrove systems were deltaic, 27.5% (37,411 km2) were estuarine and 21.0% (28,493 km2) were open coast, with lagoonal mangroves the least abundant (11.0%, 14,993 km2). Mangroves were also classified based on their sedimentary setting, with carbonate mangroves being less abundant than terrigenous, representing just 9.6% of global coverage. Our typology provides a basis for future research to incorporate geomorphic and sedimentary setting in analyses. We present two examples of such applications. Firstly, based on change in extent between 1996 and 2016, we show while all types exhibited considerable declines in area, losses of lagoonal mangroves (- 6.9%) were nearly twice that of other types. Secondly, we quantify differences in aboveground biomass between mangroves of different types, with it being significantly lower in lagoonal mangroves. Overall, our biophysical typology provides a baseline for assessing restoration potential and for quantifying mangrove ecosystem service provision

Species traits and geomorphic setting as drivers of global soil carbon stocks in seagrass meadows

Unidad de excelencia María de Maeztu CEX2019-000940-MOur knowledge of the factors that can influence the stock of organic carbon (OC) that is stored in the soil of seagrass meadows is evolving, and several causal effects have been used to explain the variation of stocks observed at local to national scales. To gain a global-scale appreciation of the drivers that cause variation in soil OC stocks, we compiled data on published species-specific traits and OC stocks from monospecific and mixed meadows at multiple geomorphological settings. Species identity was recognized as an influential driver of soil OC stocks, despite their large intraspecific variation. The most important seagrass species traits associated with OC stocks were the number of leaves per seagrass shoot, belowground biomass, leaf lifespan, aboveground biomass, leaf lignin, leaf breaking force and leaf OC plus the coastal geomorphology of the area, particularly for lagoon environments. A revised estimate of the global average soil OC stock to 20 cm depth of 15.4 Mg C ha−1 is lower than previously reported. The largest stocks were still recorded in Mediterranean seagrass meadows. Our results specifically identify Posidonia oceanica from the Mediterranean and, more generally, large and persistent species as key in providing climate regulation services, and as priority species for conservation for this specific ecosystem service

Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on the ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of largescale mangrove research

Harnessing big data to support the conservation and rehabilitation of mangrove forests globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research. This Perspective reviews the role of global-scale research in stimulating policy action and on-the-ground conservation for mangrove ecosystems. We outline the current state of knowledge in terms of global analyses and examine the challenge of translating this research in action

Development and testing of the Measure of Innovation-Specific Implementation Intentions (MISII) using Rasch measurement theory

© 2018 The Author(s). Background: Implementation is proposed to be a multiphase, multilevel process. After a period of exploration, an adoption decision is made, typically at the upper management or policy level. Nevertheless, movement through each of the subsequent phases of the implementation process involves clinicians or providers at the individual level to adopt the innovation and then change their behavior to use/deliver the innovation. Multiple behavioral change theories propose that intentions are a critical determinant of implementation behavior. However, there is a need for the development and testing of pragmatic measures of providers' intentions to use a specific innovation or evidence-based practice (EBP). Methods: Nine items were developed to assess providers' intentions to use a specific innovation or EBP. Motivational interviewing was the EBP in the study. Items were administered, as part of larger survey, to 179 providers across 38 substance use disorder treatment (SUDT) programs within five agencies in California, USA. Rasch analysis was conducted using RUMM2030 software to assess the items, their overall fit to the Rasch model, the response scale used, individual item fit, differential item functioning (DIF), and person separation. Results: Following a stepwise process, the scale was reduced from nine items to three items to increase the feasibility and acceptability of the scale while maintaining suitable psychometric properties. The three-item unidimensional scale showed good person separation (PSI =.872), no disordering of thresholds, and no evidence of uniform or non-uniform DIF. Rasch analysis supported the viability of the scale as a measure of implementation intentions. Conclusions: The Measure of Innovation-Specific Implementation Intentions (MISII) is a sound measure of providers' intentions to use a specific innovation or EBP. Future evaluation of convergent, divergent, and predictive validity are needed. The study also demonstrates the value of Rasch analysis for testing the psychometric properties of pragmatic implementation measures