Mangrove dieback during fluctuating sea levels

Recent evidence indicates that climate change and intensification of the El Niño Southern Oscillation (ENSO) has increased variation in sea level. Although widespread impacts on intertidal ecosystems are anticipated to arise from the sea level seesaw associated with climate change, none have yet been demonstrated. Intertidal ecosystems, including mangrove forests are among those ecosystems that are highly vulnerable to sea level rise, but they may also be vulnerable to sea level variability and extreme low sea level events. During 16 years of monitoring of a mangrove forest in Mangrove Bay in north Western Australia, we documented two forest dieback events, the most recent one being coincident with the large-scale dieback of mangroves in the Gulf of Carpentaria in northern Australia. Diebacks in Mangrove Bay were coincident with periods of very low sea level, which were associated with increased soil salinization of 20–30% above pre-event levels, leading to canopy loss, reduced Normalized Difference Vegetation Index (NDVI) and reduced recruitment. Our study indicates that an intensification of ENSO will have negative effects on some mangrove forests in parts of the Indo-Pacific that will exacerbate other pressures.Data described in this paper are available in Supplementary Table S1. Funding was provided by the Johnston Fund of the Smithsonian Institution and the Australian Research Council, awards LP0561498, DP0774491, DP1096749 and DP150104437

Assessing the risk of carbon dioxide emissions from blue carbon ecosystems

“Blue carbon” ecosystems, which include tidal marshes, mangrove forests, and seagrass meadows, have large stocks of organic carbon (Corg) in their soils. These carbon stocks are vulnerable to decomposition and – if degraded – can be released to the atmosphere in the form of CO2. We present a framework to help assess the relative risk of CO2 emissions from degraded soils, thereby supporting inclusion of soil Corg into blue carbon projects and establishing a means to prioritize management for their carbon values. Assessing the risk of CO2 emissions after various kinds of disturbances can be accomplished through knowledge of both the size of the soil Corg stock at a site and the likelihood that the soil Corg will decompose to CO2

Between a reef and a hard place: capacity to map the next coral reef catastrophe

Increasing sea surface temperature and extreme heat events pose the greatest threat to coral reefs globally, with trends exceeding previous norms. The resultant mass bleaching events, such as those evidenced on the Great Barrier Reef in 2016, 2017, and 2020 have substantial ecological costs in addition to economic and social costs. Advancing remote (nanosatellites, rapid revisit traditional satellites) and in-field (drones) technological capabilities, cloud data processing, and analysis, coupled with existing infrastructure and in-field monitoring programs, have the potential to provide cost-effective and timely information to managers allowing them to better understand changes on reefs and apply effective remediation. Within a risk management framework for monitoring coral bleaching, we present an overview of how remote sensing can be used throughout the whole risk management cycle and highlight the role technological advancement has in earth observations of coral reefs for bleaching events

Remote sensing for cost-effective blue carbon accounting

Blue carbon ecosystems (BCE) include mangrove forests, tidal marshes, and seagrass meadows, all of which are currently under threat, putting their contribution to mitigating climate change at risk. Although certain challenges and trade-offs exist, remote sensing offers a promising avenue for transparent, replicable, and cost-effective accounting of many BCE at unprecedented temporal and spatial scales. The United Nations Framework Convention on Climate Change (UNFCCC) has issued guidelines for developing blue carbon inventories to incorporate into Nationally Determined Contributions (NDCs). Yet, there is little guidance on remote sensing techniques for monitoring, reporting, and verifying blue carbon assets. This review constructs a unified roadmap for applying remote sensing technologies to develop cost-effective carbon inventories for BCE – from local to global scales. We summarise and discuss (1) current standard guidelines for blue carbon inventories; (2) traditional and cutting-edge remote sensing technologies for mapping blue carbon habitats; (3) methods for translating habitat maps into carbon estimates; and (4) a decision tree to assist users in determining the most suitable approach depending on their areas of interest, budget, and required accuracy of blue carbon assessment. We designed this work to support UNFCCC-approved IPCC guidelines with specific recommendations on remote sensing techniques for GHG inventories. Overall, remote sensing technologies are robust and cost-effective tools for monitoring, reporting, and verifying blue carbon assets and projects. Increased appreciation of these techniques can promote a technological shift towards greater policy and industry uptake, enhancing the scalability of blue carbon as a Natural Climate Solution worldwide

Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing

Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest

Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing

Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest

Mangrove dieback during fluctuating sea levels

Recent evidence indicates that climate change and intensification of the El Niño Southern Oscillation (ENSO) has increased variation in sea level. Although widespread impacts on intertidal ecosystems are anticipated to arise from the sea level seesaw associated with climate change, none have yet been demonstrated. Intertidal ecosystems, including mangrove forests are among those ecosystems that are highly vulnerable to sea level rise, but they may also be vulnerable to sea level variability and extreme low sea level events. During 16 years of monitoring of a mangrove forest in Mangrove Bay in north Western Australia, we documented two forest dieback events, the most recent one being coincident with the large-scale dieback of mangroves in the Gulf of Carpentaria in northern Australia. Diebacks in Mangrove Bay were coincident with periods of very low sea level, which were associated with increased soil salinization of 20-30% above pre-event levels, leading to canopy loss, reduced Normalized Difference Vegetation Index (NDVI) and reduced recruitment. Our study indicates that an intensification of ENSO will have negative effects on some mangrove forests in parts of the Indo-Pacific that will exacerbate other pressures

Is climate change shifting the poleward limit of mangroves?

Ecological (poleward) regime shifts are a predicted response to climate change and have been well documented in terrestrial and more recently ocean species. Coastal zones are amongst the most susceptible ecosystems to the impacts of climate change, yet studies particularly focused on mangroves are lacking. Recent studies have highlighted the critical ecosystem services mangroves provide, yet there is a lack of data on temporal global population response. This study tests the notion that mangroves are migrating poleward at their biogeographical limits across the globe in line with climate change. A coupled systematic approach utilising literature and land surface and air temperature data was used to determine and validate the global poleward extent of the mangrove population. Our findings indicate that whilst temperature (land and air) have both increased across the analysed time periods, the data we located showed that mangroves were not consistently extending their latitudinal range across the globe. Mangroves, unlike other marine and terrestrial taxa, do not appear to be experiencing a poleward range expansion despite warming occurring at the present distributional limits. Understanding failure for mangroves to realise the global expansion facilitated by climate warming may require a focus on local constraints, including local anthropogenic pressures and impacts, oceanographic, hydrological, and topographical conditions

People living in hilly residential areas in metropolitan Perth have less diabetes : spurious association or important environmental determinant?

BACKGROUND: Variations in 'slope' (how steep or flat the ground is) may be good for health. As walking up hills is a physiologically vigorous physical activity and can contribute to weight control, greater neighbourhood slopes may provide a protective barrier to weight gain, and help prevent Type 2 diabetes onset. We explored whether living in 'hilly' neighbourhoods was associated with diabetes prevalence among the Australian adult population.\ud \ud METHODS: Participants ([greater than or equal to]25years; n=11,406) who completed the Western Australian Health and Wellbeing Surveillance System Survey (2003-2009) were asked whether or not they had medically-diagnosed diabetes. Geographic Information Systems (GIS) software was used to calculate a neighbourhood mean slope score, and other built environment measures at 1600m around each participant's home. Logistic regression models were used to predict the odds of self-reported diabetes after progressive adjustment for individual measures (i.e., age, sex), socioeconomic status (i.e., education, income), built environment, destinations, nutrition, and amount of walking.\ud \ud RESULTS: After full adjustment, the odds of self-reported diabetes was 0.72 (95% CI 0.55-0.95) and 0.52 (95% CI 0.39-0.69) for adults living in neighbourhoods with moderate and higher levels of slope, respectively, compared with adults living in neighbourhoods with the lowest levels of slope. The odds of having diabetes was 13% lower (odds ratio 0.87; 95% CI 0.80-0.94) for each increase of one percent in mean slope.\ud \ud CONCLUSIONS: Living in a hilly neighbourhood may be protective of diabetes onset or this finding is spurious. Nevertheless, the results are promising and have implications for future research and the practice of flattening land in new housing developments