Multidecadal analysis of beach loss at the major offshore sea turtle nesting islands in the northern Arabian Gulf

Maneja, Rommel; Miller, Jean Baker; Li, Wenzhao; Thomas, Rejoice; Perera, Sachi; Flandez, Ace Vincent B.; Basali, Abdullajid U.; Alcaria, Joselito Francis A.; Gopalan, Jinoy; Tiwari, Surya Prakash; Al-Jedani, Mubarak; Prihartato, Perdana K.; Loughland, Ronald A.; Qasem, Ali; Qurban, Mohammad A.; Falath, Wail; Struppa, Daniele C.

doi:10.1016/j.ecolind.2020.107146

Cited by 12 publications

(11 citation statements)

References 59 publications

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“…This manuscript utilized the Level 1C products, which provides top-of-atmosphere reflectance in cartographic geometry. The bands that were used for processing are the red, green, blue, near infrared (NIR) and the short-wave infrared (SWIR1) [28,29].…”

Section: B Sentinel-2 Imagery For Coastsatmentioning

confidence: 99%

Assessing the Vertical Displacement of the Grand Ethiopian Renaissance Dam during its Filling using DInSAR technology and its Potential Acute Consequences on the Downstream Countries

Fawzy¹,

Thomas²,

Li³

et al. 2021

Preprint

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The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storages and flow but also pose massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this study, through the analysis of Sentinal-1 satellite imagery we show concerning deformation patterns associated with different sections of the GERD’s Main Dam (structure RCC Dam type) and the Saddle Dam (Embankment Dam type). We processed 109 descending mode scenes from Sentinel-1 SAR imagery, from December 2016 to July 2021, using the Differential Synthetic Aperture Radar Interferometry technique to demonstrate the deformation trends of both - the GERD’s Main and Saddle Dams. The time-series generated from the analysis clearly indicates different displacement trends at various sections of the GERD as well as the Saddle Dam. Results of the multi temporal data analysis on and around the project area show inconsistent subsidence at the extremities of the GERD Main Dam, especially the west side of the dam where we recorded varying displacements in the range of 10 mm to 90 mm at the crest of the dam. We conducted the current analysis after masking the images with a coherence value of 0.9 and hence, the subsequent results are extremely reliable and accurate. Further decomposition of the subsiding rate has revealed higher vertical displacement over the west side of the GERD’s Main Dam as compared to the east side. The local geological structures consisting of weak zones under the GERD’s accompanying Saddle Dam adds further instability to its structure. We identified seven critical nodes on the Saddle Dam that match the tectonic faults lying underneath it, and which display a varying degree of vertical displacements. In fact, the nodes located next to each other displayed varying displacement trends: one or more nodes displayed subsidence since 2017 while the other node in the same section displayed uplift. The geological weak zones underneath and the weight of the Saddle Dam itself may somewhat explain this inconsistency and the non-uniform vertical displacements. For the most affected cells, we observed a total displacement value of ~90 mm during the whole study period (~20 mm/year) for the Main Dam while the value of the total displacement for the Saddle dam is ~380 mm during the same period (~85 mm/year). Analysis through CoastSat tool also suggested a non-uniformity in trends of surface water-edge at the two extremities of the Main Dam.

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Section: B Sentinel-2 Imagery For Coastsatmentioning

confidence: 99%

Assessing the Vertical Displacement of the Grand Ethiopian Renaissance Dam during its Filling using DInSAR technology and its Potential Acute Consequences on the Downstream Countries

Fawzy¹,

Thomas²,

Li³

et al. 2021

Preprint

Self Cite

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“…For example, rising sea levels and increased temperatures can lead to the loss of nesting beaches for marine turtles. Coastal erosion can destroy nesting sites, making it challenging for turtles to find suitable areas to lay their eggs 73 . In the past decade, there has been an observed rise in sporadic nesting occurrences of sea turtles 74 , notably linked to unusual increases in Sea Surface Temperature (SST) 75 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning applied to global scale species distribution models (SDMs)

Fuster-Alonso,

Mestre-Tomás,

Baez

et al. 2024

Preprint

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Species Distribution Models (SDMs) have been widely applied in ecology to analyze the historical and future patterns of marine species' distributions. With the increasing impact of climate change in recent decades, understanding potential shifts in species distributions has become a crucial challenge. Research on alterations in spatial and temporal distributions has revealed an increasing focus on developing different statistical approaches for global-scale and long-term forecasts. One promising approach is Bayesian Additive Regression Trees (BART), a non-parametric machine learning tool based on a sum-of-trees model that could be useful for addressing ecological problems. The goal of this study is to apply BART on a global scale and use it to estimate and predict possible present and future habitats of marine species under different climate change scenarios. Here we show an application of BART focused on the functional group of marine turtles, analyzing their historical and future distributions both individually and as a taxonomic group, their relationship with environmental variables, and BART's capacity to predict long-term distributions at global scales. Furthermore, to assess the capabilities of BART, we conduct a simulation study under two distinct scenarios: 1) simulating a hypothetical cosmopolitan species distribution and 2) simulating a hypothetical persistent species distribution. Our results show that BART is a promising approach to predict the potential distribution of our target species, as well as their relationship with key environmental variables, on a global scale.

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“…On the other hand, chronic exposure occurs when severe weather significantly changes the physical characteristics of a nesting beach, such as sand grain size and coastal morphology (i.e., dune and vegetation loss). These changes can persist over multiple nesting seasons and have the potential to reduce overall nesting success (the number of emerging females that successfully nest) (Maneja et al, 2021; Salleh et al, 2021). Predicting exactly where and when coastal damage from severe weather events will occur is difficult because cyclones are highly dynamic and their impacts on coastlines are mediated by many factors, such as the timing of storm surge events in relation to tide height and the path of a cyclone (Ciavola et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Vulnerability of sea turtle nesting sites to erosion and inundation: A decision support framework to maximize conservation

Gammon

Whiting²,

Fossette³

2023

Ecosphere

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Sandy beaches provide essential nesting habitats for sea turtles but are threatened globally by a rapidly changing climate. Identifying which nesting sites are at the greatest risk from erosion and inundation remains an important goal of sea turtle conservation globally. Yet, efforts to identify at‐risk sites have been hindered by the ability to model complex processes and incomplete information on nesting distribution and abundance. To assess the erosion and inundation risk to the reproductive success of a discrete genetic stock of flatback turtles (Natator depressus) across its nesting range in the Pilbara region of Western Australia, we used the Integrated Valuation of Ecosystem Services and Trade‐offs (InVEST) Coastal Vulnerability Model. A relative exposure index was calculated for 402 nesting beaches in terms of six geophysical variables: wind and wave exposure, surge potential, relief, observed sea level rise, and coastal geomorphology, and coupled with published information on the distribution and abundance of turtle tracks in the region. The majority of beaches (74%) had intermediate to high exposure. In particular, 36% of beaches with a high abundance of flatback tracks (the top 25% of the frequency distribution) had a high exposure (the top 25% of the frequency distribution). This suggests that coastal exposure is a key vulnerability to the reproductive success of sea turtles that nest in this region. Promisingly, five beaches with a high abundance of turtle tracks also had a low exposure (bottom 25% of the frequency distribution), and these beaches may be critical for the long‐term resilience of the stock against sea level rise and severe storms. Exposure varied across nesting sites, and the approach presented here allows for a rapid and broadscale assessment of relative erosion and inundation risks at a scale most relevant to management.

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Multidecadal analysis of beach loss at the major offshore sea turtle nesting islands in the northern Arabian Gulf

Cited by 12 publications

References 59 publications

Assessing the Vertical Displacement of the Grand Ethiopian Renaissance Dam during its Filling using DInSAR technology and its Potential Acute Consequences on the Downstream Countries

Assessing the Vertical Displacement of the Grand Ethiopian Renaissance Dam during its Filling using DInSAR technology and its Potential Acute Consequences on the Downstream Countries

Machine learning applied to global scale species distribution models (SDMs)

Vulnerability of sea turtle nesting sites to erosion and inundation: A decision support framework to maximize conservation

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