scholarly journals Importance of the fiddler crab Uca pugnax to salt marsh soil organic matter accumulation

2010 ◽  
Vol 414 ◽  
pp. 167-177 ◽  
Author(s):  
CR Thomas ◽  
LK Blum
Geoderma ◽  
2021 ◽  
Vol 403 ◽  
pp. 115206
Author(s):  
Guohui Wu ◽  
Zhenhua Chen ◽  
Dongqi Jiang ◽  
Nan Jiang ◽  
Hui Jiang ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.


2019 ◽  
Vol 95 (10) ◽  
Author(s):  
Thomas Dinter ◽  
Simone Geihser ◽  
Matthias Gube ◽  
Rolf Daniel ◽  
Yakov Kuzyakov

ABSTRACT Salt marshes are coastal areas storing high amounts of soil organic matter (SOM) while simultaneously being prone to tidal changes. Here, SOM-decomposition and accompanied priming effects (PE), which describe interactions between labile and old SOM, were studied under controlled flooding conditions. Soil samples from two Wadden Sea salt marsh zones, pioneer (Pio), flooded two times/day, and lower salt marsh (Low), flooded ∼eight times/month, were measured for 56 days concerning CO2-efflux and prokaryotic community shifts during three different inundation-treatments: total-drained (Drained), all-time-flooded (Waterlogged) or temporal-flooding (Tidal). Priming was induced by 14C-glucose addition. CO2-efflux from soil followed Low>Pio and Tidal>Drained>Waterlogged, likely due to O2-depletion and moisture maintenance, two key factors governed by tidal inundation with regard to SOM mineralisation. PEs in both zones were positive (Drained) or absent (Waterlogged, Tidal), presumably as a result of prokaryotes switching from production of extracellular enzymes to direct incorporation of labile C. A doubled amount of prokaryotic biomass in Low compared to Pio probably induced higher chances of cometabolic effects and higher PE. 16S-rRNA-gene-amplicon-based analysis revealed differences in bacterial and archaeal community composition between both zones, revealing temporal niche adaptation with flooding treatment. Strongest alterations were found in Drained, likely due to inundation-mediated changes in C-binding capacities.


Author(s):  
Sheikha S Al-Zarban ◽  
Ibrahim Abbas ◽  
Azza A Al-Musallam ◽  
Ulrike Steiner ◽  
Erko Stackebrandt ◽  
...  

2020 ◽  
Vol 43 (4) ◽  
pp. 865-879
Author(s):  
Charles A. Schutte ◽  
John M. Marton ◽  
Anne E. Bernhard ◽  
Anne E. Giblin ◽  
Brian J. Roberts

Sign in / Sign up

Export Citation Format

Share Document