Dataset Closed Access
Elise Gallois; Isla Myers-Smith; Gergana Daskalova; Jeffrey Kerby; Haydn Thomas; Andrew Cunliffe
QHI_crop.tiff = We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b).
thermsum.tif = We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (Kemp et al., 2012). We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment.
Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk – Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179.
Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019.
Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. Global Change Biology, 26(2), 1003–1011.
Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. Methods in Ecology and Evolution, 11(1), 38-43.
Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. Methods in Ecology & Evolution, 3(1), 65-70.
The Arctic tundra is one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle.
Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada).
We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data.
We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness.
Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature.
Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.
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