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Abstract Patterns of alpine plant productivity are extremely variable in space and time. Complex topography drives variations in temperature, wind, and solar radiation. Surface and subsurface flow paths route water between landscape patches. Redistribution of snow creates scour zones and deep drifts, which drives variation in water availability and growing season length. Hence, the distribution of snow likely plays a central role in patterns of alpine plant productivity. Given that these processes operate at sub‐1 m to sub‐10 m spatial scales and are dynamic across daily to weekly time scales, historical studies using manual survey techniques have not afforded a comprehensive assessment of the influence of snow distribution on plant productivity. To address this knowledge gap, we used weekly estimates of normalised difference vegetation index (NDVI), snow extent, and peak snow depth, acquired from drone surveys at 25 cm resolution. We derived six snowpack‐related and topographic variables that may influence vegetation productivity and analysed these with respect to the timing and magnitude of peak productivity. Peak NDVI and peak NDVI timing were most highly correlated with maximum snow depth, and snow‐off‐date. We observed up to a ~30% reduction in peak NDVI for areas with deeper and later snow cover, and a ~11‐day delay in the timing of peak NDVI in association with later snow‐off‐date. Our findings leverage a novel approach to quantify the importance of snow distribution in driving alpine vegetation productivity and provide a space for time proxy of potential changes in a warmer, lower snow future.
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Resource selection of a nomadic ungulate in a dynamic landscape
Nomadic movements are often a consequence of unpredictable resource dynamics. However, how nomadic ungulates select dynamic resources is still understudied. Here we examined resource selection of nomadic Mongolian gazelles ( Procapra gutturosa ) in the Eastern Steppe of Mongolia. We used daily GPS locations of 33 gazelles tracked up to 3.5 years. We examined selection for forage during the growing season using the Normalized Difference Vegetation Index (NDVI). In winter we examined selection for snow cover which mediates access to forage and drinking water. We studied selection at the population level using resource selection functions (RSFs) as well as on the individual level using step-selection functions (SSFs) at varying spatio-temporal scales from 1 to 10 days. Results from the population and the individual level analyses differed. At the population level we found selection for higher than average NDVI during the growing season. This may indicate selection for areas with more forage cover within the arid steppe landscape. In winter, gazelles selected for intermediate snow cover, which may indicate preference for areas which offer some snow for hydration but not so much as to hinder movement. At the individual level, in both seasons and across scales, we were not able to detect selection in the majority of individuals, but selection was similar to that seen in the RSFs for those individuals showing selection. Difficulty in finding selection with SSFs may indicate that Mongolian gazelles are using a random search strategy to find forage in a landscape with large, homogeneous areas of vegetation. The combination of random searches and landscape characteristics could therefore obscure results at the fine scale of SSFs. The significant results on the broader scale used for the population level RSF highlight that, although individuals show uncoordinated movement trajectories, they ultimately select for similar vegetation and snow cover.
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- Award ID(s):
- 1915347
- PAR ID:
- 10281668
- Editor(s):
- Weckerly, Floyd W.
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 16
- Issue:
- 2
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0246809
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0246809
