Alpine shrubs have benefited more than trees from 20th century warming at a treeline ecotone site in the French Pyrenees

Francon, Loïc; Roussel, Erwan; Lopez-Saez, Jérôme; Saulnier, Mélanie; Stoffel, Markus; Corona, Christophe

doi:10.1016/j.agrformet.2022.109284

Cited by 8 publications

(5 citation statements)

References 122 publications

(179 reference statements)

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“…Our results aligned well with previous investigations in Arctic tundra ecosystems that found widespread shrub expansion and increased growth rate (Chapin et al, 2005; Elmendorf et al, 2012; Myers‐Smith et al, 2011; Myers‐Smith, Elmendorf, et al, 2015; Myers‐Smith, Hallinger, et al, 2015). Recent warming at higher latitudes seems to promote growth and productivity with shrub species showing higher sensitivity to climate and the ability to anticipate and accelerate their growth dynamics and expansion respect trees (Forbes et al, 2010; Francon et al, 2023; Pellizzari et al, 2017). Trees, being much more coupled to air temperature and related atmospheric patterns, might still be affected by cool summer conditions and short growing seasons (Carrer et al, 2019; Pellizzari et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Growth form and leaf habit drive contrasting effects of Arctic amplification in long‐lived woody species

Frigo

Eggertsson²,

Prendin

et al. 2023

Global Change Biology

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Current global change is inducing heterogeneous warming trends worldwide, with faster rates at higher latitudes in the Northern Hemisphere. Consequently, tundra vegetation is experiencing an increase in growth rate and uneven but expanding distribution. Yet, the drivers of this heterogeneity in woody species responses are still unclear. Here, applying a retrospective approach and focusing on long‐term responses, we aim to get insight into growth trends and climate sensitivity of long‐lived woody species belonging to different functional types with contrasting growth forms and leaf habits (shrub vs. tree and deciduous vs. evergreen). A total of 530 samples from 7 species (common juniper, dwarf birch, woolly willow, Norway spruce, lodgepole pine, rowan, and downy birch) were collected in 10 sites across Iceland. We modelled growth trends and contrasted yearly ring‐width measurements, filtering in high‐ and low‐frequency components, with precipitation, land‐ and sea‐surface temperature records (1967–2018). Shrubs and trees showed divergent growth trends, with shrubs closely tracking the recent warming, whereas trees, especially broadleaved, showed strong fluctuations but no long‐term growth trends. Secondary growth, particularly the high‐frequency component, was positively correlated with summer temperatures for most of the species. On the contrary, growth responses to sea surface temperature, especially in the low frequency, were highly diverging between growth forms, with a strong positive association for shrubs and a negative for trees. Within comparable vegetation assemblage, long‐lived woody species could show contrasting responses to similar climatic conditions. Given the predominant role of oceanic masses in shaping climate patterns in the Arctic and Low Arctic, further investigations are needed to deepen the knowledge on the complex interplay between coastal tundra ecosystems and land‐sea surface temperature dynamics.

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Section: Discussionmentioning

confidence: 99%

Growth form and leaf habit drive contrasting effects of Arctic amplification in long‐lived woody species

Frigo

Eggertsson²,

Prendin

et al. 2023

Global Change Biology

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“…Our results raise questions about the future dynamics of shrublands. While several studies show a strong link between shrub dynamics and encroachment and climatic factors (Šenfeldr et al 2021, Yang et al 2022, Francon et al 2023, the role of past land use cannot be overlooked (Galop 1994, Améztegui et al 2010, Ameztegui et al 2016, MalfasiCannone 2020. Land abandonment during the industrial revolution has been a major driving force behind the current trajectory of alpine landscapes (Tasser et al 2007, Rutherford et al 2008, Pecher et al 2011, as at that time, farmers used high mountain pastures for their cattle and eliminated shrublands by fire.…”

Section: Implications For Future Dynamics Of Shrublandsmentioning

confidence: 99%

Uncovering the distribution and limiting factors of Ericaceae-dominated shrublands in the French Alps

Bayle,

Carlson,

Nicoud

et al. 2024

Frontiers of Biogeography

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Highlights• Ericaceous-dominated mountain shrubland are key habitats in alpine ecosystems, and understanding their dynamics is limited by the difficulties of mapping them using remote sensing.• Pigment-sensitive spectral indices based on Sentinel-2 bands used within a specific phenological window successfully allows Ericaceous-dominated mountain shrublands mapping.• Topoclimatic factors related to water conditions are driving the distribution of Ericaceous-dominated mountain shrublands at regional scale while temperatures drives the upper elevation boundaries.• Decline of the traditional agro-sylvo-pastoral system is suggested as a common critical juncture in the trajectory of shrublands across all mountain ranges.• Local topoclimatic conditions is suggested to have led to contrasting growth and encroachment depending on the location within the ecological niche of the habitat identified in this analysis.

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“…T HE snow cover area (SCA), defined as the spatial extent of the snow cover on the land surface [1], is an important variable to understand hydrological and ecological processes in mountainous regions. In particular, mountain soil and vegetation properties are largely driven by seasonal snow cover duration and snow melt-out date [2]- [6]. Therefore, information on the spatial-temporal variability of the snow cover area over long time periods is critical to study the response of mountain ecosystems to climate change.…”

Section: Introductionmentioning

confidence: 99%

Snow and Cloud Classification in Historical SPOT Images: An Image Emulation Approach for Training a Deep Learning Model Without Reference Data

Barrou Dumont,

Gascoin,

Inglada

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The lack of revisit in long-term satellite time series such as Landsat is an issue to assess ecosystems response to snow cover variations in mountains. A recent release of the SPOT 1-5 satellite images collection by the SPOT World Heritage (SWH) program offers the opportunity to increase the temporal revisit of Landsat from 1986 to 2015 at 20 m resolution. However, spectral and radiometric limitations of these images hinder the application of well-established pixel-wise methods to extract the snow cover area. As a work-around, deep learning techniques such as convolutional neural networks can incorporate both spectral and spatial information to classify every pixel as snow, cloud, or snow-free. However, the lack of reference data poses a challenge to the implementation of such data-driven approaches. Here, we develop an emulator of SPOT images which takes as input Sentinel-2 images. As a result, an emulated SPOT image can be paired with a reference snow map generated from its source Sentinel-2 image to train a deep learning model able to process actual SPOT images. We follow this approach to train a U-Net and evaluate different training strategies. We apply the different models to classify actual SPOT images for which we have reference data for validation. The method yields high precision in detecting snow, with minimal false snow pixel identification. This is at the cost of overestimating cloud pixels around clouds and highly saturated areas. The results confirm the potential of this method to generate time series of snow cover maps using the SWH collection.

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Alpine shrubs have benefited more than trees from 20th century warming at a treeline ecotone site in the French Pyrenees

Cited by 8 publications

References 122 publications

Growth form and leaf habit drive contrasting effects of Arctic amplification in long‐lived woody species

Growth form and leaf habit drive contrasting effects of Arctic amplification in long‐lived woody species

Uncovering the distribution and limiting factors of Ericaceae-dominated shrublands in the French Alps

Snow and Cloud Classification in Historical SPOT Images: An Image Emulation Approach for Training a Deep Learning Model Without Reference Data

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