Andreas Kollert

Recent changes in alpine vegetation are often attributed to climate warming, particularly community composition shifts towards more warmth-associated species, or thermophilization. Here we assess the link between thermophilization and warming across 53 European mountain summits. We combine long-term macroclimatic and microclimatic temperature time series with vegetation surveys in 724 permanent plots, monitored over 21 years, to evaluate a possible thermophilization signal and relate it to rates of change in 10 temperature metrics. We find evidence of both thermophilization of alpine plant communities and an increase in temperatures. However, although these two trends are related when averaged across mountain regions, their relationship is weak at the individual plot scale, especially when considering microclimatic temperature metrics. Instead, substrate conditions and particularly the availability of thermophilic colonizers in the surrounding vegetation have a major influence on plot-level thermophilization rates. We conclude that the response of plant communities to climate change strongly depends on the abiotic and biotic context, and intensified monitoring efforts are needed to reduce the resulting uncertainties.

The increasingly acknowledged and consequently also better understood microclimatic variability in terrestrial ecosystems has motivated a call for finer spatial resolution in species distribution modelling, especially in the case of sedentary low‐stature organisms such as plants. In contrast, less attention has so far been paid to the way climate should be represented in these models. In fact, most modelling applications rely only on a handful of so‐called bioclimatic variables (i.e. essential climatic variables designed for ecological applications), which is at odds with the hypothesised variation in the sensitivity of individual plant species to different facets of climate. We argue that the recent shift towards microclimate modelling provides a window of opportunity for re‐evaluating the predominant reliance on the small set of bioclimatic variables. We used a unique dataset of 895 1‐m ² plots with vascular plant species observations and in situ soil temperature measurements across a high‐mountain landscape spanning 1700 elevational metres. From the hourly temperature measurements, we calculated bioclimatic variables as well as 188 additional ‘grid variables' arising from an aggregation of various summary statistics over different parts of the year. We then used those ‘grid variables', their subsets, and the bioclimatic variables to fit species distribution models for 101 plant species with combinations of one, two or three predictors. We found that bioclimatic variables consistently delivered less accurate models than many of the grid variables. Models based on subsets of grid variables only slightly decreased in accuracy and remained superior over bioclimatic variables even after the set was narrowed from the initial 188 to only six variables on the basis of a cluster analysis. These results highlight that modelling species distributions with only a few climatic variables is a viable strategy. However, the most suitable variables may often be different from those that are commonly used nowadays.

Snow is a crucial factor determining plant species distributions in alpine and arctic environments. Therefore, metrics like the duration of snow cover are important predictors to model plant distributions. Many studies employed snow cover metrics derived from optical satellite image time series. Such satellite-derived observations are easily accessible and highly consistent, making them a viable choice for current and past conditions. However, an inherent limitation is their applicability for future projections of snow cover, which is only possible by establishing statistical relationships to ancillary data sets. Snow cover simulated by a physically-based snow model could circumvent these constraints, but it was rarely employed for predicting alpine plant species distributions. Increasing availability of input data, computational power and data sets for validation nowadays allow for modeling at reasonably high resolutions.To this end, we report first results of several modeling experiments, to quantify the differences of using snow cover metrics derived from webcam time series and modeled snow data for a study site of approximately 5 km² in the Stubaier Alps (Tyrol, Austria). Melt-out date is one of most commonly used snow metrics in species distribution models. Hence, we derive the melt-out dates from two seasons (2022 and 2023) of webcam-based and modeled snow cover. Subsequently, we modeled the distribution of 79 plant species with the melt-out dates as predictors along with several proxies for topographic heterogeneity at spatial resolutions of 1 m and 20 m in order to account for the small-scale variability of snow cover in alpine landscapes. The study demonstrates how the usage of modeled and observed snow data affects modeling of high-alpine vegetation distribution. These insights are important for appropriately designing species distribution modeling studies based on modeled rather than observed snow data.Acknowledgements: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 883669).

An increase in vegetation productivity has been attributed to accelerated warming in different mountain ranges over the last decades by analysis of satellite imagery. Here, we quantify such a greening trend on 767 sampling plots with a high topographic variety in elevation, slope, and aspect in the sub-alpine to nival vegetation belt of Mt. Schrankogel (Tyrol, Austria) over the past four decades by analysing Landsat satellite image time series. We found (i) a good agreement of NDVI with in-situ vegetation cover estimates in a reference year and (ii) a widespread greening trend. Our set of plots has experienced a median greening trend of 0.018 NDVI units per decade, with 98% of the plots showing a positive NDVI trend. These results need to be considered with caution as the detailed analysis of the NDVI time series together with knowledge of the local conditions at the plots reveals potential pitfalls for interpretation. These are related to geomorphological disturbance of soil and vegetation, legacy effects of 20th century glacier retreat, or data scarcity (due to snow and clouds). Nevertheless, our study generally supports the notion that the productivity of cold-limited vegetation has increased which is even detectable from space.

Besides environmental sorting, other processes like biotic interactions and dispersal limitation are vital for the assembly of plant communities in high mountains and their re‐assembly under changing climatic conditions. Nevertheless, studies that compare the impact of these factors on plant community assembly above the tree line are largely lacking so far. We analysed occurrence changes in vascular plant communities of 492 permanent 1‐m² plots in the alpine‐nival ecotone of Mt. Schrankogel, Austrian Alps by comparing resurvey data from 2014 with data from the initial survey in 1994. We combined these data with species inventories from 899 additional plots sampled in 2021 and 2022 across a larger landscape above the tree line covering an elevational range of 1700 m, which we used for fine‐scale habitat suitability modelling. We assessed the relative effects of projected habitat suitability, propagule pressure from surrounding populations and biomass density of neighbours on 1532 colonization and 372 extirpation events of 31 species observed on the permanent plots. We found that all three factors are significantly related to both colonisations and extirpations, with habitat suitability having the strongest, propagule pressure a slightly weaker, and vegetation density the weakest effect. Colonisations can be better explained by the three process proxies than extirpations. Our results indicate a crucial role of dispersal limitation besides the predominant effect of environmental filtering on the (re‐)assembly of the alpine‐nival plant community, while competitive/facilitative effects between plants tend to play a minor role. The strong imprint of nearby source plant populations on colonization/extirpation events suggests that recent plant migrations predominantly occur in small steps. This implies that while the topographically complex alpine terrain offers climatic microrefugia for plants, it may also pose potential barriers, hindering species from following their suitable climatic niches upwards. Synthesis: Besides filtering by environmental conditions dispersal limitation had a strong effect on the observed changes in a local alpine plant community over two decades. Limited dispersal capacities of plant species may counteract the ability of isolated cold areas to effectively shelter high alpine plants from the effects of climate warming.

Recently, snow cover has gained a lot of interest as an important driver of plant species distribution in arctic and alpine environments, especially on small spatial scales . However, variation of snow cover at this scale is hardly resolved by open satellite data. Hence, linking remotely sensed snow cover and critical patterns and processes in vegetation can be challenging due to a mismatch in spatial resolution. We present a study based on a high alpine network of three webcams for the validation of snow cover products covering an entire year. Satellite based snow cover products (Landsat, Sentinel-2, downscaled MODIS products) are benchmarked on webcam-derived snow cover. While optical satellite remote sensing is a valuable tool for characterizing snow cover dynamics at the scale of tens of meters, cloud cover causes considerable data gaps. As a temporally and spatially more continuous estimate, we additionally produce meter-scale snow cover using the openAmundsen model, and we compare this to the webcam derived snow cover as well. For all datasets, ecologically relevant indicators like snow cover duration and the number of snow-free days are aggregated and validated both for the entire year and on a sub-seasonal scale.

Disturbance by geomorphic processes is a key factor for current and potential plant species distributions in high mountain regions. We implemented and tested an approach for the detection, quantification, and geomorphological classification of 3D topographic change using airborne laser scanning (ALS) data. This approach is applied to two point cloud epochs (from 2006 and 2017) to identify and analyse changes related to five different process categories in a study area in the European Alps: Fluvial Processes, Rock Glacier Movement & Landslides, Channel Erosion & Debris Flow, Rockfall Release & Deposition and Vegetation & Anthropogenic Change. The results are assessed through comparison with a manually produced geomorphological map. The analysis covering the eleven-year time period provides detailed information on the magnitudes and spatial distribution of change per identified geomorphological process. The study shows that the workflow is capable of providing the fundamental basis for mapping vegetation disturbance by geomorphic activity in a high alpine site.