Vegetation Mediates Soil Temperature and Moisture in Arctic-Alpine Environments

Aalto, Juha; Roux, Peter C. le; Luoto, Miska

doi:10.1657/1938-4246-45.4.429

Cited by 84 publications

(78 citation statements)

References 70 publications

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“…Using high‐resolution topographic information in combination with climate and soil measurements could provide a more promising basis for modelling high‐resolution soil moisture data (Aalto et al. ; Pradervand et al. ).…”

Section: Discussionmentioning

confidence: 99%

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What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

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Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in‐depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo‐environmental sciences.

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Section: Discussionmentioning

confidence: 99%

“…) and spatial modelling (Aalto et al. ) show promise in estimating actual soil moisture at higher resolutions. To conclude, although often accounted for in SDMs with distal predictors, water‐related variables could be improved through combined approaches, mixing refined field measures, GIS modelling and remote sensing.…”

Section: Discussionmentioning

confidence: 99%

What we use is not what we know: environmental predictors in plant distribution models

Mod

Scherrer

Luoto

et al. 2016

J Vegetation Science

Self Cite

194

198

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“…), and may have high spatial variability at fine scales due to soil conditions and mesotopography (Isard ; Aalto et al. ).…”

Section: Methodsmentioning

confidence: 99%

Outcomes of biotic interactions are dependent on multiple environmental variables

Mod

Roux

Luoto

2014

J Vegetation Science

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Question: Can variation in the outcome of biotic interactions in relation to environmental severity be more accurately predicted when considering multiple stress and/or disturbance variables?Location: Arctic-alpine tundra in Kilpisjärvi, North-Finland. Methods:To test the impact of including multiple environmental variables in analyses of the outcomes of biotic interactions, we modelled reproductive effort and cover of 17 arctic-alpine species as a function of Empetrum nigrum ssp. hermaphroditum cover, geomorphological disturbance and soil moisture with statistical interactions of the explanatory variables included. We implemented a best-subset approach using generalised linear models (GLM) and selected the best model for each species based on Akaike's information criteria (AIC).Results: For the majority of species, models including multiple environmental variables were selected as best. Reproductive effort depended on one or both environmental variables for all species and 14 species were additionally influenced by Empetrum, with the impact of Empetrum varying with abiotic conditions in all but one of those species. Moreover, the 3-way interaction of three explanatory variables was included in the best-fit models for six species. The impact of Empetrum on species cover showed a similar pattern, with 11 species affected by Empetrum and its statistical interactions with one or both abiotic variables. Conclusions

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“…Soil moisture is highly influenced by snow dynamics, especially in areas of relatively low summer precipitation, and snow cover also constrains communities by altering the length of the growing season and modifying soil temperatures (Bokhorst et al., ). In the rugged terrain of the High Arctic, these environmental conditions can show a 10‐fold variation over a few metres, and small‐scale variations can shape community turnover at extremely fine scales (Aalto, le Roux, & Luoto, ). Hence, simulations that forecast plant communities under climate change scenarios should consider the high spatial variability in Arctic landscapes.…”

Section: Introductionmentioning

confidence: 99%

Forecasted homogenization of high Arctic vegetation communities under climate change

Stewart

Simonsen

Svenning

et al. 2018

Journal of Biogeography

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Aim Climate change results in increasing temperature and modified precipitation regimes in the High Arctic. Models can help anticipate the consequences on future biotic dynamics, e.g. vegetation. In rugged terrain, forecasts should consider fine‐scale spatial variability in environmental conditions that are proximally linked to plant performance. Here, we forecasted Arctic plant community response to future climate change using high‐resolution environmental variables. Location Zackenberg in the High Arctic of Greenland. Methods Using a combination of remote‐sensing data and field measurements, we interpolated soil moisture and temperature at 1 m resolution together with spatial data on snow cover and solar radiation. We calibrated stacked species distribution models (S‐SDMs) with data from 200 vegetation plots. To explore the sensitivity of Arctic communities to climate change, we forecasted these models under simulated increases in temperature and changes (positive or negative) in snow cover and soil moisture, corresponding to more winter and/or summer precipitation and higher frequencies of summer droughts. Results S‐SDMs associated with high‐resolution environmental variables were able to reproduce the spatial variation in species richness and plant community structure along a mountain slope in Zackenberg. Model forecasts under climate change revealed that most species reacted to a combination of changes in soil moisture and temperature, and changes in these two variables resulted in an extensive restructuring of the distributions of species assemblages. In most scenarios, a gradual homogenization of communities was forecasted due to shrub expansion. Main conclusions Increasing temperatures and altered soil moisture were predicted to turn the currently highly heterogeneous tundra landscape at Zackenberg into homogenous dwarf‐shrub tundra. Such homogenization of vegetation communities may have profound ramifications for species, interaction webs, and ecosystem processes via modifications to the surface albedo, energy and water balance, as well as snow accumulation and permafrost.

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Vegetation Mediates Soil Temperature and Moisture in Arctic-Alpine Environments

Cited by 84 publications

References 70 publications

What we use is not what we know: environmental predictors in plant distribution models

What we use is not what we know: environmental predictors in plant distribution models

Outcomes of biotic interactions are dependent on multiple environmental variables

Forecasted homogenization of high Arctic vegetation communities under climate change

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