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Lauber, T.

Publications and source records attributed to Lauber, T..

3 recordsLinked to original sources

Niche constraints drive differences between mycorrhizal fungal guilds in future range shifts

Mycorrhizal fungi are a diverse and ubiquitous group of plant symbionts whose distribution strongly influences ecosystem function across the globe. Yet, until now, we do not have quantitative data on the range sizes of different mycorrhizal fungal taxa, limiting our capacity to forecast future shifts in community composition and function. Here, we use 115,924 DNA sequence-derived observations to map the distribution of 651 common mycorrhizal fungal taxa and forecast future changes to their range sizes. We demonstrate that climate, plant cover, and soil factors, particularly mean annual temperature, net primary productivity, and soil organic carbon, exert major control over mycorrhizal fungal distributions. Based on these drivers, the ecological niches of mycorrhizal fungi consistently differ between arbuscular and ectomycorrhizal functional guilds. Arbuscular mycorrhizal fungal taxa generally occupy a wider niche breadth than ectomycorrhizal fungi, occurring across larger ranges of climate, soil, plant cover, topography, and disturbance conditions. Our models also predict widespread decreases in the suitable range size of mycorrhizal fungal taxa under projected future global climates, with average ranges decreasing by 13.8% or ~2.2 million km2 under high emissions scenarios (ssp5-8.5). This decrease in projected range size will be most pronounced for ectomycorrhizal fungi, strongly linked to constraints from their smaller overall niches. By generating a global atlas of common mycorrhizal fungi and their associated environmental niche, we establish a critical baseline for widely suspected declines in global fungal biodiversity.

ecology↗

Enhanced local cooling effects of forests across the globe

Forests cool the land surface in warm regions and warm the land surface in cool regions. Because these local temperature buffering effects depend on background climate1,2, increasingly large areas might experience forest cooling effects as the climate warms3-5. Here, using statistical modeling, we quantified changes in the sensitivity of land surface temperatures6 to forest cover change7 from 1988 - 2016, across 15 million km2 of land area. Forests had a local cooling effect on day and night surface temperatures in 86.3% (72 million km2) and 61.6% (5,108 million km2) of the forest area, respectively. This area has increased by 0.3% (0.2 million km2) at day and 4.7% (2 million km2) at night over recent decades. Our study indicates that climate change is enhancing the cooling effect of forests in the short term, underscoring the importance of protecting natural, diverse forest ecosystems in the face of rising global temperatures that threaten ecosystems8,9, human health10,11, and food production12.

ecology↗

A geospatial mapping pipeline for ecologists

Geospatial modelling can give fundamental insights in the biogeography of life, providing key information about the living world in current and future climate scenarios. Emerging statistical and machine learning approaches can help us to generate new levels of predictive accuracy in exploring the spatial patterns in ecological and biophysical processes. Although these statistical models cannot necessarily represent the essential mechanistic insights that are needed to understand global biogeochemical processes under ever-changing environmental conditions, they can provide unparalleled predictive insights that can be useful for exploring the variation in biophysical processes across space. As such, these emerging tools can be a valuable approach to complement existing mechanistic approaches as we aim to understand the biogeography of Earths ecosystems. Here, we present a comprehensive methodology that efficiently handles large datasets to produce global predictions. This mapping pipeline can be used to generate quantitative, spatially explicit predictions, with a particular emphasis on spatially-explicit insights into the evaluation of model uncertainties and inaccuracies.

ecology↗