Subalpine Ecosystems Research Articles

Effect of Warming on Soil Fungal Community Along Altitude Gradients in a Subalpine Meadow

The subalpine grassland ecosystem is sensitive to climatic changes. Previous studies investigated the effects of warming on grassland ecosystems at a single altitude, with little information about the response of subalpine meadows to warming along altitude gradients. This study aimed to evaluate the effects of warming on aboveground grass, belowground soil properties, and fungal community along altitude gradients in the subalpine meadow of Mount Wutai using the high-throughput sequencing method. Warming reduced the restriction of low temperatures on the growth of subalpine grass, resulting in increasing grass biomass, community height, and coverage. More grass biomass led to higher soil organic carbon resources, which primarily affected fungal community composition following warming. Warming might induce more stochastic processes of fungal community assembly, increasing fungal diversity at low altitudes. In contrast, warming triggered more deterministic processes to decrease fungal diversity at medium and high altitudes. Warming might improve the efficiency of soil nutrient cycling and organic matter turnover by increasing the relative abundance of soil saprotrophs and improving fungal network connectivity. The relative abundance of certain grass pathogens significantly increased following warming, thereby posing potential risks to the sustainability and stability of subalpine meadow ecosystems. Overall, this study comprehensively evaluated the response of the subalpine meadow ecosystems to warming along altitude gradients, clarifying that warming changes soil fungal community composition at different altitudes. The long-term monitoring of pathogen-related shifts should be conducted in subalpine meadow ecosystem following warming. This study provided significant scientific insights into the impact of future climatic changes on subalpine ecosystems.

Assessing the health of climate-sensitive trees in a subalpine ecosystem through microbial community dynamics

Climate change has significantly affected the subalpine ecosystems, leading to mass die-offs of the Korean fir tree, a key climate-sensitive species in these environments. Proactive analysis of the phenotypic responses of these trees to climate change or the establishment of preemptive strategies for trees to adapt to these environmental changes remains a challenge. The current study aimed to investigate the impact of climate change on the health of Korean fir (Abies koreana) in the subalpine ecosystem of Jirisan Mountain, South Korea. We integrated soil physicochemical analyses, microbial community dynamics, neutral community model, and network analyses to examine the relationships between tree health and microbial communities. Our findings revealed significant changes in soil chemical properties, including pH and nutrient concentrations, across the various health statuses of trees. Microbial community analysis demonstrated shifts in bacterial and fungal communities corresponding to the health continuum of the trees, with decreased diversity and altered composition in the declining trees. A remarkable increase in modularity of the microbial network and a clear transition from stochastic to deterministic microbial community assembly processes were observed as the trees progressed from a healthy to a dead stage. Two bacterial genera, Bradyrhizobium and Burkholderia, along with an unclassified fungal group from Basidiomycota, were identified as key microbial indicators of good tree health. This study highlighted the importance of microbial communities as bioindicators for assessing the health of subalpine ecosystem and its resilience to climate change, offering valuable insights into the conservation and management strategies for subalpine ecosystems.

Unveiling nematode responses to afforestation from distributions of body size in a subalpine ecosystem

Unveiling nematode responses to afforestation from distributions of body size in a subalpine ecosystem

Lidar-derived estimates of boreal shrub biomass in Southcentral Alaska

Abstract Despite widespread observations of shrub proliferation and expansion (shrubification), few studies quantify shrub biomass at the regional scale. Here we describe and implement a two-part modeling approach to estimate and map tall shrub (diameter at root collar > 2.5 cm) expected aboveground biomass, or E[SHB], across a 16.6 million ha boreal region where shrubification occurs in wetlands and subalpine ecosystems. Using n = 384 field plots nested within m= 11 study sites across southcentral Alaska, we constructed random forest models of the probability (pSH) that shrub wood volume surpasses tree wood volume, and generalized additive models of aboveground biomass of tall shrubs (SHB) using rasterized aerial lidar variables collected by NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, together with gridded climate data from a Parameter-elevation Regressions on Independent Slopes Model 30-year normal climatology (PRISM). Applying those models to G-LiHT tiles, then averaging across tiles within n = 843 watersheds covering 9.2 million ha, we estimated that below 1000 m asl, the area-weighted mean value of E[SHB] = pSH x SHB = 6.6 Mg ha−1 with sd = 4.5 Mg ha−1. This is the first study to estimate current shrub biomass density at the regional scale in southcentral Alaska and serves as a biomass baseline for measuring and modeling aboveground carbon fluxes where plant communities are undergoing climate-driven change.

Using eddy covariance observations to determine the carbon sequestration characteristics of subalpine forests in the Qinghai–Tibet Plateau

Abstract. Subalpine forests are a crucial component of the carbon cycling system in the Qinghai–Tibet Plateau (QTP). However, there are currently significant data gaps in the QTP, and it is essential to enhance continuous monitoring of forest carbon absorption processes in the future. This study investigates 2 years' carbon exchange dynamics of a subalpine forest on the QTP using an eddy covariance method. We first characterized the seasonal carbon dynamics of the subalpine forest, revealing the higher carbon dioxide (CO2) exchange rates in summer and autumn and lower rates in winter and spring, and found that autumn is the peak period for carbon sequestration in this subalpine forest, with the maximum measured value of CO2 absorption reaching 10.70 µmol m−2 s−1. Subsequently, we examined the environmental factors influencing the carbon sequestration function. Principal component analysis (PCA) shows that photosynthetically active radiation (PAR) was the major environmental factor driving the net ecosystem exchange (NEE) of CO2, significantly influencing forest carbon absorption, and the increase in relative humidity decreases the rate of carbon fixation. In addition, we explored NEE and its influencing factors at the regional scale and found that air temperature promotes carbon dioxide absorption (negative NEE values), while the average annual precipitation shows a minor effect on NEE. At the annual scale, the subalpine forest functions as a strong carbon sink, with an average NEE of −332 to −351 g C m−2 (from November 2020 to October 2022). Despite the challenges of climate change, forests remain robust carbon sinks with the highest carbon sequestration capacity in the QTP, with an average annual CO2 absorption rate of 368 g C m−2. This study provides valuable insights into the carbon cycling mechanism in subalpine ecosystems and the global carbon balance.

Tracking ecosystem decline in an uncertain and changing alpine landscape

AbstractThis study investigated regional and site level diversity for alpine and subalpine systems. The aim was to assess whether differing analytical methods could track diversity through time. The study was undertaken within Kosciusko National Park in south‐eastern Australia (6900 km2) which encompasses most of the alpine zone (~2500 km2 or 48% of its occurrence on mainland Australia). Multivariate ordination, species richness and the relationship of diversity to landscape patterns were assessed and considered in relation to changes predicted to impinge upon these systems over coming decades. Species richness had limited capacity to detect predicted changes; however, ordination based on floristic patterns may have more capacity to detect change if adequate replication is applied. Complex patterns across landscapes and at the site level contributed to these analytical challenges. Species richness is commonly used to assess ecosystem status and to provide benchmark values, but it had very limited capacity to do so in the assessed alpine and subalpine ecosystems, including the most threatened. Government agencies often rely heavily on species richness to assess ecosystem change and this urgently needs re‐evaluation.

Ectomycorrhizal communities of adult and young European larch are diverse and dynamics at high altitudinal sites

Abstract Background/Aims The European larch is a pioneer tree and a valuable economic resource in subalpine ecosystems, thus playing crucial roles to ecosystem services and human activities. However, their ectomycorrhizal fungal community remains unknown in high altitudinal natural habitats. Here, we explore the mycobiont diversity of Larix decidua var. decidua between naturally rejuvenated and adult trees, compare ectomycorrhizal colonization patterns in geographically disjunct areas within the Alps of South Tyrol, Italy, characterized by distinct climatic conditions, and explore turnover rates across various seasons. Methods Our approach combines morphotyping of mycorrhized root tips with molecular analysis. Particular effort was given to monitor both ectomycorrhizal host-specialist and -generalist fungi. Results Both adult and young trees show a 100% mycorrhization rate, with a total diversity of 68 ectomycorrhizal species. The ectomycorrhizal composition is dominated by typical host specialists of larch trees (e.g., Lactarius porninsis, Russula laricina, Suillus cavipes, S. grevillei, S. viscidus), which are widely distributed across sites. A rich diversity of host generalists was also detected. The composition of rare species within a habitat was comparatively consistent during one sampling campaign, but exhibited significant differences among individual sampling campaigns. The ectomycorrhizal compositions were only weakly correlated with distinct climatic conditions and tree ages. However, species richness and diversity, particularly of generalist fungi, was consistently higher in warmer, drier sites compared to cooler, more humid ones. Conclusions This study suggests potential mycobiont community shifts across climatic conditions with significant implications for the adaptability and resilience of subalpine forests in the face of climate change.

Abundant Species Govern the Altitude Patterns of Bacterial Community in Natural and Disturbed Subalpine Forest Soils

Abundant and rare bacteria exhibit unequal responses to environmental changes and disturbances, potentially resulting in differential contributions to the altitudinal characteristics of total community in natural and disturbed soils. Although the altitude patterns of soil bacteria have been widely studied, it remains unclear whether these patterns are consistent among bacteria with varying predominance levels, and which subpopulation contributes more to maintaining these patterns in natural and disturbed subalpine forest soils. In this study, we collected 18 natural subalpine forest soil samples and 18 disturbed ones from three altitudes (2900 m a.s.l., 3102 m a.s.l., and 3194 m a.s.l.) along the Wenma highway in Miyaluo, Lixian, Sichuan, Southwest China. By partitioning total bacterial communities based on species predominance, we found that bacteria with higher predominance levels tended to exhibit altitude patterns (α-diversity, community structure, and functional redundancy) similar to those of total bacteria in both natural and disturbed subalpine forest soils, although they only occupied a small portion of the community. Abundant bacteria might play critical roles in maintaining the regional ecological characteristics of total community across the altitude gradient, while the rare and hyper-rare ones might contribute more to local diversity and functional redundancy. In natural soils, the altitude patterns of α-diversity inferred from total, abundant, and rare bacteria were mainly shaped by NO3−-N, while soil conductivity mainly drove the altitude patterns of α-diversity inferred from hyper-rare bacteria. Additionally, the community structures of total, abundant, rare, and hyper-rare bacteria were mainly shaped by NO3−-N, while the altitude patterns of functional redundancy inferred from total, abundant, and rare bacteria were mainly shaped by soil conductivity in natural soils. In disturbed subalpine forest soils, the influences of NO3−-N for the altitude patterns of α-diversity and community structure, and those of soil conductivity for functional redundancy, were relatively weak in total, abundant, rare, and hyper-rare bacteria. This study examined the roles of bacteria with varying predominance levels in maintaining the altitude pattern of bacteria in both natural and disturbed subalpine forest soils, providing novel insights for devising strategies to conserve biodiversity and ecologically restore disturbed soils in subalpine ecosystems.

Characteristics of the Rhizospheric AMF Community and Nutrient Contents of the Dominant Grasses in Four Microhabitats of the Subalpine Zone in Northwestern Yunnan, China

At the southeastern periphery of the Tibetan Plateau, the subalpine ecosystem hosts grasses as some of the most substantial species. However, the community and function of arbuscular mycorrhizal fungi (AMF) around the rhizospheres of grasses in the subalpine zone are still poorly understood. In the present study, 28 soils and 11 species of dominant grasses collected from four microhabitats (shrubland, grassland, woodland, and forest) in the subalpine zone of northwestern Yunnan, China, were used to investigate the AMF community by Illumina MiSeq high-throughput sequencing technology as well as nutrient contents. Among the four microhabitats, the maximum soil nutrient levels around the rhizospheres of grasses were observed in woodland. The nitrogen, phosphorus, and potassium concentrations in Dactylis glomerata shoots were significantly higher than those in the other 10 grass species. The AMF diversity of grassland in summer was substantially greater than that of the other three microhabitats (p < 0.05). Discrepancies were observed within a given plant species across microhabitats; for example, in summer, the nitrogen concentration in the shoot of Iris tectorum in woodland was significantly higher than that in both forest and shrubland (p < 0.05). A total of eight genera were detected in the AMF communities, which were dominated by Glomus, with a relative abundance of 45.4–94.4% in summer and 60.5–84.3% in winter. Moreover, the abundance of Glomus was significantly positively correlated with the content of alkali-hydrolyzable nitrogen in soil and nitrogen in grasses according to the Mantel test. As a critical nutrient element in soil, nitrogen is beneficial for plant growth. Thus, these results provide a better understanding of the resilience of soil AMF community and the ecological adaptability of grasses in the subalpine ecosystems of northwestern Yunnan.

Increase of temperature exacerbates the conversion of P fractions in organic horizon

In terrestrial ecosystems, phosphorus (P) is the limiting nutrient of primary production. The soil organic horizon is a vital source of bioavailable P in subalpine coniferous forests. However, the response of organic horizon P to temperature increase in subalpine coniferous forests is not well characterized. By studying different decomposed degree of organic horizon across an altitudinal gradient, we aimed to simulate responses of the organic horizon P conversion to MAT increase in subalpine ecosystems. In this study, relative enrichment and relative depletion of P fractions were defined as the conversions between different P fractions. We initially observed only unidirectional conversion from labile inorganic P (LIP) to highly resistant organic P (HOP) at a mean annual temperature (MAT) of 1 °C. However, with increasing MAT, there was a relative depletion of moderately resistant inorganic P (MIP) (MAT = 2.4 °C), followed by a successive depletion of moderately resistant organic P (MOP) (MAT = 4.1 °C). Concurrently, we observed relative enrichment of labile organic P (LOP) (MAT = 2.4 °C). Combined with indoor incubation experiments, we further found that the concentration of available P peaked (81.79 mg kg−1) at the initial stage of MIP relative depletion (MAT = 2.4 °C), while the net P mineralization rate (2.19 mg kg−1 d−1) reached a maximum following the initial relative depletion of MOP (MAT = 4.1 °C). Under elevated temperature, the pH of the organic horizon plays a crucial role in determining P fractions variation. These findings suggest that temperature increase can exacerbate the conversion of P fractions and the release of bioavailable P from organic horizon by successively triggering the relative depletion of MIP, the relative enrichment of LOP and relative depletion of MOP at specific MAT thresholds, which has important implications for the enhancement of primary production and carbon sequestration in subtropical coniferous forests under future climate warming.

Using photographs and deep neural networks to understand flowering phenology and diversity in mountain meadows

AbstractMountain meadows are an essential part of the alpine–subalpine ecosystem; they provide ecosystem services like pollination and are home to diverse plant communities. Changes in climate affect meadow ecology on multiple levels, for example, by altering growing season dynamics. Tracking the effects of climate change on meadow diversity through the impacts on individual species and overall growing season dynamics is critical to conservation efforts. Here, we explore how to combine crowd‐sourced camera images with machine learning to quantify flowering species richness across a range of elevations in alpine meadows located in Mt. Rainier National Park, Washington, USA. We employed three machine‐learning techniques (Mask R‐CNN, RetinaNet and YOLOv5) to detect wildflower species in images taken during two flowering seasons. We demonstrate that deep learning techniques can detect multiple species, providing information on flowering richness in photographed meadows. The results indicate higher richness just above the tree line for most of the species, which is comparable with patterns found using field studies. We found that the two‐stage detector Mask R‐CNN was more accurate than single‐stage detectors like RetinaNet and YOLO, with the Mask R‐CNN network performing best overall with mean average precision (mAP) of 0.67 followed by RetinaNet (0.5) and YOLO (0.4). We found that across the methods using anchor box variations in multiples of 16 led to enhanced accuracy. We also show that detection is possible even when pictures are interspersed with complex backgrounds and are not in focus. We found differential detection rates depending on species abundance, with additional challenges related to similarity in flower characteristics, labeling errors and occlusion issues. Despite these potential biases and limitations in capturing flowering abundance and location‐specific quantification, accuracy was notable considering the complexity of flower types and picture angles in this dataset. We, therefore, expect that this approach can be used to address many ecological questions that benefit from automated flower detection, including studies of flowering phenology and floral resources, and that this approach can, therefore, complement a wide range of ecological approaches (e.g., field observations, experiments, community science, etc.). In all, our study suggests that ecological metrics like floral richness can be efficiently monitored by combining machine learning with easily accessible publicly curated datasets (e.g., Flickr, iNaturalist).

Species-Abundance Models for the Early Postfire Succession of Subalpine Shrub Grassland

Fire is one of the principal factors influencing subalpine ecosystem succession. Species numbers and plant compositions are used to determine postfire disturbance, vegetation, structural change, and succession. Ecologists also integrate species diversity and mathematical models to enable researchers to obtain increasingly detailed insights into habitats during post-disturbance restoration processes. This study employed five species-abundance models, namely the niche preemption model, the broken-stick model, the log-normal model, the Zipf model, and the Zipf–Mandelbrot model, to perform fitting analysis on the abundance data of postfire species coverage in shrub grasslands near 369 Hut at Xue Mountain in Shei-Pa National Park, Taiwan. We performed the logarithmic transformation on plant-coverage areas for each period of postfire shrub-grassland succession, and then, based on histograms drawn for species–coverage distribution modes, the test results consistently showed normal distributions (p < 0.05). Species-coverage histograms measuring various periods showed that there were comparatively higher numbers of common species during postfire succession and that the numbers of rare species progressively increased. The fitting results of the five species-abundance models showed that although the most suitable abundance models for each period of postfire succession varied, the majority of these periods demonstrated decent fitting with respect to the Zipf–Mandelbrot model. These findings showed that fuel consumption provided nutrients in a manner that facilitated postfire regeneration. Moreover, dominant species, such as Yushania niitakayamensis, and Miscanthus transmorrisonensis, did not fully occupy growing spaces and resource availabilities; consequently, seeded species were able to grow.

Species-dependent phosphorus acquisition strategy modulates soil phosphorus cycle in the subalpine forest of eastern Tibetan Plateau

The availability of phosphorus (P) in soils will ultimately determine forest productivity because of increasing P limitation in terrestrial ecosystems. However, it is still unclear how and to what extent of different plant P-acquisition strategies affect soil P cycling in the subalpine ecosystems. Here, bulk soils (BS) and rhizosphere soils (RS) under Abies fabri and Rhododendron decorum were respectively collected in the early-, mid- and late-growing seasons in a subalpine forest of eastern Tibetan Plateau, and low molecular weight organic acids, microbial biomass P and P fractions were analyzed to decipher the effects of the plants on soil P availability. The P fractions in both BS and RS showed a distinct difference between A. fabri and R. decorum because of their different P acquisition strategies. The ericoid mycorrhiza-associated R. decorum sequestered soil available P through organic P mineralization, while the ectomycorrhizal mycorrhiza-associated A. fabri directly or indirectly acquired both the organic and inorganic P pools. Seasonal variations in soil available P further revealed that the difference in the P acquisition by the two species was closely associated with their growing stages. Inorganic P dissolution by citric acid determined available P supply for A. fabri in the mid-growing season, while organic P mineralization contributed to available P for R. decorum in the early-growing season. Our results indicate that mycorrhizal types and plant growing stages drive plant P acquisition, which results in the coexistence patterns of different species in the same habitat.

Elevated soil moisture amplified the effects of freeze–thaw cycles on soil CO2 and CH4 fluxes in subalpine forests

Soil carbon emissions from subalpine ecosystems have been demonstrated to be influenced by freeze–thaw cycles (FTCs). Under climate change, moisture and number of FTCs have been altered significantly in subalpine regions. Thus, we selected a typical subalpine forest in Northwest China and conducted an incubation study to explore the effects of various soil moisture (SM) levels and numbers of FTCs on soil CO2 and CH4 fluxes during nine FTCs. Our results revealed that the soil CO2 emissions and CH4 uptakes had significant responses to changes in SM (FCO2 = 2327.32, p < 0.001; FCH4 = 353.51, p < 0.001) and the number of FTCs (FCO2 = 2506.45, p < 0.001; FCH4 = 60.85, p < 0.001). Specifically, CO2 emissions in the thawing phases and CH4 uptakes in the freezing phases were largest in the first FTC and then gradually decreased and stabilized with an increase in the number of FTCs. Regarding SM, the soil CO2 emissions and CH4 uptakes at 60 and 90 % water-filled pore space (WFPS) at the same number of FTCs were significantly higher than those at 30 % WFPS. Moreover, the interactive effects of the number of FTCs and SM on soil CO2 emissions and CH4 uptakes were significant (FCO2 = 279.70, p < 0.001; FCH4 = 17.76, p < 0.001). Especially in the first FTC, SM dramatically amplified the effects of FTCs on CO2 emissions and CH4 uptakes. A partial least squares path model further confirmed that the number of FTCs had significant negative effects while SM had significant positive effects on CO2 emissions and CH4 uptakes. In addition, SM explained more of the variation than the number of FTCs in CO2 emissions and CH4 uptakes. CO2 emissions were modulated primarily by substrate accessibility and nutrient availability, and for CH4 uptakes, microbial properties also played a substantial role in addition to substrate accessibility and nutrient availability. We conclude that the increases in the number of FTCs and SM due to climate warming and humidification may trigger the potentially substantial increases in soil CO2 emissions and CH4 uptakes.

Response sensitivity processes of conifers radial growth to climate factors based on tree ring width variations

The tree radial growth process exhibits complex signals in response to climate change. These signals can significantly influence the tree growth process. Essential physiological factors play a critical role in determining growth-response differences. However, the mechanisms affecting the nonlinear response of conifer species growth to climate change have yet to be fully elucidated. In this study, we measured the characteristic signals of growth process-wide (narrow) rings from two conifer species, Qinghai spruce (Picea crassifolia Kom.) and Chinese pine (Pinus tabuliformis Carr.) in the subalpine zone at the southern edge of the Tengger Desert, and analyzed their phenology and seasonal water-heat utilization dynamic mechanisms using the process-based Vaganov-Shashkin model (VS-oscilloscope). Our investigation demonstrates that both species are sensitive to temperature, although P. crassifolia is more responsive to temperature-induced drought stress. Furthermore, the phenology and seasonal water-heat utilization dynamics are the main driving forces behind the climate response sensitivity and radial growth trends of conifer species. The study revealed earlier dormancy for both species in narrow rings, as well as earlier growth initiation for P. crassifolia. Additionally, temperature was identified as the key cause of growth decline trends in the middle growing season. The limited soil moisture caused by temperature-induced deficits in the end growing seasons was also responsible for the frequent narrow rings of P. tabuliformis. Therefore, we propose that management and conservation measures for subalpine ecosystems should be formulated based on the response sensitivity and physiological mechanism of conifer species.

Fire‐regime variability and ecosystem resilience over four millennia in a Rocky Mountain subalpine watershed

Abstract Wildfires strongly influence forest ecosystem processes, including carbon and nutrient cycling, and vegetation dynamics. As fire activity increases under changing climate conditions, the ecological and biogeochemical resilience of many forest ecosystems remains unknown. To investigate the resilience of forest ecosystems to changing climate and wildfire activity over decades to millennia, we developed a 4800‐year high‐resolution lake‐sediment record from Silver Lake, Montana, USA (47.360° N, 115.566° W). Charcoal particles, pollen grains, element concentrations and stable isotopes of C and N serve as proxies of past changes in fire, vegetation and ecosystem processes such as nitrogen cycling and soil erosion, within a small subalpine forest watershed. A published lake‐level history from Silver Lake provides a local record of palaeohydrology. A trend towards increased effective moisture over the late Holocene coincided with a distinct shift in the pollen assemblage c. 1900 yr BP, resulting from increased subalpine conifer abundance. Fire activity, inferred from peaks in macroscopic charcoal, decreased significantly after 1900 yr BP, from one fire event every 126 yr (83–184 yr, 95% CI) from 4800 to 1900 yr BP, to one event every 223 yr (175–280 yr) from 1900 yr BP to present. Across the record, individual fire events were followed by two distinct decadal‐scale biogeochemical responses, reflecting differences in ecosystem impacts of fires on watershed processes. These distinct biogeochemical responses were interpreted as reflecting fire severity, highlighting (i) erosion, likely from large or high‐severity fires, and (ii) nutrient transfers and enhanced within‐lake productivity, likely from lower severity or patchier fires. Biogeochemical and vegetation proxies returned to pre‐fire values within decades regardless of the nature of fire effects. Synthesis. Palaeorecords of fire and ecosystem responses provide a novel view revealing past variability in fire effects, analogous to spatial variability in fire severity observed within contemporary wildfires. Overall, the palaeorecord highlights ecosystem resilience to fire across long‐term variability in climate and fire activity. Higher fire frequencies in past millennia relative to the 20th and 21st century suggest that northern Rocky Mountain subalpine ecosystems could remain resilient to future increases in fire activity, provided continued ecosystem recovery within decades.

Climate conditions are primary predictors of the regional‐scale spatial diversity patterns of ectomycorrhizal fungi

AbstractAimSpatial models are valuable for revealing biodiversity patterns but are less commonly applied to soil microbes than to aboveground macroorganisms. Ectomycorrhizal (EM) fungi are symbiotic microbes with high taxonomic and functional diversity that are associated with forest trees. We aimed to predict regional‐scale spatial patterns of EM fungal richness and community composition.LocationForests and subalpine ecosystems in Japan, from Hokkaido to Okinawa.TaxonEM fungi (Asco‐ and Basidiomycetes).MethodsWe used EM fungal DNA sequence data from 1507 soil cores at 39 sites covering a wide range of environmental conditions. The random forest machine learning approach was applied to determine the relative importance of environmental variables (i.e. climate, soil and ecosystem productivity) and to make spatial predictions. The spatial patterns of EM fungal richness and community composition were mapped at 1‐km2 grid resolution.ResultsTemperature generally had a strong influence on EM fungal richness and community composition dissimilarity. Our regional spatial analysis revealed that (1) EM fungal richness was higher in northeastern and montane regions than in southwestern regions and low‐elevation plains, (2) different EM fungal lineages exhibited contrasting spatial diversity patterns and (3) community composition dissimilarity shifted sharply from high to low elevations, and gradually from northeastern to southwestern regions, mainly in relation to climate gradients. Areas with low applicability for spatial modelling were identified based on multidimensional environmental spaces, which will help to prioritize data collection for future research.Main ConclusionsOur study provides a baseline of the potential spatial patterns of EM fungal communities, which were explained primarily by climate variables and secondarily by soil factors and ecosystem productivity. The predicted spatial patterns may be valuable for identifying diversity hotspots and advancing the assessment of climate change impacts on ecologically important root‐associated fungi.

Elevational variations in stem hydraulic efficiency and safety of Abies fabri

Abstract The structural and functional traits of coniferous trees can reflect their growth states and adaptive strategies to a harsh environment. However, it is still unclear if and how hydraulic traits of subalpine conifers change with altitude. Therefore, the changes in stem hydraulic characteristics of Abies fabri along an elevational gradient (2700–3700 m a.s.l.) were identified in a subalpine ecosystem in southwest China and linked to anatomical properties. Xylem hydraulic efficiency decreased with increasing elevation. Surprisingly, higher hydraulic dysfunction and vulnerability to embolism occurred at higher altitudes. The trade‐off between hydraulic efficiency and safety was weak in A. fabri at higher elevations. Low temperature and superfluous precipitation may be the main constraints for hydraulic function and mechanical strength ((t/b)2)) in plants at high elevations (p &lt; 0.05). The strongly limited hydraulic transport system reflected the severe growth constraint of A. fabri at high elevations. Although series of anatomical traits varied with elevation (e.g. smaller mean diameter of tracheid, mean hydraulic conduit diameter and mean pit aperture diameter at higher altitude) and revealed the adaptive strategies to enhance embolism resistance, thickness‐to‐span ratio ((t/b)2) played a dominant role in the trade‐off between hydraulic efficiency and safety. Thickness‐to‐span ratio was positively correlated with stem hydraulic conductivity but negatively correlated with percent loss of conductivity and water potential at 50% loss of conductivity, (t/b)2 and specific leaf area. Therefore, plants with better hydraulic and growth states at low elevations could allocate more resources to building up solid mechanical supporting systems to cope with high wind load, while those at high elevations with impaired growth states and limited hydraulic functions had to invest more resources in leaves under the harsh environment. The weak trade‐off between hydraulic efficiency and safety (lower hydraulic efficiency and high risk of embolism) could limit the growth and distribution of A. fabri at timberlines; however, global warming trends may facilitate hydraulic transport and benefit plants' growth in the future. Read the free Plain Language Summary for this article on the Journal blog.

Fungal sporocarp productivity and diversity shaped by weather conditions in Pinus uncinata stands

Pinus uncinata is a species frequently growing in the tree line limit of southwestern European subalpine ecosystems, hosting fungal communities that provide relevant ecosystem services. Despite the high importance of these forests for conservation, limited prior research has analysed the sporocarp fruiting patterns. This study aims at filling this gap in knowledge by describing interannual changes in sporocarp productivity, diversity, and community composition over 5 years and identifying possible relationships with the main weather and soil physicochemical characteristics. The studied P. uncinata stands had an average annual total sporocarp productivity of 21.60 kg ha-1yr−1 in dry weight, hosting a total of 255 species with productivity-dominant ectomycorrhizal families such as Russulaceae and Tricholomataceae that occurred mostly from mid-August to end September. In contrast, saprotrophs showed more stable productivity values from mid-August to end of November with average annual sporocarp productivity of 1.76 kg ha-1yr−1 in dry weight but exhibited higher fungal richness values from the start of October until the end of November. Fungal productivity and species richness were mostly influenced by weather conditions while soil physicochemical properties had implications in shaping the sporocarp community composition. This study quantifies the interannual fungal fruiting pattern of the understudied fungal communities and underlines the importance of climatic variables in shaping fungal fruiting patterns within a climate change context. In addition, this work is also of special relevance since P. uncinata ecosystems represent the tree line limit, which makes this species and its associated fungal communities especially sensitive to the potential impacts of global warming.

Changes in movement, habitat use, and response to human disturbance accompany parturition events in bighorn sheep (Ovis canadensis)

Parturition and the early neonatal period are critical life history stages in ungulates with considerable implications for population growth and persistence. Understanding the changes in behaviour induced by ungulate parturition is important for supporting effective population management, but reliably identifying birth sites and dates presents a challenge for managers. Rocky Mountain bighorn sheep (Ovis canadensis canadensis) are one such highly valued and ecologically important species in montane and subalpine ecosystems of Western North America. In the face of changing patterns of anthropogenic land use, wildlife managers increasingly require site-specific knowledge of the movement and habitat selection characteristics of periparturient sheep to better inform land use planning initiatives and ensure adequate protections for lambing habitat. We used movement data from GPS collared parturient (n = 13) and non-parturient (n = 8) bighorn sheep in Banff National Park, Canada to (1) identify lambing events based on changes in key movement metrics, and (2) investigate how resource selection and responses to human use change during the periparturient period. We fit a hidden Markov model (HMM) to a multivariate characterization of sheep movement (step length, daily home range area, residence time) to predict realistic lambing dates for the animals in our study system. Leave-one-out cross validation of our model resulted in a 93% success rate for parturient females. Our model, which we parameterized using data from known parturient females, also predicted lambing events in 25% of known non-parturient ewes in a test dataset. Using a latent selection difference function and resource selection functions, we tested for postpartum changes in habitat use, as well as seasonal differences in habitat selection. Immediately following lambing, ewes preferentially selected high-elevation sites on solar aspects that were more rugged, closer to escape terrain, and further from roads. Within-home range habitat selection was similar between individuals in different reproductive states, but parturient ewes had stronger selection for low snow depth, sites closer to barren ground, and sites further from trails. We propose that movement-based approaches such as HMMs are a valuable tool for identifying critical parturition habitat in species with complex movement patterns and may have particular utility in study areas without access to extensive field observations or vaginal implant transmitters. Furthermore, our results suggest that managers should minimize human disturbance in lambing areas to avoid interfering with maternal behaviour and ensure access to a broad range of suitable habitat in the periparturient period.