Dynamics and drivers of mycorrhizal fungi after glacier retreat.

The development of terrestrial ecosystems depends greatly on plant mutualists such as mycorrhizal fungi. The global retreat of glaciers exposes nutrient-poor substrates in extreme environments and provides a unique opportunity to study early successions of mycorrhizal fungi by assessing their dynamics and drivers. We combined environmental DNA metabarcoding and measurements of local conditions to assess the succession of mycorrhizal communities during soil development in 46 glacier forelands around the globe, testing whether dynamics and drivers differ between mycorrhizal types. Mycorrhizal fungi colonized deglaciated areas very quickly (< 10 yr), with arbuscular mycorrhizal fungi tending to become more diverse through time compared to ectomycorrhizal fungi. Both alpha- and beta-diversity of arbuscular mycorrhizal fungi were significantly related to time since glacier retreat and plant communities, while microclimate and primary productivity were more important for ectomycorrhizal fungi. The richness and composition of mycorrhizal communities were also significantly explained by soil chemistry, highlighting the importance of microhabitat for community dynamics. The acceleration of ice melt and the modifications of microclimate forecasted by climate change scenarios are expected to impact the diversity of mycorrhizal partners. These changes could alter the interactions underlying biotic colonization and belowground-aboveground linkages, with multifaceted impacts on soil development and associated ecological processes.

Less extreme and earlier outbursts of ice-dammed lakes since 1900.

Episodic failures of ice-dammed lakes have produced some of the largest floods in history, with disastrous consequences for communities in high mountains1-7. Yet, estimating changes in the activity of ice-dam failures through time remains controversial because of inconsistent regional flood databases. Here, by collating 1,569 ice-dam failures in six major mountain regions, we systematically assess trends in peak discharge, volume, annual timing and source elevation between 1900 and 2021. We show that extreme peak flows and volumes (10 per cent highest) have declined by about an order of magnitude over this period in five of the six regions, whereas median flood discharges have fallen less or have remained unchanged. Ice-dam floods worldwide today originate at higher elevations and happen about six weeks earlier in the year than in 1900. Individual ice-dammed lakes with repeated outbursts show similar negative trends in magnitude and earlier occurrence, although with only moderate correlation to glacier thinning8. We anticipate that ice dams will continue to fail in the near future, even as glaciers thin and recede. Yet widespread deglaciation, projected for nearly all regions by the end of the twenty-first century9, may bring most outburst activity to a halt.

A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran.

We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.

The cyclic expansion and contraction characteristics of a loess slope and implications for slope stability.

Loess covers approximately 6.6% of China and forms thick extensive deposits in the northern and northwestern parts of the country. Natural erosional processes and human modification of thick loess deposits have produced abundant, potentially unstable steep slopes in this region. Slope deformation monitoring aimed at evaluating the mechanical behavior of a loess slope has shown a cyclic pattern of contraction and expansion. Such cyclic strain change on the slope materials can damage the loess and contribute to slope instability. The site showing this behavior is a 70-m high loess slope near Yan'an city in Shanxi Province, northwest China. A Ground-Based Synthetic Aperture Radar (GB-SAR) sensor and a displacement meter were used to monitor this cyclic deformation of the slope over a one-year period from September 2018 to August 2019. It is postulated that this cyclic behavior corresponds to thermal and moisture fluctuations, following energy conservation laws. To investigate the validity of this mechanism, physical models of soil temperature and moisture measured by hygrothermographs were used to simulate the observed cyclic deformations. We found good correlations between the models based on the proposed mechanism and the exhibited daily and annual cyclic contraction and expansion. The slope absorbed energy from the time of maximum contraction to the time of maximum expansion, and released energy from the time of maximum expansion to the time of maximum contraction. Recoverable cyclic deformations suggest stresses in the loess are within the elastic range, and non-recoverable cyclic deformations suggest damage of the loess material (breakage of bonds between soil grains), which could lead to instability. Based on these observations and the models, we developed a quantitative relationship between weather cycles and thermal deformation of the slope. Given the current climate change projections of temperature increases of up to 3.5 °C by 2100, the model estimates the loess slope to expand about 0.35 mm in average, which would be in addition to the current cyclic "breathing" behavior experienced by the slope.

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

Observations on the May 2019 Joffre Peak landslides, British Columbia.

Two catastrophic landslides occurred in quick succession on 13 and 16 May 2019, from the north face of Joffre Peak, Cerise Creek, southern Coast Mountains, British Columbia. With headscarps at 2560 m and 2690 m elevation, both began as rock avalanches, rapidly transforming into debris flows along middle Cerise Creek, and finally into debris floods affecting the fan. Beyond the fan margin, a flood surge on Cayoosh Creek reached bankfull and attenuated rapidly downstream; only fine sediment reached Duffey Lake. The toe of the main debris flow deposit reached 4 km from the headscarp, with a travel angle of 0.28, while the debris flood phase reached the fan margin 5.9 km downstream, with a travel angle of 0.22. Photogrammetry indicates the source volume of each event is 2-3 Mm3, with combined volume of 5 Mm3. Lidar differencing, used to assess deposit volume, yielded a similar total result, although error in the depth estimate introduced large volume error masking the expected increase due to dilation and entrainment. The average velocity of the rock avalanche-debris flow phases, from seismic analysis, was ~ 25-30 m/s, and the velocity of the 16 May debris flood on the upper fan, from super-elevation and boulder sizes, was 5-10 m/s. The volume of debris deposited on the fan was ~ 104 m3, 2 orders of magnitude less than the avalanche/debris flow phases. Progressive glacier retreat and permafrost degradation were likely the conditioning factors; precursor rockfall activity was noted at least ~6 months previous; thus, the mountain was primed to fail. The 13 May landslide was apparently triggered by rapid snowmelt, with debuttressing triggering the 16 May event.

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Lithostratigraphic and magnetostratigraphic data from late Cenozoic glacial and proglacial sequences underlying the Altiplano at La Paz, Bolivia.

We provide lithostratigraphic and magnetostratigraphic data derived from a Plio-Pleistocene continental sediment sequence underlying the Altiplano plateau at La Paz, Bolivia. The record comprises six sections along the upper Río La Paz valley, totaling over one kilometre of exposure and forming a ~20-km transect oblique to the adjacent Cordillera Real. Lithostratigraphic characterization includes lithologic and stratigraphic descriptions of units and their contacts. We targeted gravel and diamicton units for paleomagnetic sampling to address gaps in the only previous magnetostratigraphic study from this area. Paleomagnetic data - magnetic susceptibility and primary remanent magnetization revealed by progressive alternating field demagnetization - are derived from 808 individually oriented samples of flat-lying, fine-grained sediments. The datasets enable characterization of paleo-surfaces within the sequence, correlation between stratigraphic sections, and differentiation of asynchronous, but lithologically similar units. Correlation of the composite polarity sequence to the geomagnetic polarity time scale supports a range of late Cenozoic paleoenvironmental topics of regional to global importance: the number and ages of early glaciations in the tropical Andes; interhemispheric comparison of paleoclimate during the Plio-Pleistocene climatic transition; timing of and controls on inter-American faunal exchange; and the variability of Earth's paleomagnetic field.

Multiple tropical Andean glaciations during a period of late Pliocene warmth.

The extent and behaviour of glaciers during the mid-Piacenzian warm period illustrate the sensitivity of the cryosphere to atmospheric CO2 concentrations above pre-industrial levels. Knowledge of glaciation during this period is restricted to globally or regionally averaged records from marine sediments and to sparse terrestrial glacial deposits in mid-to-high latitudes. Here we expand the Pliocene glacial record to the tropics by reporting recurrent large-scale glaciation in the Bolivian Andes based on stratigraphic and paleomagnetic analysis of a 95-m sequence of glacial sediments underlying the 2.74-Ma Chijini Tuff. Paleosols and polarity reversals separate eight glacial diamictons, which we link to cold periods in the benthic oxygen isotope record. The glaciations appear to coincide with the earliest glacial activity at high northern latitudes and with events in Antarctica, including the strong M2 cold peak and terminal Pliocene climate deterioration. This concordance suggests inter-hemispheric climate linkages during the late Pliocene and requires that the Central Andes were at least as high in the late Pliocene as today. Our record fills a critical gap in knowledge of Earth systems during the globally warm mid-Piacenzian and suggests a possible driver of faunal migration preceding the large-scale biotic interchange in the Americas during the earliest Pleistocene.

Assessing and managing multiple risks in a changing world-The Roskilde recommendations.

Roskilde University (Denmark) hosted a November 2015 workshop, Environmental Risk-Assessing and Managing Multiple Risks in a Changing World. This Focus article presents the consensus recommendations of 30 attendees from 9 countries regarding implementation of a common currency (ecosystem services) for holistic environmental risk assessment and management; improvements to risk assessment and management in a complex, human-modified, and changing world; appropriate development of protection goals in a 2-stage process; dealing with societal issues; risk-management information needs; conducting risk assessment of risk management; and development of adaptive and flexible regulatory systems. The authors encourage both cross-disciplinary and interdisciplinary approaches to address their 10 recommendations: 1) adopt ecosystem services as a common currency for risk assessment and management; 2) consider cumulative stressors (chemical and nonchemical) and determine which dominate to best manage and restore ecosystem services; 3) fully integrate risk managers and communities of interest into the risk-assessment process; 4) fully integrate risk assessors and communities of interest into the risk-management process; 5) consider socioeconomics and increased transparency in both risk assessment and risk management; 6) recognize the ethical rights of humans and ecosystems to an adequate level of protection; 7) determine relevant reference conditions and the proper ecological context for assessments in human-modified systems; 8) assess risks and benefits to humans and the ecosystem and consider unintended consequences of management actions; 9) avoid excessive conservatism or possible underprotection resulting from sole reliance on binary, numerical benchmarks; and 10) develop adaptive risk-management and regulatory goals based on ranges of uncertainty. Environ Toxicol Chem 2017;36:7-16. © 2016 SETAC.