Earth & Environmental Sciences

Biogeography is a dynamic field that has transformed dramatically over the last few decades from being necessarily descriptive to become a rigorous science. Major recent areas of growth have included phylogenetics and phylogeography, microbial biogeography and metagenomics, and macroecology. However, the welcome recent deluge of massive amounts of data, in particular from genomics, museum specimens, and field observations, as well as environmental information, is posing a huge challenge to the field. The society has several key roles, not only to serve as a home for researchers in the field and enabling interaction among them, but also: (1) to provide a forum to facilitate awareness and use of rapidly developing tools and data; (2) to encourage a solid foundation in organismal research, with emphasis on field and museum based resources; (3) to promote global connections; and (4) to cultivate interdisciplinarity, such that the predictive capabilities of the field can be used to inform management and policy.

Machine learning (ML) applications in soil science have significantly increased over the past two decades, reflecting a growing trend towards data-driven research addressing soil security. This extensive application has mainly focused on enhancing predictions of soil properties, particularly soil organic carbon, and improving the accuracy of digital soil mapping (DSM). Despite these advancements, the application of ML in soil science faces challenges related to data scarcity and the interpretability of ML models. There is a need for a shift towards Soil Science-Informed ML (SoilML) models that use the power of ML but also incorporate soil science knowledge in the training process to make predictions more reliable and generalisable. This paper proposes methodologies for embedding ML models with soil science knowledge to overcome current limitations. Incorporating soil science knowledge into ML models involves using observational priors to enhance training datasets, designing model structures which reflect soil science principles, and supervising model training with soil science-informed loss functions. The informed loss functions include observational constraints, coherency rules such as regularisation to avoid overfitting, and prior or soil-knowledge constraints that incorporate existing information about the parameters or outputs. By way of illustration, we present examples from four fields: digital soil mapping, soil spectroscopy, pedotransfer functions, and dynamic soil property models. We discuss the potential to integrate process-based models for improved prediction, the use of physics-informed neural networks, limitations, and the issue of overparametrisation. These approaches improve the relevance of ML predictions in soil science and enhance the models’ ability to generalise across different scenarios while maintaining soil science principles, transparency and reliability.

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system's internal variability.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO₂ levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO₂ levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO₂ provided only slight amelioration. By contrast, elevated CO₂ mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO₂ or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

Lianas (woody climbers) are crucial components of tropical forests and they have been increasingly recognized to have profound effects on tropical forest carbon dynamics. Despite their importance, lianas' representation in vegetation models remains limited, partly due to the complexity of liana-tree dynamics and the diversity in liana life history strategies. This paper provides a comprehensive review of advances and challenges for mechanistically representing lianas in forest ecosystem models and a proposed path towards effectively representing lianas in these models. Defining a liana plant functional type is a significant challenge because of the high morphological and physiological diversity amongst liana species, and because of their structural association with trees. Here, we identify critical liana traits that likely should contribute to establishing a liana plant functional type, along with key processes to properly represent lianas in ecosystem models. Subsequently, we discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements. A fundamental redesign of the tree-centric demographic vegetation models seems appropriate to accommodate the unique growth and competition strategies of lianas. We illustrate the potential of such models with a single-site case study where we disentangle putative mechanisms of liana increasing abundance. Furthermore, we underscore the critical need for comprehensive liana demographic and functional data (including long-term, physiological, and pantropical observations) for the qualitative implementation and evaluation in the proposed modeling efforts. Currently, there is a scarcity of liana data and the data that do exist have a neotropical bias. We finally introduce a new liana functional trait database that can centralize existing liana trait data, incentivize improved data gathering and thus facilitate model development and scientific analyses.

Aluminium (Al)-tolerant beneficial bacteria confer resistance to Al toxicity to crops in widely distributed acidic soils. However, the mechanism by which microbial consortia maintain Al tolerance under acid and Al toxicity stress remains unknown. Here, we demonstrate that a soil bacterial consortium composed of Rhodococcus erythropolis and Pseudomonas aeruginosa exhibit greater Al tolerance than either bacterium alone. P. aeruginosa releases the quorum sensing molecule 2-heptyl-1H-quinolin-4-one (HHQ), which is efficiently degraded by R. erythropolis. This degradation reduces population density limitations and further enhances the metabolic activity of P. aeruginosa under Al stress. Moreover, R. erythropolis converts HHQ into tryptophan, promoting the synthesis of peptidoglycan, a key component for cell wall stability, thereby improving the Al tolerance of R. erythropolis. This study reveals a metabolic cross-feeding mechanism that maintains microbial Al tolerance, offering insights for designing synthetic microbial consortia to sustain food security and sustainable agriculture in acidic soil regions.

Abstract. The hydrological cycle of sub-arctic areas is dominated by the snowmelt event. An understanding of the mechanisms that control water fluxes during high-volume infiltration events in sub-arctic till soils is needed to assess how future changes in the timing and magnitude of snowmelt can affect soil water storage dynamics. We conducted a tracer experiment in which deuterated water was used to irrigate a plot on a forested hilltop in Lapland, tracked water fluxes of different mobility and monitored how the later snowmelt modifies the labelled soil water storage. We used lysimeters and destructive soil coring for soil water sampling and monitored and sampled the groundwater. Large spatiotemporal variability between the waters of different mobility was observed in the subsurface, while surface water flow during the tracer experiment was largely controlled by a fill-and-spill mechanism. Extensive soil saturation induced the flow of labelled water into the roots of nearby trees. We found that labelled water remained in deeper soil layers over the winter, but the snowmelt event gradually displaced all deuterated water and fully homogenized all water fluxes at the soil–vegetation interface. The conditions required for the full displacement of the old soil water occur only during a snowmelt with a persistently high groundwater table. We propose a conceptual model where infiltration into the soil and eventual soil water replenishment occur in three stages. First, unsaturated macropore flow is initiated via the surface microtopography and is directed towards the groundwater storage. The second stage is characterized by groundwater rise through the macropore network, subsequent pore water saturation and increased horizontal connectivity of macropores. Shallow subsurface lateral fluxes develop in more permeable shallow soil layers. In the third stage, which materializes during a long period with a high groundwater table and high hydrological connectivity within the soil, the soil water is replenished via enhanced matrix flow and pore water exchange with the macropore network.

This study presents a multidisciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF) disposal. The framework consists of (1) OpenMC for simulating neutronics, fuel depletion, and radioactive decays; (2) NWPY for computing the repository footprint given the thermal constraints; and (3) PFLOTRAN for simulating radionuclide transport in the geosphere to quantify the repository performance and environmental impact. We first perform the meta-analysis of past comparative analyses to identify the factors that led previously to their inconsistent conclusions. We then demonstrate the new framework by comparing five reactor types. Our analysis highlights the granularity and the specificities of each reactor and fuel type so that we should avoid making sweeping conclusions about advanced reactor SNF. Significant findings are that (1) the repository footprint is neither linearly related to SNF volume nor to decay heat, due to the repositorys thermal constraint (2), fast reactors have significantly higher I-129 inventory, which is often the primary dose contributor, and (3) the repository performance primarily depends on the waste forms. The TRISO-based reactors, in particular, have significantly higher SNF volumes compared to the others but result in smaller repository footprints and lower peak dose rates. The open-source framework ensures proper multidisciplinary connections between reactor simulations and environmental assessments, as well as the transparency/traceability required for such comparative analyses. It aims to support reactor designers, repository developers, and policymakers in evaluating the impact of different reactor designs, with the ultimate goal of improving the sustainability of nuclear energy systems.