The impact of fungi on soil protist communities in European cereal croplands.

Protists, a crucial part of the soil food web, are increasingly acknowledged as significant influencers of nutrient cycling and plant performance in farmlands. While topographical and climatic factors are often considered to drive microbial communities on a continental scale, higher trophic levels like heterotrophic protists also rely on their food sources. In this context, bacterivores have received more attention than fungivores. Our study explored the connection between the community composition of protists (specifically Rhizaria and Cercozoa) and fungi across 156 cereal fields in Europe, spanning a latitudinal gradient of 3000 km. We employed a machine-learning approach to measure the significance of fungal communities in comparison to bacterial communities, soil abiotic factors, and climate as determinants of the Cercozoa community composition. Our findings indicate that climatic variables and fungal communities are the primary drivers of cercozoan communities, accounting for 70% of their community composition. Structural equation modelling (SEM) unveiled indirect climatic effects on the cercozoan communities through a change in the composition of the fungal communities. Our data also imply that fungivory might be more prevalent among protists than generally believed. This study uncovers a hidden facet of the soil food web, suggesting that the benefits of microbial diversity could be more effectively integrated into sustainable agriculture practices.

The impact of agricultural management on soil aggregation and carbon storage is regulated by climatic thresholds across a 3000 km European gradient.

Organic carbon and aggregate stability are key features of soil quality and are important to consider when evaluating the potential of agricultural soils as carbon sinks. However, we lack a comprehensive understanding of how soil organic carbon (SOC) and aggregate stability respond to agricultural management across wide environmental gradients. Here, we assessed the impact of climatic factors, soil properties and agricultural management (including land use, crop cover, crop diversity, organic fertilization, and management intensity) on SOC and the mean weight diameter of soil aggregates, commonly used as an indicator for soil aggregate stability, across a 3000 km European gradient. Soil aggregate stability (-56%) and SOC stocks (-35%) in the topsoil (20 cm) were lower in croplands compared with neighboring grassland sites (uncropped sites with perennial vegetation and little or no external inputs). Land use and aridity were strong drivers of soil aggregation explaining 33% and 20% of the variation, respectively. SOC stocks were best explained by calcium content (20% of explained variation) followed by aridity (15%) and mean annual temperature (10%). We also found a threshold-like pattern for SOC stocks and aggregate stability in response to aridity, with lower values at sites with higher aridity. The impact of crop management on aggregate stability and SOC stocks appeared to be regulated by these thresholds, with more pronounced positive effects of crop diversity and more severe negative effects of crop management intensity in nondryland compared with dryland regions. We link the higher sensitivity of SOC stocks and aggregate stability in nondryland regions to a higher climatic potential for aggregate-mediated SOC stabilization. The presented findings are relevant for improving predictions of management effects on soil structure and C storage and highlight the need for site-specific agri-environmental policies to improve soil quality and C sequestration.

Preliminary quantitative assessment of the multidimensional impact of the COVID-19 pandemic on Sustainable Development Goals.

The intersectoral impacts of the COVID-19 pandemic on humanity raises concerns about its implications for sustainable development. Here, we examine a global quantitative impact of COVID-19 pandemic on Sustainable Development Goals (SDGs) across all 17 goals using 65 proxy indicators across 72 countries collected from April 2020 to February 2021. Our data-driven analysis indicated that adverse impacts of the pandemic have been particularly concerned on gender equality (Goal 5), affordable and clean energy (Goal 7), decent work and economic growth (Goal 8), sustainable cities and communities (Goal 11), and responsible consumption and production (Goal 12) with global scores estimated to be -0.38, -0.21, -0.28, -0.22 and -0.16, respectively. Country income level was a variable that strongly differentiates the responses to the pandemic (e.g., lower incomes had 14 negative goals compared to 11 and 4 negative goals assigned to middle- and high-income countries, respectively). However, Goals 5 and 8 were highly impacted worldwide regardless of income status. Furthermore, countries that had already higher performance in SDGs were less impacted by the pandemic, highlighting the importance of progress on the SDGs in increasing societal resilience to pandemics. The findings provide insights into the reinforcement of recovery policies (e.g., protecting vulnerable groups and transitioning to a green economy) and a basis for a quantitative discussion on the sectors to be prioritized.

Plant and soil biodiversity have non-substitutable stabilising effects on biomass production.

The stability of plant biomass production in the face of environmental change is fundamental for maintaining terrestrial ecosystem functioning, as plant biomass is the ultimate source of energy for nearly all life forms. However, most studies have focused on the stabilising effect of plant diversity, neglecting the effect of soil biodiversity, the largest reservoir of biodiversity on Earth. Here we investigated the effects of plant and soil biodiversity on the temporal stability of biomass production under varying simulated precipitation in grassland microcosms. Soil biodiversity loss reduced temporal stability by suppressing asynchronous responses of plant functional groups. Greater plant diversity, especially in terms of functional diversity, promoted temporal stability, but this effect was independent of soil biodiversity loss. Moreover, multitrophic biodiversity, plant and soil biodiversity combined, was positively associated with temporal stability. Our study highlights the importance of maintaining both plant and soil biodiversity for sustainable biomass production.

Classifying human influences on terrestrial ecosystems.

Human activity is affecting every ecosystem on Earth, with terrestrial biodiversity decreasing rapidly. Human influences materialize in the form of numerous, jointly acting factors, yet the experimental study of such joint impacts is not well developed. We identify the absence of a systematic ordering system of factors according to their properties (traits) as an impediment to progress and offer an a priori trait-based factor classification to illustrate this point, starting at the coarsest level with the physical, biological or chemical nature of factors. Such factor classifications can serve in communication of science, but also can be used as heuristic tools to develop questions and formulate new hypotheses, or as predictors of effects, which we explore here. We hope that classifications such as the one proposed here can help shift the spotlight on the multitude of anthropogenic changes affecting ecosystems, and that such classifications can be used to help unravel joint impacts of a great number of factors.

Rapid functional shifts across high latitude forests over the last 65 years.

Global environmental changes have strongly affected forest demographic rates, particularly amplified tree mortality in high latitude forests (e.g., two to five times greater mortality probability over the half-century). Although forest functional composition is critical for multitrophic biodiversity and ecosystem functioning, it remains unclear how functional composition has changed over time across large high latitude regions, which have been warming twice the rate of the globe as a whole. Using extensive spatial and long-term forest inventory data (17,107 plots monitored 1951-2016) across Canada, we found that after accounting for stand age-dependent functional shifts, functional composition shifted toward fast-growing deciduous broadleaved trees and higher drought tolerance over time. The temporal shift toward deciduous broadleaved trees was consistent across the baseline climate. However, over the study period, drought tolerance increased (or shade tolerance decreased) by 300% in colder boreal regions, while drought tolerance did not shift significantly in warmer temperate climates. A further analysis accounting for temporal changes in atmospheric CO2 , temperature, and water availability indicated that the functional composition of colder regions shifted toward drought tolerance more rapidly with rising CO2 than warmer regions, suggesting the greater vulnerability of boreal forests than temperate forests under ongoing global environmental changes. Future ecosystem management practices should consider spatial differences in functional responses to global environmental change, focusing on high latitude forests experiencing higher rates of warming and compositional changes.

Ten simple rules for increased lab resilience.

When running a lab we do not think about calamities, since they are rare events for which we cannot plan while we are busy with the day-to-day management and intellectual challenges of a research lab. No lab team can be prepared for something like a pandemic such as COVID-19, which has led to shuttered labs around the globe. But many other types of crises can also arise that labs may have to weather during their lifetime. What can researchers do to make a lab more resilient in the face of such exterior forces? What systems or behaviors could we adjust in 'normal' times that promote lab success, and increase the chances that the lab will stay on its trajectory? We offer 10 rules, based on our current experiences as a lab group adapting to crisis.

Myristate and the ecology of AM fungi: significance, opportunities, applications and challenges.

A recent study by Sugiura and coworkers reported the non-symbiotic growth and spore production of an arbuscular mycorrhizal (AM) fungus, Rhizophagus irregularis, when the fungus received an external supply of certain fatty acids, myristates (C:14). This discovery follows the insight that AM fungi receive fatty acids from their hosts when in symbiosis. If this result holds up and can be repeated under nonsterile conditions and with a broader range of fungi, it has numerous consequences for our understanding of AM fungal ecology, from the level of the fungus, at the plant community level, and to functional consequences in ecosystems. In addition, myristate may open up several avenues from a more applied perspective, including improved fungal culture and supplementation of AM fungi or inoculum in the field. We here map these potential opportunities, and additionally offer thoughts on potential risks of this potentially new technology. Lastly, we discuss the specific research challenges that need to be overcome to come to an understanding of the potential role of myristate in AM ecology.

Predicting the ecological impacts of large-dam removals on a river network based on habitat-network structure and flow regimes.

Large dams provide vital protection and services to humans. However, an increasing number of large dams worldwide are old and not operating properly. The removal of large dams has excellent potential to restore habitat connectivity and flow regimes; therefore, projecting the related ecological consequences is an emerging need for water resource and ecosystem management. However, no modeling methods are currently available for such projections at the basin scale. We devised a scheme that integrates changes in flow regimes and habitat network structure into a basin-scale impact assessment of removal of large dams and applied it to the Nagara-Ibi Basin, Japan. We used a graph-theoretical approach and a hydrological model, to quantify changes in habitat availability for 11 freshwater fishes at the basin scale under multiple removal scenarios. We compared these results with the change predicted using a conventional scheme that considered only changes to the habitat network due to dam removal. Our proposed scheme revealed that an increase in flow variability associated with dam removal projected both positive and negative effects on basin-scale habitat availability, depending on the focal species, endangered species had a negative response to dam removal. In contrast, the conventional approach projected only positive effects for all species. This difference in the outcomes indicates that large-dam removal can have negative and positive effects on watershed restoration due to changes in flow regimes. Our results also suggest the effect of removal of large dams may depend on the dams and their locations. Our study is the first step in projecting ecological trade-offs associated with the removal of large dams on riverscapes at the basin scale and provides a foundation for future process-based watershed restoration.

Root traits are more than analogues of leaf traits: the case for diaspore mass.

Root traits are often thought to be analogues of leaf traits along the plant economics spectrum. But evolutionary pressures have most likely shaped above- and belowground patterns differentially. Here, we aimed to identify the most important aboveground traits for explaining root traits without an a priori focus on known concepts. We measured morphological root traits in a glasshouse experiment on 141 common Central European grassland species. Using random forest algorithms, we built predictive models of six root traits from 97 aboveground morphological, ecological and life history traits. Root tissue density was best predicted by leaf dry matter content, whereas traits related to root fineness were best predicted by diaspore mass: the heavier the diaspore, the coarser the root system. Specific leaf area (SLA) was not an important predictor for any of the root traits. This study confirms the hypothesis that root traits are more than analogues of leaf traits within a plant economics spectrum. The results reveal a novel ecological pattern and highlight the power of root data to close important knowledge gaps in trait-based ecology.

Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small datasets with class imbalance and hierarchical grouping.

Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low, mainly due to the relatively small dataset, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features like color, patchiness, and colony extension rate could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e. phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g. outer or inner hyphae) depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for a better understanding of the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered.

Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning.

Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R2 of 0.71. The overall performance in both countries reached an R2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.

Number and dissimilarity of global change factors influences soil properties and functions.

Soil biota and functions are impacted by various anthropogenic stressors, including climate change, chemical pollution or microplastics. These stressors do not occur in isolation, and soil properties and functions appear to be directionally driven by the number of global change factors acting simultaneously. Building on this insight, we here hypothesize that co-acting factors with more diverse effect mechanisms, or higher dissimilarity, have greater impacts on soil properties and functions. We created a factor pool of 12 factors and calculated dissimilarity indices of randomly-chosen co-acting factors based on the measured responses of soil properties and functions to the single factors. Results show that not only was the number of factors important, but factor dissimilarity was also key for predicting factor joint effects. By analyzing deviations of soil properties and functions from three null model predictions, we demonstrate that higher factor dissimilarity and a larger number of factors could drive larger deviations from null models and trigger more frequent occurrence of synergistic factor net interactions on soil functions (decomposition rate, cellulase, and ß-glucosidase activity), which provides mechanistic insights for understanding high-dimensional effects of factors. Our work highlights the importance of considering factor similarity in future research on interacting factors.

Toward a cohesive understanding of ecological complexity.

Ecological systems are quintessentially complex systems. Understanding and being able to predict phenomena typical of complex systems is, therefore, critical to progress in ecology and conservation amidst escalating global environmental change. However, myriad definitions of complexity and excessive reliance on conventional scientific approaches hamper conceptual advances and synthesis. Ecological complexity may be better understood by following the solid theoretical basis of complex system science (CSS). We review features of ecological systems described within CSS and conduct bibliometric and text mining analyses to characterize articles that refer to ecological complexity. Our analyses demonstrate that the study of complexity in ecology is a highly heterogeneous, global endeavor that is only weakly related to CSS. Current research trends are typically organized around basic theory, scaling, and macroecology. We leverage our review and the generalities identified in our analyses to suggest a more coherent and cohesive way forward in the study of complexity in ecology.

Improving quantitative synthesis to achieve generality in ecology.

Synthesis of primary ecological data is often assumed to achieve a notion of 'generality', through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Unfortunately, ecologists rarely define either the generality of their findings, their estimand (the target of estimation) or the population of interest. Given that generality is fundamental to science, and the urgent need for scientific understanding to curb global scale ecological breakdown, loose usage of the term 'generality' is problematic. In other disciplines, generality is defined as comprising both generalizability-extending an inference about an estimand from the sample to the population-and transferability-the validity of estimand predictions in a different sampling unit or population. We review current practice in ecological synthesis and demonstrate that, when researchers fail to define the assumptions underpinning generalizations and transfers of effect sizes, generality often misses its target. We provide guidance for communicating nuanced inferences and maximizing the impact of syntheses both within and beyond academia. We propose pathways to generality applicable to ecological syntheses, including the development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.

Multiple anthropogenic pressures eliminate the effects of soil microbial diversity on ecosystem functions in experimental microcosms.

Biodiversity is crucial for the provision of ecosystem functions. However, ecosystems are now exposed to a rapidly growing number of anthropogenic pressures, and it remains unknown whether biodiversity can still promote ecosystem functions under multifaceted pressures. Here we investigated the effects of soil microbial diversity on soil functions and properties when faced with an increasing number of simultaneous global change factors in experimental microcosms. Higher soil microbial diversity had a positive effect on soil functions and properties when no or few (i.e., 1-4) global change factors were applied, but this positive effect was eliminated by the co-occurrence of numerous global change factors. This was attributable to the reduction of soil fungal abundance and the relative abundance of an ecological cluster of coexisting soil bacterial and fungal taxa. Our study indicates that reducing the number of anthropogenic pressures should be a goal in ecosystem management, in addition to biodiversity conservation.

Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach.

BACKGROUND: Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints. OBJECTIVES: To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence. METHODS: Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence. RESULTS: The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas. CONCLUSIONS: Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.

Rapid evolution of trait correlation networks during bacterial adaptation to the rhizosphere.

There is a growing awareness that traits do not evolve individually but rather are organized as modular networks of covarying traits. Although the importance of multi-trait correlation has been linked to the ability to evolve in response to new environmental conditions, the evolvability of the network itself has to date rarely been assessed experimentally. By following the evolutionary dynamics of a model bacterium adapting to plant roots, we demonstrate that the whole structure of the trait correlation network is highly dynamic. We experimentally evolved Pseudomonas protegens, a common rhizosphere dweller, on the roots of Arabidopsis thaliana. We collected bacteria at regular intervals and determined a range of traits linked to growth, stress resistance, and biotic interactions. We observed a rapid disintegration of the original trait correlation network. Ancestral populations showed a modular network, with the traits linked to resource use and stress resistance forming two largely independent modules. This network rapidly was restructured during adaptation, with a loss of the stress resistance module and the appearance of new modules out of previously disconnected traits. These results show that evolutionary dynamics can involve a deep restructuring of phenotypic trait organization, pointing to the emergence of novel life history strategies not represented in the ancestral phenotype.

Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions.

Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.