Sustainable agricultural practices contribute significantly to One Health 

Yan et al., 2022.

The One Health concept proposes that the health of humans, animals, and the environment are interconnected. Agricultural production is a critical component of One Health as food links the environment to human health. Food not only provides nutrients to humans but also represents an important pathway for human exposure to environmental microbes as well as potentially harmful agrochemicals. In addition, inappropriate agronomic practices can cause damage to the environment which can have unintended adverse impacts on human health. Therefore, improving agricultural production systems and protecting environmental health should not be viewed as isolated goals as they are strongly interlinked. Here, we used the nexus of soil, plant, and human microbiomes to discuss sustainable agricultural production from the One Health perspective. We highlighted three interconnected challenges faced by current agronomic practices: the transmissions of pathogens in soil-human microbial loops, the dissemination of antibiotic resistance genes in agroecosystems, and the impacts of chemical pesticides on humans and environmental health. Finally, we propose the potential of utilising microbiomes for better sustainable agronomic practices to contribute to key goals of the One Health concept.

https://onlinelibrary.wiley.com/doi/full/10.1002/sae2.12019

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A keystone gene underlies the persistence of an experimental food web

Barbour et al., 2022. 

In the past few decades, the identification of keystone species, that is, those with essential roles in structuring a community or ecosystem, has increased across systems. Barbour et al. extended this concept to genes, showing that a single allele of a particular plant defense gene facilitates species coexistence across a small experimental trophic system. Specifically, plants with this allele grew faster, supporting larger populations of two species of herbivores and their predators. This finding suggests that genotype variation can play a role in the structure and function of organismal systems. 

Genes encode information that determines an organism’s fitness. Yet we know little about whether genes of one species influence the persistence of interacting species in an ecological community. Here, we experimentally tested the effect of three plant defense genes on the persistence of an insect food web and found that a single allele at a single gene promoted coexistence by increasing plant growth rate, which in turn increased the intrinsic growth rates of species across multiple trophic levels. Our discovery of a “keystone gene” illustrates the need to bridge between biological scales, from genes to ecosystems, to understand community persistence.

https://www.science.org/doi/epdf/10.1126/science.abf2232

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Landscape complexity and functional groups moderate the effect of diversified farming on biodiversity: A global meta-analysis  

Sánchez et al., 2022.

Homogenisation and intensification of agricultural ecosystems are among the most important threats to biodiversity, linked to declines in pollinators, soil biota and ecosystem functioning. Diversification has been proposed as a way to restore ecosystem functioning in agricultural landscapes. To manage agricultural land for multiple ecosystem functions, evidence is needed of the effect of diversification on functionally distinct taxa. We contribute to closing this knowledge gap through a global meta-analysis of 161 peer-reviewed articles addressing the abundance and richness of six distinct functional groups: autotrophs, decomposers, natural enemies, pests, pollinators, and other. We found diversified farming systems increased overall species richness by 26% on average, relative to simplified farming systems. However, the effect of diversified farming on the overall mean abundance was weak. Our study shows diversified farming systems enhanced the abundance and richness of beneficial species while reducing the abundance of pests (e.g., weeds, herbivores), thus providing benefits for both agricultural production and biodiversity. The positive effect of diversified farming systems on the overall mean species richness was stronger in farms in more simplified landscapes, i.e., those that are further from natural and semi-natural habitats or have a lower proportion of seminatural vegetation in a 1 km radius. Pollinator’s abundance and richness were highest in diversified farming plots located far away from natural and semi-natural habitats. In contrast, proximity to these natural and semi-natural habitats (<250 m) increased the positive effect of diversified farming systems on natural enemies’ abundance, while reducing the number of pests. Our results add to the body of evidence calling for the repurposing of policies, regulations, and international agendas to promote, support and incentivize the adoption of diversified farming practices for supporting biodiversity. Spatial planning of diversification schemes should consider the landscape context of farms to ensure the greatest benefit of intervention.

Mean effect size of farming systems (diversified vs. simplified) on abundance from the interaction between functional groups and: A) the percentage of landscape covered by natural and semi-natural habitats; B) land cover Shannon's diversity index; C) categorical variables for the nearest Euclidean distance to natural or semi-natural habitat. Lines and dots = mean effect sizes in LRR. Error bars = [ ± 95% CI]. *Mean effect size significantly different from zero

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https://doi.org/10.1016/j.agee.2022.107933

Machine Learning and Deep Learning -- A review for Ecologists  

Maximilian Pichler, Florian Hartig, 2022.

The popularity of Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) has sharply risen in recent years. Despite their spike in popularity, the inner workings of ML and DL algorithms are perceived as opaque, and their relationship to classical data analysis tools remains debated. It is often assumed that ML and DL excel primarily at making predictions. Recently, however, they have been increasingly used for classical analytical tasks traditionally covered by statistical models. Moreover, recent reviews on ML have focused exclusively on DL, missing out on synthesizing the wealth of ML algorithms with different advantages and general principles. Here, we provide a comprehensive overview of ML and DL, starting with their historical developments, their algorithm families, their differences from traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL excel at prediction tasks, and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends, particularly scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future.

https://arxiv.org/abs/2204.05023

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Permanence via invasion graphs: Incorporating community assembly into Modern Coexistence Theory  

Josef Hofbauer, Sebastian J. Schreiber, 2022.

To understand the mechanisms underlying species coexistence, ecologists often study invasion growth rates of theoretical and data-driven models. These growth rates correspond to average per-capita growth rates of one species with respect to an ergodic measure supporting other species. In the ecological literature, coexistence often is equated with the invasion growth rates being positive. Intuitively, positive invasion growth rates ensure that species recover from being rare. To provide a mathematically rigorous framework for this approach, we prove theorems that answer two questions: (i) When do the signs of the invasion growth rates determine coexistence? (ii) When signs are sufficient, which invasion growth rates need to be positive? We focus on deterministic models and equate coexistence with permanence, i.e., a global attractor bounded away from extinction. For models satisfying certain technical assumptions, we introduce invasion graphs where vertices correspond to proper subsets of species (communities) supporting an ergodic measure and directed edges correspond to potential transitions between communities due to invasions by missing species. These directed edges are determined by the signs of invasion growth rates. When the invasion graph is acyclic (i.e. there is no sequence of invasions starting and ending at the same community), we show that permanence is determined by the signs of the invasion growth rates. In this case, permanence is characterized by the invasibility of all -i communities, i.e., communities without species i where all other missing species having negative invasion growth rates. We show that dissipative Lotka-Volterra models satisfy our technical assumptions and computing their invasion graphs reduces to solving systems of linear equations. We provide additional applications of the results and discuss open problems.

https://arxiv.org/abs/2204.03773

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