Institute for Environmental Futures

BACKGROUND: Most, if not all, green plant (Virdiplantae) species including angiosperms and ferns are polyploids themselves or have ancient polyploid or whole genome duplication signatures in their genomes. Polyploids are not only restricted to our major crop species such as wheat, maize, potato and the brassicas, but also occur frequently in wild species and natural habitats. Polyploidy has thus been viewed as a major driver in evolution, and its influence on genome and chromosome evolution has been at the centre of many investigations. Mechanistic models of the newly structured genomes are being developed that incorporate aspects of sequence evolution or turnover (low-copy genes and regulatory sequences, as well as repetitive DNAs), modification of gene functions, the re-establishment of control of genes with multiple copies, and often meiotic chromosome pairing, recombination and restoration of fertility. SCOPE: World-wide interest in how green plants have evolved under different conditions - whether in small, isolated populations, or globally - suggests that gaining further insight into the contribution of polyploidy to plant speciation and adaptation to environmental changes is greatly needed. Forward-looking research and modelling, based on cytogenetics, expression studies, and genomics or genome sequencing analyses, discussed in this Special Issue of the Annals of Botany, consider how new polyploids behave and the pathways available for genome evolution. They address fundamental questions about the advantages and disadvantages of polyploidy, the consequences for evolution and speciation, and applied questions regarding the spread of polyploids in the environment and challenges in breeding and exploitation of wild relatives through introgression or resynthesis of polyploids. CONCLUSION: Chromosome number, genome size, repetitive DNA sequences, genes and regulatory sequences and their expression evolve following polyploidy - generating diversity and possible novel traits and enabling species diversification. There is the potential for ever more polyploids in natural, managed and disturbed environments under changing climates and new stresses.

Climate change-induced stresses are perceived by plants at the root-soil interface, where they are alleviated through interactions between the host plant and the rhizosphere microbiome. The recruitment of specific microbiomes helps mitigate stress, increases resistance to pathogens, and promotes plant growth, development, and reproduction. The structure of the rhizosphere microbiome is shaped by crop domestication and variations in ploidy levels. Here we list key genes that regulate rhizosphere microbiomes and host genetic traits. We also discuss the prospects for rigorous analysis of symbiotic interactions, research needs, and strategies for systematically utilizing microbe-crop interactions to improve crop performance. Finally, we highlight challenges of maintaining live rhizosphere microbiome collections and mining heritable variability to enhance interactions between host plants and their rhizosphere microbiomes.

<p>Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically.</p>\n<p><br></p>\n<p>This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences.</p>

Shrub encroaching on grasslands threatens grassland ecosystems and negatively affects human land use and livelihood. However, existing research on this subject is limited, having focused on small areas, thus hindering a comprehensive understanding of the diverse patterns of large-scale shrub invasions into grasslands. In recent decades, a significant encroachment of shrubs into Xilin Gol grassland has been observed in China, resulting in considerable damage to the grasslands. Therefore, this study aimed to investigate the rapid expansion of shrub-encroached grassland (SEG) from 1990 to 2020 in the Xilin Gol League, Inner Mongolia Autonomous Region, China, using long-term Landsat images. The spatial distribution characteristics of the new SEG areas were analyzed. The main factors driving the shrub encroachment in the study area were identified using a geographic detector. Our results revealed a continuous expansion of SEG in the study area over the past three decades, marked by an increase of 48.76 × 103 km2 and an average annual gain rate of 9.41 %. The increased SEG was primarily owing to grassland transition. The sprawl of the SEG was the most prominent in the western and northern regions of the study area, extending to gentle slopes of approximately 5° and low-middle elevations between 600–1300 m above mean sea level. Shrub encroachment was primarily influenced by surface temperature, wind speed, and relative humidity, with elevation, slope, and precipitation exerting relatively weaker influences. Rather than being driven by a singular factor, the expansion was a result of the combined influence of various factors. This study provides a valuable case study for understanding shrub invasion dynamics in arid and semi-arid regions globally.

Abstract Flavonoids in Musaceae are involved in pigmentation and stress responses, including cold resistance, and are a component of the healthy human diet. Identification and analysis of the sequence and copy number of flavonoid biosynthetic genes are valuable for understanding the nature and diversity of flavonoid evolution in Musaceae species. In this study, we identified 71–80 flavonoid biosynthetic genes in chromosome-scale genome sequence assemblies of Musaceae, including those of Ensete glaucum, Musella lasiocarpa, Musa beccarii, M. acuminata, M. balbisiana and M. schizocarpa, checking annotations with BLAST and determining the presence of conserved domains. The number of genes increased through segmental duplication and tandem duplication. Orthologues of both structural and regulatory genes in the flavonoid biosynthetic pathway are highly conserved across Musaceae. The flavonoid 3ʹ,5ʹ-hydroxylase gene F3ʹ5ʹH was amplified in Musaceae and ginger compared with grasses (rice, Brachypodium, Avena longiglumis, and sorghum). One group of genes from this gene family amplified near the centromere of chromosome 2 in the x = 11 Musaceae species. Flavonoid biosynthetic genes displayed few consistent responses in the yellow and red bracts of Musella lasiocarpa when subjected to low temperatures. The expression levels of MlDFR2/3 (dihydroflavonol reductase) increased while MlLAR (leucoanthocyanidin reductase) was reduced by half. Overall, the results establish the range of diversity in both sequence and copy number of flavonoid biosynthetic genes during evolution of Musaceae. The combination of allelic variants of genes, changes in their copy numbers, and variation in transcription factors with the modulation of expression under cold treatments and between genotypes with contrasting bract-colours suggests the variation may be exploited in plant breeding programmes, particularly for improvement of stress-resistance in the banana crop.

Abstract Background and Aims The grass genus Urochloa ( Brachiaria ) includes forage crops that are important for beef and dairy industries in tropical and sub-tropical Africa, South America, and Oceania/Australia. Economically important species include U. brizantha , U. decumbens , U. humidicola , U. mutica , U. arrecta , U. trichopus , U. mosambicensis , and M. maximus , all native to the African continent. Perennial growth habits, large, fast growing palatable leaves, intra- and interspecific morphological variability, apomictic reproductive systems, and frequent polyploidy are widely shared within the genus. The combination of these traits likely favoured the selection for forage domestication and weediness, but trait emergence across Urochloa cannot be modelled, as a robust phylogenetic assessment of the genus has not been conducted. Methods Using a target enrichment sequencing approach (Angiosperms353 baits), we inferred a species level phylogeny for Urochloa sensu lato , encompassing 57 species (∼50% of the genus) and outgroups. We determined the phylogenetic placement of agriculturally important species and identify their closest wild relatives. Further, we mapped key traits associated with forage crop potential to the species tree, exploring trait distribution across the genus. Key Results Agricultural species belong to five independent clades, including U. brizantha and U. decumbens lying in a previously defined species complex. Crop wild relatives were identified for these clades supporting previous sub-generic groupings in Urochloa based on morphology. Using ancestral trait estimation models, we find that five morphological traits that correlate with forage potential (perennial growth habits, culm height, leaf size, a winged rachis, and large seeds) independently evolved in forage clades. Conclusions Urochloa s.l. is a highly diverse genus that contains numerous species with agricultural potential, including crop wild relatives that are currently underexploited. The African continent is the centre of origin for these clades and their conservation across their native distributions is essential. Genomic and phenotypic diversity in forage clade species and their wild relatives needs to be better assessed to improve sustainability in Urochloa cultivar production.

The development of a decision framework for landscape governance and management has become one of the prioritised policy instruments for actualising policy objectives relating to agri-food system, biodiversity conservation, nature restoration, environmental management, climate change mitigation and adaptation, net zero greenhouse gas emissions, and the transition to renewable energy supplies in the United Kingdom (UK). However, the landscape lens in policy-making is challenging because of the diverse landscape archetypes, environmental problems, and diverging policy targets that it must address. This highlights the importance of having a robust, evidence-based landscape decisions framework. To address this issue, this study undertakes a systematic review and transdisciplinary synthesis of research outputs from the Landscape Decisions Programme (LDP). This study compiles and synthesizes outputs from the LDP projects in the context of the relevant literature to develop an understanding of the relationships among the emerging evidence with respect to decision-making for more sustainable and multifunctional landscapes. The synthesis analyzed the drivers of landscape decisions, and methodological approaches used to generate evidence for decision-making. The emerging themes from the synthesis were distilled into five principles that can be used as a basis for a roadmap towards the development of a holistic landscape decisions framework.

At COP26, the Glasgow Leaders Declaration committed the majority of the world’s nations toending deforestation by 2030. On 29 June 2023, the EU Regulation on deforestation-freeproducts (EU) 2023/1115 entered into force. The main driver of deforestation and forestdegradation is the expansion of agricultural land for the production of commodities like cattle,wood, cocoa, soy, palm oil, coffee, rubber and derived products. Any trader wishing to sell thesecommodities on the EU single market or export from within it, must prove that the products donot originate from recently deforested land or have contributed to forest degradation.Satellite imagery provides the means of addressing the implementation of the EU Regulationon deforestation-free supply chains, and of strengthening forest governance through theprovision of timely information to national forest services. We present the PyEO near-real-timeforest alert system from Sentinel-2, a current operational application to reduce illegal logging inKenya, and its potential to support im- and exporters in demonstrating deforestation-free supplychains developed by the ForestMind project.The software implementation used the Python for Earth Observation (PyEO) library toautomatically extract information on forest loss from Sentinel-2 satellite imagery. It queries theCopernicus Data Space Ecosystem for new imagery, downloads the automatically selectedSentinel-2 images, applies a previously trained random forest machine learning model to detectforest loss, and generates a multi-layer analyst report.For the forest law enforcement in Kenya, the latest forest alerts are sifted and prioritised bythe Kenya Forest Service’s Forest Information Centre in Nairobi, and delegated to forest rangersin the field for investigation. Forest rangers navigate to the field site inside the forest reserve,accompanied by a local community scout, and report back to head office with their observationsand whether any arrests for illegal logging were made. Since its introduction in Kwale County in2019, over 2000 forest alerts have been investigated. The dominant cause of the deforestation isillegal logging, followed by charcoal production.For the due diligence application, a Forest Analyst can then use the analyst-report andadditional software tools to create company reports suitable for communication to im- andexporters for monitoring the impact of their supply chains on deforestation and forestdegradation. 

Abstract Flavonoids in Musaceae are involved in pigmentation and stress responses, including pathogen defense and UV protection, and are a component of the healthy human diet. Identification and analysis of the sequence and copy number of flavonoid biosynthetic genes are valuable for understanding the nature and diversity of flavonoid evolution in Musaceae species. In this study, we identified 71 to 80 flavonoid biosynthetic genes in chromosome-scale genome sequence assemblies of Musaceae, including those of Ensete glaucum , Musella lasiocarpa , Musa beccarii , M. acuminata , M. balbisiana , and M. schizocarpa, checking annotations with BLAST and determining the presence of conserved domains. The number of genes increased through segmental duplication and tandem duplication. Orthologues of both structural and regulatory genes in the flavonoid biosynthetic pathway are highly conserved across Musaceae. The flavonoid 3’,5’-hydroxylase gene F3’5’Hs was amplified in Musaceae and ginger compared with grasses (rice, Brachypodium, Avena longiglumis, and sorghum). One group of genes from this gene family amplified near the centromere of chromosome 2 in the x = 11 Musaceae species. Flavonoid biosynthetic genes displayed few consistent responses in the yellow and red bracts of Musella lasiocarpa when subjected to low temperatures. The expression levels of MlDFR2/3 increased while MlLAR was reduced by half. These results establish the range of diversity in both sequence and copy number of flavonoid biosynthetic genes during evolution of Musaceae. The combination of allelic variants of genes, changes in their copy numbers, and variation in transcription factors with the modulation of expression under cold treatments and between bract-colours suggests the variation may be exploited in plant breeding programmes, particularly for improvement of stress-resistance in the banana crop.