Fire in the Swamp Forest: Palaeoecological Insights Into Natural and Human-Induced Burning in Intact Tropical Peatlands

Tropical peat swamp forests are invaluable for their role in storing atmospheric carbon, notably in their unique below-ground reservoirs. Differing from terra firme forests, the peat-forming function of tropical swamps relies on the integrity of discrete hydrological units, in turn intricately linked to the above-ground woody, and herbaceous vegetation. Contemporary changes at a local, e.g., fire, to global level, e.g., climatic change, are impacting the integrity, and functioning of these ecosystems. In order to determine the level of impact and predict their likely future response, it is essential to understand past ecosystem disturbance, and resilience. Here, we explore the impact of burning on tropical peat swamp forests. Fires within degraded tropical peatlands are now commonplace; whilst fires within intact peat swamp forests are thought to be rare events. Yet little is known about their long-term natural fire regime. Using fossil pollen and charcoal data from three peat cores collected from Sarawak, Malaysian Borneo, we looked at the incidence and impact of local and regional fire on coastal peat swamp forests over the last 7,000 years. Palaeoecological results demonstrate that burning has occurred in these wetland ecosystems throughout their history, with peaks corresponding to periods of strengthened ENSO. However, prior to the Colonial era c. 1839 when human presence in the coastal swamp forests was relatively minimal, neither local nor regional burning significantly impacted the forest vegetation. After the mid-nineteenth century, at the onset of intensified land-use change, fire incidence elevated significantly within the peatlands. Although fire does not correlate with past vegetation changes, the long-term data reveal that it likely does correlate with the clearance of forest by humans. Our results suggest that human activity may be strongly influencing and acting synergistically with fire in the recent past, leading to the enhanced degradation of these peatland ecosystems. However, intact tropical peat swamp forests can, and did recover from local fire events. These findings support present-day concerns about the increase in fire incidence and combined impacts of fire, human disturbance and El Niño on peat swamp forests, with serious implications for biodiversity, human health and global climate change

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing Worl

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications

Globally important plant functional traits for coping with climate change

The last decade has seen a proliferation of studies that use plant functional traits to assess how plants respond to climate change. However, it remains unclear whether there is a global set of traits that can predict plants’ ability to cope or even thrive when exposed to varying manifestations of climate change. We conducted a systematic global review which identified 148 studies to assess whether there is a set of common traits across biomes that best predict positive plant responses to multiple climate changes and associated environmental changes. Eight key traits appear to best predict positive plant responses to multiple climate/environmental changes across biomes: lower or higher specific leaf area (SLA), lower or higher plant height, greater water-use efficiency (WUE), greater resprouting ability, lower relative growth rate, greater clonality/bud banks/below-ground storage, higher wood density, and greater rooting depth. Trait attributes associated with positive responses appear relatively consistent within biomes and climate/environmental changes, except for SLA and plant height, where both lower and higher trait attributes are associated with a positive response depending on the biome and climate/environmental change considered. Overall, our findings illustrate important and general trait-climate responses within and between biomes that help us understand which plant phenotypes may cope with or thrive under current and future climate change.publishedVersio

In silico analyses of maleidride biosynthetic gene clusters

Maleidrides are a family of structurally related fungal natural products, many of which possess diverse, potent bioactivities. Previous identification of several maleidride biosynthetic gene clusters, and subsequent experimental work, has determined the ‘core’ set of genes required to construct the characteristic medium-sized alicyclic ring with maleic anhydride moieties. Through genome mining, this work has used these core genes to discover ten entirely novel putative maleidride biosynthetic gene clusters, amongst both publicly available genomes, and encoded within the genome of the previously un-sequenced epiheveadride producer Wicklowia aquatica CBS 125634. We have undertaken phylogenetic analyses and comparative bioinformatics on all known and putative maleidride biosynthetic gene clusters to gain further insights regarding these unique biosynthetic pathways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40694-022-00132-z

Maleidride biosynthesis – construction of dimeric anhydrides – more than just heads or tails

Covering: up to early 2022 Maleidrides are a family of polyketide-based dimeric natural products isolated from fungi. Many maleidrides possess significant bioactivities, making them attractive pharmaceutical or agrochemical lead compounds. Their unusual biosynthetic pathways have fascinated scientists for decades, with recent advances in our bioinformatic and enzymatic understanding providing further insights into their construction. However, many intriguing questions remain, including exactly how the enzymatic dimerisation, which creates the diverse core structure of the maleidrides, is controlled. This review will explore the literature from the initial isolation of maleidride compounds in the 1930s, through the first full structural elucidation in the 1960s, to the most recent in vivo, in vitro, and in silico analyses

Heterologous Production of Fungal Maleidrides Reveals the Cryptic Cyclization Involved in their Biosynthesis

Fungal maleidrides are an important family of bioactive secondary metabolites that consist of 7, 8, or 9-membered carbocycles with one or two fused maleic anhydride moieties. The biosynthesis of byssochlamic acid (a nonadride) and agnestadride A (a heptadride) was investigated through gene disruption and heterologous expression experiments. The results reveal that the precursors for cyclization are formed by an iterative highly reducing fungal polyketide synthase supported by a hydrolase, together with two citrate-processing enzymes. The enigmatic ring formation is catalyzed by two proteins with homology to ketosteroid isomerases, and assisted by two proteins with homology to phosphatidylethanolamine-binding proteins. Ring cycle: The enzymes involved in the cyclization of the maleidride family of bioactive fungal natural products, including agnestadride A and byssochlamic acid, were identified. These previously unknown proteins show homology to ketosteroid isomerases (KI-like) and phosphatidylethanolamine-binding proteins (PEBP-like).BBSRCSyngent