Earth Trust

Abstract Acoustic telemetry is a powerful tool for understanding spatial and temporal fish movements in complex freshwater systems but determining appropriate sample sizes for measuring individual variability in movement metrics is challenging. Here, the movements of 170 common bream Abramis brama were measured over two years in the highly connected River Bure wetland system, eastern England. Analyses of seasonal and spatial movement patterns revealed higher movement metrics in winter versus summer, with the extent of movements measured enabled by the high lateral connectivity of this wetland system, with bream often occupying the numerous off-channel lentic habitats present. Power analyses were then applied to this relatively large dataset to assess how changes in tagged fish number influenced the individual variability in the movement metrics. Although retaining 95% of the movement variability required relatively large samples, using 60 individuals retained 90% of individual variability, with only 20 fish needed to retain 80%. These results emphasise the importance of high connectivity in wetlands to enable potadromous fish to access key functional habitats in all seasons. It also reveals relatively small sample sizes in acoustic telemetry can capture much of the movement variability measured across larger numbers of tagged fish.

<title>Abstract</title> Climate mitigation scenarios from Integrated Assessment Models (IAMs) typically assume smooth, predictable transitions, leaving them ill-equipped to assess the impacts of disruptive events. Here we introduce the Disruptive Events-Resilient Pathways (DERP) framework, a methodology that maps mitigation pathways along two dimensions: Mitigation Action Ambition and Resilience to Socioeconomic Impacts. The framework employs narrative-based societal archetypes to systematically explore how different resilience capacities shape mitigation strategies under disruption. Our application to stylised heatwave and technological disruptions reveals that socioeconomic resilience is a precondition for a capital-efficient transition, that a myopic focus on single technologies creates hidden vulnerabilities by delaying structural change, and that a diversified, resilient strategy can hedge against these risks at little to no additional cost. The DERP framework is therefore a valuable diagnostic tool, advancing scenario analysis to a new frontier by considering long-term transition dynamics alongside the resilience required to navigate disruptions.

The terrestrial carbon cycle responds to human activity, ecosystem dynamics, and weather and climate variability including extreme events. Satellite remote sensing has transformed our ability to estimate ecosystem carbon dioxide uptake, the gross primary productivity (GPP), with increasing accuracy and spatial resolution. Many aspects of terrestrial carbon cycling happen quickly on sub-daily or daily scales. These dynamics may not be captured at the temporal scales of typical remote sensing products from polar orbiting satellites – often multiple days or longer. Imagers onboard geostationary satellites measure the Earth system at “hypertemporal” time scales of minutes or less and often have the spectral capabilities to estimate GPP and other surface-atmosphere fluxes using established approaches. Here, we use observations and data products from the Advanced Baseline Imager (ABI) on the Geostationary Environmental Operational Satellite – R Series (GOES-R) to create ALIVE GPP ( A dvanced Baseline Imager L ive I maging of V egetated E cosystems), a GPP product that provides open data on the native five-minute basis of GOES-R CONUS scenes with latency under one day. Our machine learning model, trained on GPP estimates from 111 eddy covariance flux towers with 276 site-years of data spanning tropical to boreal ecosystems, captures up to 70 % of the observed variability when 20 % of tower sites are withheld, with R 2 values of 0.78 (0.82) when aggregating to daily (weekly) periods. We compared ALIVE GPP predictions against eight-day MODIS MOD17A2 GPP estimates and daily GPP estimates from the Breathing Earth System Simulator v2 (BESSv2) and demonstrate how ALIVE GPP simulates the impacts of phenological transitions, flash drought, and hurricanes. Advancements to geostationary satellite imagery, machine learning, and cloud computing make it possible to estimate carbon flux in near real-time and provide new ways to understand the ever-changing carbon cycle and the processes that define it. • We created ‘ALIVE GPP ’, a five-minute GPP mapping framework with <1 day latency. • Gradient boosting models were trained on eddy covariance and GOES-R data across CONUS. • ALIVE GPP R 2 is 0.67 to 0.70 for half-hourly intervals and 0.78 for daily sums. • Rapid regional GPP responses to extreme events matched expectations.

It is widely recognised by plant pathologists and epidemiologists that heaps of plant waste in parks, gardens and plant nurseries can provide a means of pathogen aggregation, which can become a source of disease to the wider site and surrounding natural environment. However, the risks of this activity and the management mitigations required, are not clear to practitioners who may have a number of competing priorities.Information© The Authors 2023

Since its inception in 2011, Learning Analytics has matured and expanded in terms of reach (e.g., primary and K-12 education) and in having access to a greater variety, volume and velocity of data (e.g. collecting and analyzing multimodal data). Its roots in multiple disciplines yield a range and richness of theoretical influences resulting in an inherent theoretical pluralism. Such multi-and interdisciplinary origins and influences raise questions around which learning theories inform learning analytics research, and the implications for the field should a particular theory dominate. In establishing the theoretical influences in learning analytics, this scoping review focused on the Learning Analytics and Knowledge Conference (LAK) Proceedings (2011–2020) and the Journal of Learning Analytics (JLA) (2014–2020) as data sources. While learning analytics research is published across a range of scholarly journals, at the time of this study, a significant part of research into learning analytics had been published under the auspices of the Society of Learning Analytics (SoLAR), in the proceedings of the annual LAK conference and the field’s official journal, and as such, provides particular insight into its theoretical underpinnings. The analysis found evidence of a range of theoretical influences. While some learning theories have waned since 2011, others, such as Self-Regulated Learning (SRL), are in the ascendency. We discuss the implications of the use of learning theory in learning analytics research and conclude that this theoretical pluralism is something to be treasured and protected.

It is with a sense of irony that we offer a conclusion to this book. As we acknowledged in the introductory chapter, when we invited authors to submit proposals for a book on exploring the potential and challenges of learning analytics for open, distance and distributed learning institutions and forms of delivery, no one would have imagined how the world, and in particular the education sector would be disrupted by the Covid-19 pandemic.

Abstract The overall aim of the experiment was to compare the performance of a range of broadleaved species for their biomass production when grown for short rotations on ex-agricultural sites in England. The objectives were to compare over 7 years on four sites the survival, growth and biomass production of eight promising broadleaved species in relation to one another and two conifer species used as controls. Sites were planted in 2010/2011, and by winter 2016/2017 across all species and sites, the mean height, basal diameter (BD) and diameter at 1.3 m (DBH) were 444, 6.4 and 3.7 cm, respectively, despite considerable weed competition on these ex-agricultural sites. There were statistically significant main effects of species and site on all three measures and also significant interactions between sites and species. Totnes, the site with the highest mean annual temperature and the greatest rainfall, had the most uniform growth of the 10 species. At the three more variable sites, there was a general pattern of red alder (Alnus rubra Bong.) with the greatest growth, followed by a group of Italian alder (Alnus cordata (Loisel.) Duby), hybrid aspen (Populus tremula L. x tremuloides Michx.) and common alder (Alnus glutinosa (L.) Gaertner), then silver birch (Betula pendula Roth) with moderate growth. Sycamore (Acer pseudoplatanus L.), sweet chestnut (Castanea sativa Miller) and ash (Fraxinus excelsior L.), which had been infected with ash dieback, had the poorest growth. The estimated total stem volume after seven growing seasons averaged 31 m3 ha−1, with red alder having the greatest volume production, followed by hybrid aspen and Italian alder. Calculated stem biomass (oven dry tonnes per hectare, odt ha−1) after seven growing seasons averaged over all sites and species was 14.1 odt ha−1 (equivalent to 7 tC). The rapid volume growth and intermediate wood density of red alder and Italian alder combined to give the greatest estimated biomass, averaging 34.5 (17 tC) and 24.0 odt ha−1 (12 tC). At three sites, the estimated biomass of red alder was around 40 odt ha−1. Sitka spruce (Picea sitchensis (Bongard) Carrière) and Japanese larch (Larix kaempferi (Lamb.) Carrière) had a standing biomass estimated at 6.3 and 14.0 odt ha−1, respectively.

In this chapter the authors provide an overview of where Blockchain is being used in high resource settings and explore its potential use in emerging health systems for universal health coverage. There is opportunity to address issues in emerging health systems through adaptation and testing of Blockchain, especially in the management of patient records and data, financing, supply chain management, health workforce management, and surveillance processes. It also has complementary relevance for identity and financial inclusion, which are vital for improving the health of the poor in emerging economies. Reference is also made to the use of Blockchain for displaced people and humanitarian settings, which is the subject of Chapter 7 of this book. There remains, however, a need for more research and evaluation as these technologies are implemented and increased user participation in design to ensure that privacy and security issues are addressed. Furthermore, greater attention to local implementation and health sector applications in low resource settings is required.

Abstract The concept of soil health has been extensively reviewed in the scientific literature, but there is only patchy and inconsistent information available to farmers and growers who are concerned about the declining condition of their soils and are looking for appropriate test methods and management interventions to help reverse it. Although there are well‐established laboratory methods for soil chemical analysis, and a range of laboratory and field methods for measuring soil physical properties, only now are methods starting to emerge for soil biological analysis. This study provides an overview of the methods that are currently available commercially (or are close to commercialization) for farmers and growers in the UK . We examine the science underpinning the methods, the value of the information provided and how farmers and advisors can use results from such assessments for informed decision‐making in relation to soil management.

Huge selection pressure is being placed on European populations of the common ash tree (Fraxinus excelsior) by the fungal pathogen Hymenoscyphus pseudoalbidus, which causes ash dieback. It has recently been introduced in the UK and it is known that in areas such as Denmark where the disease is widespread, less than 10% of trees in natural populations have low susceptibility to the fungus. Over the coming decades there will be strong selection for those variants in ash populations that reduce susceptibility. We wish ultimately to identify these genetic or epigenetic variants, enabling the conservation of the species in Europe. Future studies RNA-seq data will be fed into the annotation pipeline Maker [9, 10] to locate coding genes. We will also assemble the transcriptome to analyse expression profiles in the different tissues. We plan to perform bisulphite sequencing to compare DNA methylation patterns between high and low susceptibility individuals. By making the reference genome publicly available on our website (www.ashgenome.org) we are encouraging other groups to use this foundational resource for evolutionary and ecological genetics studies. These can include the discovery of markers for a low-susceptibility genotype, which could be used to aid the breeding of resistant ash tree populations.