Digitizing natureGaia's Web Karen Bakker MIT Press, 2024. 288 pp.

Digital tools are transforming conservation work but must be carefully deployed, argues an environmental scholar.

Connecting thiamine availability to the microbial community composition in Chinook salmon spawning habitats of the Sacramento River basin.

Thiamine deficiency complex (TDC) is a major emerging threat to global populations of culturally and economically important populations of salmonids. Salmonid eggs and embryos can assimilate exogenous thiamine, and evidence suggests that microbial communities in benthic environments can produce substantial amounts of thiamine. We therefore hypothesize that natural dissolved pools of thiamine exist in the surface water and hyporheic zones of riverine habitats where salmonids with TDC migrate, spawn, and begin their lives. To examine the relationship between dissolved thiamine-related compounds (dTRCs) and their microbial source, we determined the concentrations of these metabolites and the compositions of microbial communities in surface and hyporheic waters of the Sacramento River, California and its tributaries. Here we determine that all dTRCs are present in femto-picomolar concentrations in a range of critically important salmon spawning habitats. We observed that thiamine concentrations in the Sacramento River system are orders of magnitude lower than those of marine waters, indicating substantial differences in thiamine cycling between these two environments. Our data suggest that the hyporheic zone is likely the source of thiamine to the overlying surface water. Temporal variations in dTRC concentrations were observed where the highest concentrations existed when Chinook salmon were actively spawning. Significant correlations were seen between the richness of microbial taxa and dTRC concentrations, particularly in the hyporheic zone, which would influence the conditions where embryonic salmon incubate. Together, these results indicate a connection between microbial communities in freshwater habitats and the availability of thiamine to spawning TDC-impacted California Central Valley Chinook salmon.IMPORTANCEPacific salmon are keystone species with considerable economic importance and immeasurable cultural significance to Pacific Northwest indigenous peoples. Thiamine deficiency complex has recently been diagnosed as an emerging threat to the health and stability of multiple populations of salmonids ranging from California to Alaska. Microbial biosynthesis is the major source of thiamine in marine and aquatic environments. Despite this importance, the concentrations of thiamine and the identities of the microbial communities that cycle it are largely unknown. Here we investigate microbial communities and their relationship to thiamine in Chinook salmon spawning habitats in California's Sacramento River system to gain an understanding of how thiamine availability impacts salmonids suffering from thiamine deficiency complex.

Accounting for the climate benefit of temporary carbon storage in nature.

Nature-based climate solutions can contribute to climate mitigation, but the vulnerability of land carbon to disturbances means that efforts to slow or reverse land carbon loss could result in only temporary storage. The challenge of accounting for temporary storage is a key barrier to the implementation of nature-based climate mitigation strategies. Here we offer a solution to this challenge using tonne-year accounting, which integrates the amount of carbon over the time that it remains in storage. We show that tonne-years of carbon storage are proportional to degree-years of avoided warming, and that a physically based tonne-year accounting metric could effectively quantify and track the climate benefit of temporary carbon storage. If the world can sustain an increasing number of tonne-years alongside rapid fossil fuel CO2 emissions reductions, then the resulting carbon storage (even if only temporary) would have considerable and lasting climate value by lowering the global temperature peak.

Mothers of disabled infants had higher cortisol levels in a free-ranging group of Japanese macaques (Macaca fuscata).

Glucocorticoids (GCs) are hormones released in response to stressors and can provide insight into an organism's physiological well-being. Experiencing chronic challenges to homeostasis is associated with significant deviations from baseline fecal GCs (fGCs) in many species, providing a noninvasive biomarker for assessing stress. In the group of free-ranging Japanese macaques (Macaca fuscata) at the Awajishima Monkey Center in Japan, ~17% have congenital limb malformations. We collected 646 fecal samples from 27 females over three consecutive birth seasons (May-August) and analyzed them using enzyme immunoassay to extract fGCs. We explored the relationship between fGC levels and individual (physical impairment and reproductive status), social (dominance rank and availability of kin for social support), and ecological variables (exposure to potential predators, rainfall, and wild fruit availability). A disabled infant was associated significantly with higher fGC in the mother; however, physical impairment in adult females was not significantly related to fGC levels. Females with higher dominance rank had significantly lower fGC levels than lower ranking females. Other factors did not relate significantly to fGC. These results suggest that providing care that meets the support needs of disabled infants poses a physiological challenge for mothers and suggests that physically impaired adults are able to effectively compensate for their disabilities with behavioral plasticity. Once an individual with congenital limb malformations survives infancy through their mother's care, physical impairment does not appear to influence fGC values, while social variables like dominance rank significantly influenced cortisol values in free-ranging female Japanese macaques.

Mitigation and adaptation emissions embedded in the broader climate transition.

Climate change necessitates a global effort to reduce greenhouse gas emissions while adapting to increased climate risks. This broader climate transition will involve large-scale global interventions including renewable energy deployment, coastal protection and retreat, and enhanced space cooling, all of which will result in CO2 emissions from energy and materials use. Yet, the magnitude of the emissions embedded in these interventions remains unconstrained, opening the potential for underaccounting of emissions and conflicts or synergies between mitigation and adaptation goals. Here, we use a suite of models to estimate the CO2 emissions embedded in the broader climate transition. For a gradual decarbonization pathway limiting warming to 2 °C, selected adaptation-related interventions will emit ∼1.3 GtCO2 through 2100, while emissions from energy used to deploy renewable capacity are much larger at ∼95 GtCO2. Together, these emissions are equivalent to over 2 y of current global emissions and 8.3% of the remaining carbon budget for 2 °C. Total embedded transition emissions are reduced by ∼80% to 21.2 GtCO2 under a rapid pathway limiting warming to 1.5 °C. However, they roughly double to 185 GtCO2 under a delayed pathway consistent with current policies (2.7 °C warming by 2100), mainly because a slower transition relies more on fossil fuel energy. Our results provide a holistic assessment of carbon emissions from the transition itself and suggest that these emissions can be minimized through more ambitious energy decarbonization. We argue that the emissions from mitigation, but likely much less so from adaptation, are of sufficient magnitude to merit greater consideration in climate science and policy.

Moderate support for the use of digital tracking to support climate-mitigation strategies.

The use of digital tracking of individuals throughout the coronavirus 2019 (COVID-19) pandemic renewed societal debates on the efficacy and ethics of digital surveillance to mitigate collective crises. While digital emissions tracking is being used to support climate-mitigation strategies, to date there has been limited exploration of the opportunities and challenges of deploying it at the individual level. Here, we assess temporal and regional differences in levels of support for the use of digital surveillance in times of crisis, such as climate change. Results from a global survey indicate moderate support for the use of digital tracking, including for personal carbon footprints. Response varied regionally, with the lowest support in North America and Europe. This study raises key questions-if digital surveillance tools could be part of a socially acceptable response to the climate crisis, is it worth exploring? Or is this an unacceptable risk for society?

Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery.

Uncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of CO2e per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery.

Current global efforts are insufficient to limit warming to 1.5°C.

Human activities have caused global temperatures to increase by 1.25°C, and the current emissions trajectory suggests that we will exceed 1.5°C in less than 10 years. Though the growth rate of global carbon dioxide emissions has slowed and many countries have strengthened their emissions targets, current midcentury net zero goals are insufficient to limit global warming to 1.5°C above preindustrial temperatures. The primary barriers to the achievement of a 1.5°C-compatible pathway are not geophysical but rather reflect inertia in our political and technological systems. Both political and corporate leadership are needed to overcome this inertia, supported by increased societal recognition of the need for system-level and individual lifestyle changes. The available evidence does not yet indicate that the world has seriously committed to achieving the 1.5°C goal.

Atmospheric methane removal: a research agenda.

Atmospheric methane removal (e.g. in situ methane oxidation to carbon dioxide) may be needed to offset continued methane release and limit the global warming contribution of this potent greenhouse gas. Because mitigating most anthropogenic emissions of methane is uncertain this century, and sudden methane releases from the Arctic or elsewhere cannot be excluded, technologies for methane removal or oxidation may be required. Carbon dioxide removal has an increasingly well-established research agenda and technological foundation. No similar framework exists for methane removal. We believe that a research agenda for negative methane emissions-'removal' or atmospheric methane oxidation-is needed. We outline some considerations for such an agenda here, including a proposed Methane Removal Model Intercomparison Project (MR-MIP). This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery.

We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

Automatic 3D dense phenotyping provides reliable and accurate shape quantification of the human mandible.

Automatic craniomaxillofacial (CMF) three dimensional (3D) dense phenotyping promises quantification of the complete CMF shape compared to the limiting use of sparse landmarks in classical phenotyping. This study assesses the accuracy and reliability of this new approach on the human mandible. Classic and automatic phenotyping techniques were applied on 30 unaltered and 20 operated human mandibles. Seven observers indicated 26 anatomical landmarks on each mandible three times. All mandibles were subjected to three rounds of automatic phenotyping using Meshmonk. The toolbox performed non-rigid surface registration of a template mandibular mesh consisting of 17,415 quasi landmarks on each target mandible and the quasi landmarks corresponding to the 26 anatomical locations of interest were identified. Repeated-measures reliability was assessed using root mean square (RMS) distances of repeated landmark indications to their centroid. Automatic phenotyping showed very low RMS distances confirming excellent repeated-measures reliability. The average Euclidean distance between manual and corresponding automatic landmarks was 1.40 mm for the unaltered and 1.76 mm for the operated sample. Centroid sizes from the automatic and manual shape configurations were highly similar with intraclass correlation coefficients (ICC) of > 0.99. Reproducibility coefficients for centroid size were < 2 mm, accounting for < 1% of the total variability of the centroid size of the mandibles in this sample. ICC's for the multivariate set of 325 interlandmark distances were all > 0.90 indicating again high similarity between shapes quantified by classic or automatic phenotyping. Combined, these findings established high accuracy and repeated-measures reliability of the automatic approach. 3D dense CMF phenotyping of the human mandible using the Meshmonk toolbox introduces a novel improvement in quantifying CMF shape.

Mass SARS-CoV-2 Testing in a Dormitory-Style Correctional Facility in Arkansas.

Objectives. To assess SARS-CoV-2 transmission within a correctional facility and recommend mitigation strategies.Methods. From April 29 to May 15, 2020, we established the point prevalence of COVID-19 among incarcerated persons and staff within a correctional facility in Arkansas. Participants provided respiratory specimens for SARS-CoV-2 testing and completed questionnaires on symptoms and factors associated with transmission.Results. Of 1647 incarcerated persons and 128 staff tested, 30.5% of incarcerated persons (range by housing unit = 0.0%-58.2%) and 2.3% of staff tested positive for SARS-CoV-2. Among those who tested positive and responded to symptom questions (431 incarcerated persons, 3 staff), 81.2% and 33.3% were asymptomatic, respectively. Most incarcerated persons (58.0%) reported wearing cloth face coverings 8 hours or less per day, and 63.3% reported close contact with someone other than their bunkmate.Conclusions. If testing remained limited to symptomatic individuals, fewer cases would have been detected or detection would have been delayed, allowing transmission to continue. Rapid implementation of mass testing and strict enforcement of infection prevention and control measures may be needed to mitigate spread of SARS-CoV-2 in this setting.

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression.

BACKGROUND AND OBJECTIVES: Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females. METHODS: 3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix. RESULTS: Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2). CONCLUSIONS: Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

[Intensive care back up for infectious disease disasters]. / Intensivmedizinisches Back-up bei infektiologischen Katastrophen.

BACKGROUND: Outbreaks of infectious diseases pose particular challenges for hospitals and intensive care units. OBJECTIVES: Typical infectiological scenarios and their significance for modern intensive care medicine are presented. MATERIALS AND METHODS: Selected pathogens/infectious diseases that have significantly strained the resources of intensive care units are described. RESULTS: Intensive medical care is necessary in severe cases of many infectious diseases. In the context of epidemics/pandemics, many critically ill patients have to be admitted within a short time. Examples are the 2009 H1N1 influenza pandemic, the 2011 enterohemorrhagic Escherichia coli (EHEC) outbreak in northern Germany, the 2014/2015 Ebola fever outbreak and the 2020 coronavirus disease 19 (COVID-19) pandemic. Multidisciplinary teams, protocol development, adequate staffing, and training are required to achieve optimal treatment outcomes, including prevention of healthcare worker infections. CONCLUSIONS: Pandemics and epidemics are unique challenges for intensive care unit preparedness planning.

Multilevel principal components analysis of three-dimensional facial growth in adolescents.

BACKGROUND AND OBJECTIVES: The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents. METHODS: These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 "dense" 3D points were defined regularly for each shape by using a deformable template via "meshmonk" software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices. RESULTS: Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average "trajectories" with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed. CONCLUSIONS: mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for "dense" 3D point data.

Exposure to excessive heat and impacts on labour productivity linked to cumulative CO2 emissions.

Cumulative CO2 emissions are a robust predictor of mean temperature increase. However, many societal impacts are driven by exposure to extreme weather conditions. Here, we show that cumulative emissions can be robustly linked to regional changes of a heat exposure indicator, as well as the resulting socioeconomic impacts associated with labour productivity loss in vulnerable economic sectors. We estimate historical and future increases in heat exposure using simulations from eight Earth System Models. Both the global intensity and spatial pattern of heat exposure evolve linearly with cumulative emissions across scenarios (1% CO2, RCP4.5 and RCP8.5). The pattern of heat exposure at a given level of global temperature increase is strongly affected by non-CO2 forcing. Global non-CO2 greenhouse gas emissions amplify heat exposure, while high local emissions of aerosols could moderate exposure. Considering CO2 forcing only, we commit ourselves to an additional annual loss of labour productivity of about 2% of total GDP per unit of trillion tonne of carbon emitted. This loss doubles when adding non-CO2 forcing of the RCP8.5 scenario. This represents an additional economic loss of about 4,400 G$ every year (i.e. 0.59 $/tCO2), varying across countries with generally higher impact in lower-income countries.