The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.
To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.
They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.
When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.
Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved.
The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.
The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.
Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.
The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.
The post What colors were prehistoric mammals? appeared first on Sciworthy.
Authors: María-Luisa Chas-Amil, Catedrática de Economía Aplicada, Universidade de Santiago de Compostela and Julia María Touza, Professor of Environmental Economics, University of York
As the flames advanced towards the village of Bédar, in the Almería province of Andalusia, the mayor gave a desperate order to ring the church bells to warn residents. It was not enough. The July 9 Los Gallardos fire, which claimed 13 lives and burnt 7,000 hectares, was Spain’s third deadliest on record.
Most of the victims were retired foreigners living in isolated homes, who died trapped in their vehicles or fleeing on foot along dead-end paths in a landscape dominated by scrubland and rugged terrain, most of which is difficult to access.
High temperatures and extremely dry weather turn scrub, grasslands and bushes into highly flammable fuel. This, combined with complex terrain that makes fire spread faster and unpredictably, poses an enormous challenge for firefighting efforts.
Andalusian legislation requires local emergency plans for forest fires in high-risk areas, and self-protection plans for detached buildings and housing estates. However, there is a widespread problem with compliance and implementation of these measures.
The disaster in Los Gallardos shows that this problem will only get worse, unless we take decisive and effective action that goes beyond the current dominant paradigm of simply extinguishing and resisting fire.
Where, when and how fires occur is changing. Highly destructive fires are on the rise, housing is expanding into the urban-forest interface. This exposes more homes to fire, and the the risk is increasing most quickly in scrubland and grassland areas, which are not typically associated with fire risk.
This is a major problem for communities that do not know how to respond to a risk they have not often been exposed to. We therefore need to urgently rethink how fire risk is managed.
It is unsustainable to respond only after a disaster has happened. What we need are strategies based on preparedness, mitigation and adaptation. The following eight points are particularly important in achieving this.
1. Manage social vulnerability
Wildfires exacerbate the existing social vulnerabilities that affect people’s ability to anticipate, cope with, withstand and recover from them. In high-risk areas the population may consist of elderly people, people living alone in isolated homes, with financial constraints, or who do not know the local area well.
Fires also pose a risk to local economies. Businesses – including those that provide public services and infrastructure – can be vulnerable for a range of reasons, including flammable facilities, their location, or disruptions to supply chains, essential services and staff mobility.
Targeted planning can identify and address all of these vulnerabilities.
2. Adopt nature-based solutions
Nature-based solutions have not been fully integrated into fire risk management, even though they are widely recognised as essential parts of climate change adaptation and nature restoration.
Nature-based solutions include controlled grazing, the creation of less flammable habitats, green firebreaks, and the restoration of water networks to increase ambient humidity and create areas that slow the spread of fire. These measures also bring many additional benefits for the local population and economy.
3. Create (and update) emergency, self-protection and training plans
Local authorities must draw up emergency plans that the public can access. Isolated dwellings and housing estates must have their own coordinated plans. The plans should include fire hydrants and water supply points, accessible warning systems, access and evacuation routes, and protocols for vulnerable people.
But plans alone are not enough without training. There is also a need for ongoing programmes, tailored to each community, that train people to safely maintain their homes and surroundings, familiarise them with meeting points and shelter areas, and teach them to distinguish between lockdown and evacuation.
Drills are essential. Evacuation is not the same as fleeing, and emergency response must be planned and practised.
4. Promote collective action
Risk reduction is most effective when it is community-based, as this increases overall collective resilience. This means public authorities, community organisations, and the public and private sector must all work alongside one another.
The public should also be actively involved in the design of emergency plans. This builds legitimacy, improves the capacity to respond to fires, and fosters trust and a sense of shared responsibility for preventing fires.
5. Promote integrated and coordinated governance
Fire risk management cannot be the sole responsibility of forestry or firefighting services – it needs an integrated system. All agencies that act in the area must take fire risk into account when making decisions.
In practice, this means avoiding new construction and establishing specific building regulations in high-risk areas, creating buffer zones between different land uses, and promoting uses that help to maintain fire-resilient landscapes.
6. Incentivise risk management
Economic tools and behavioural insights can be leveraged to make self-protection measures and land management the easiest and most rewarding options. This includes nudges: interventions that are designed to encourage but not force people to make certain decisions.
One example of this is payments for environmental services, which financially incentivise land management practices that reduce wildfire risk.
The role of insurance is also evolving in the face of natural and climate-related risks. Instruments are being designed based on a proactive model that incentivises individual and collective risk-reduction behaviour.
7. Improve monitoring and advance warning systems
Up-to-date information on a fire’s development is essential. Real-time monitoring systems, sensors, satellite imagery, predictive models of fire behaviour and alert applications providing detailed information can all improve response capabilities and enable safer evacuations. Integrating these tools into local management plans is a necessary investment.
8. Encourage public and private investment in long-term preparedness
Investments in prevention and preparedness provide significant returns. Growing requirements – both regulatory and voluntary – for the private sector to disclose information on nature-related risks and dependencies are generating new demand for investment products (carbon and biodiversity credits, habitat banks, environmental impact bonds, and so on).
This growing private investment must now be channelled towards measures that strengthen both people’s and the landscape’s wildfire resilience. This is to supplement, not replace public investment.
The Los Gallardos blaze serves as a reminder that we need to fundamentally change the way we understand and manage fire risk. Investment in preparedness and coordinated planning between public authorities, the private sector and the general public could be the key to ensuring that, should the alarm bells ring again, the population is ready to respond.
This article is republished from The Conversation under a Creative Commons license. Original article.
The post Eight lifesaving ways to improve wildfire risk management appeared first on Mapping Ignorance.
Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.
One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.
Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.
To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.
The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes.
They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.
After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).
They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.
They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.
Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.
The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.
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