It’s wonderful living in the world, but it’s very complicated. For us to see nature in all its beauty, there are maybe hundreds of other angles. This curiosity with nature and its systems, which has been important to us since we’ve known each other, had begun very early in our lives. We have been able to comprehend ourselves and our pasts through the study of habitats, biology, bone structure, systems like digestion.
Nature is full of intricate structures, but we’ve figured out how to study them in a systematic way. Ecology is all about how organisms interact with one another and their environment.
The study of ecology involves the integration of a wide range of levels, which form an ordered sequence. These levels range from the micro-level to the planetary level; they begin with cell structures and culminate in the biosphere. The aim of ecology is to investigate the interactions between biological and abiotic elements. Biotic elements are those found in living organisms, including animals, plants, and bacteria. This term encompasses a broad range of elements in ecosystems, while abiotic elements include non-living elements, such as rock, water, and air. These elements interact with one another to form ecosystems.
An ecosystem is made up of living things and the environment around them. It’s a system where biological and non-biological elements interact with each other. Ecosystems can range from small to large. There are also lots of different types of ecosystems based on the environment they’re in, like marine, aquatic and land-based.
Research has long been concerned with the manner in which we interact with the natural world’s ecosystems. We, as humans, still interact extensively with the world’s ecosystems in our daily lives. Even in urban settings, life flourishes with the planting of urban trees, in the gaps between the pavements, in the humid basement, and even in the refrigerator. Living organisms are ubiquitous and we have been interacting with the Earth’s ecosystems for an extended period of time.
This is why it is important to study and understand ecology, to teach it, to protect it from misinformation. A large part of being human is understanding and adapting to the environment. Therefore, we need to understand our environment through science and science. There are many different ways of conducting ecology research. Some research can be conducted on site, collecting information on a daily basis, such as in a wooded area. However, with the advent of technology, ecological research has become much faster and more efficient. Technology and science have always worked hand in hand. They both help us in our daily lives and they both help us understand ecosystems. Then Earth. Then the multiplanetary system. Then the universe. They are both very valuable and excellent tools for humanity.
There are many different ways in which technology is used in science. Cameras, Sensors, Processors, Computers, Microscopes, Centrifuge Machines, Telescopes and Microscopes are just a few examples of how technology has been used in science. In particular, computers have been extremely helpful to researchers. The way in which computers were used in research was very innovative. As computing technology improved, computers became even more helpful to researchers. Nowadays, it is almost impossible to do research that contains large datasets without computers, because the datasets are larger because of the increased amount of information that can be collected in the context of research.
For decades, ecology researchers have been tracking seal populations by creating libraries of aerial photographs. This requires hours of manual labor to identify the seals in each image. In order to solve this problem, a team of researchers are using a deep learning model to count the seals in the photos of ecology researchers. The model runs 100 images in less than one minute.
Source: “AI helps speed up ecological surveys”,Wageningen University & Research, December 1st 2021
Deep-learning is a subset of machine learning and is focused on learning from large volumes of data. Many applications of AI are based on deep learning. AI helps automate processes and performs analytical tasks, likely better and faster than humans.
Deep-learning neural networks attempt to imitate the human brain. They are used to identify, classify, and describe objects within a picture.
To test their model, the researchers tested it on a much smaller dataset: images of tiny growth rings in fish bones called otoliths. These growth rings are notoriously difficult to annotate on their own. The researchers found their model had about the same error rate as manual methods but was able to annotate 100 images in less than a minute, whereas an expert would take three hours.
This tells us how much AI helps researchers because the data ecologists work with is very laborious because the animal images may be very large or there may be a large dataset of images. Because computers are faster and more accurate than humans, it is widely used in ecology research with large datasets of images.
Humans have been using artificial intelligence (AI) since the late 1950’s, but AI’s capabilities are rapidly advancing due to a number of factors: sensors, satellites, and the Internet collecting huge amounts of data; the development of faster and more powerful computers; and seeing artificial intelligence evolve before our eyes. With AI, one can expect to see a rise in what can be achieved. There are new concepts that humans can now compute, now with the help of AI.
In a recent study on AI and ecology, a team of researchers showed that an artificial intelligence technique could be used to identify, count, and describe animal images captured by a motion-sensing camera. The researchers trained the system to identify animals in their natural habitat, and the results showed that the system was very accurate at identifying animals, identifying 99.3% of images, which is as accurate as humans.
In order to protect endangered species, it is necessary to know how many individuals are present in the study area. Research using AI is mainly focused on protecting wildlife by knowing where the animals are, how many are present, if they have been harmed by poachers, and so on.
AI technology collects wildlife data accurately, inconspicuously, and cheaply. This can be used in many fields, such as ecology and wildlife biology, as well as zoology.
AI can be used to help researchers study the wild animals collectively and develop strategies for their conservation.
AI can track wildlife patterns and predict their extinction.
AI can help conservationists detect and stop the poaching of wildlife.
AI can provide information on climate change and how to reduce its impact by creating a proper plan
AI can help in assessing the population and seeing changes
AI can help to stop the illegal trade of animals on social media
Animals are disappearing at an alarming rate. This is happening not only in terms of genetic diversity but also in terms of ecological diversity and behavioral diversity.
The current state of animal diversity is not well understood. There are over 120,000 animal species monitored by IUCN (International Union for Conservation of Nature) Red List, and 17,000 of them have a ‘deficiency’ status.
In animal ecology, there are many examples of accelerating research through the use of machine learning based systems.
For example, Microsoft’s “species classification API demo” is a machine learning based system that correctly identifies over 5000 animal species and plant species.
Source: AIWS, “This Is Why AI In Wildlife Conservation Is So Glorious!”
There is a study that shows how AI is being used to fight against poachers. The study used machine-learning algorithms to analyze the movement of herbivores on the savannah that were fitted with a bio-logging device to detect human threats. This method can locate human intruders up to 500 m away. This suggests that’sentinel animals’ may be useful in combating wildlife poaching.
TRex is a new image based tracking software that can track hundreds of individually recognized animals in real time. In this example, the software is used to visualize the trail formation of a termite colony.
Sensors and cameras are at the heart of AI and technology as they are providers of data sets. New sensors expand the range of data types available to animal ecology. Sensor data offers a variety of ways to monitor wildlife, monitor and track populations, and gather behavioral data. Sensor data can be mobile or stationary, and can be organized to collect on particular species of interest, track activity in a desired location, or document habitat changes (satellites) over time.
AI is changing the landscape of wildlife conservation at a time when advances in this field are needed more than ever. By improving the speed and efficiency of data collection and reducing costs, technology-driven solutions may be just what is needed to overcome the drivers of extinction in the 21st century.
In order to provide accurate estimates of populations, understanding animal behavior, and fighting against poaching and biodiversity loss, animal ecology and wildlife need to be able to process large amounts of data. This will be done with the help of artificial intelligence and deep-learning.
In 2016, 772 weather events and disasters occurred, which is three times more than in 1980. Currently, 20% of species are threatened with extinction, and this number is projected to rise to 50% by the end of the 21st century. Even if all countries adhere to their Paris climate commitments, average global temperatures are projected to rise by 3°C compared to pre-industrial levels.
Just as AI can help us with biotic factors in ecology, it can also help us to research, inspect and identify abiotic factors.
As a tool, AI is well-positioned to help address climate change. Its ability to collect, compile, and interpret vast datasets on emissions and climate impacts, as well as other data-driven topics, can help all stakeholders to take a more informed approach to combat carbon emissions and build a more sustainable society.
In a recent study, researchers have found that AI can accurately identify tropical cyclones and weather fronts, as well as atmospheric rivers, with an accuracy of 89 to 99 percent. This means that AI can identify ecological events and help people prepare for disasters ahead of time.
This will be especially important in the near future as climate change continues to worsen and more and more disasters occur around the world, causing death and destruction.
People, animals and plants are all affected when disasters affect ecosystems.
AI can also be used to create early warning systems that provide timely alerts about future events. For instance, by analyzing weather station, satellite image and sensor network data, AI can be used to identify conditions that favor extreme weather events like hurricanes, floods, and wildfires.
These early warning systems can help us mitigate the effects of such events before they happen.
AI can be used to measure emissions, decrease emissions and greenhouse gas emissions. In fact, 43% of organizations have a vision for how AI can be used in their climate change efforts, showing strong interest in this technology.
To predict the long-term effects of climate change, it’s important to be able to predict localized long-run trends. For instance, what is the likelihood of a severe drought in a specific region over the next decade? What are the impacts of a severe drought on agricultural production, water supply and human health? By analyzing historical data and forecasting future trends, AI can help answer these questions.
AI can also be used to create intelligent flood defenses that leverage real-time data on rainfall, river level and land elevation to help protect against flooding. Additionally, intelligent buildings using sensor data to modify heating, cooling and ventilating can help reduce emissions and save energy.
Source: Vox, “How melting glaciers fueled Pakistan’s fatal floods”
These are some of the start-ups/companies that focus on AI and Ecology:
Blue Sky Analytics
A climate-tech company that uses satellite data to turn it into environmental intelligence
Cloud to Street
Cloud to Street uses satellites and artificial intelligence to track floods in real-time anywhere in the world. They run a global flood database that provides insights into flood exposure around the world. They are committed to reducing the risk of floods and saving lives.
A developer of technologies for monitoring and analyzing plant development, health and stress
Prospera’s technology collects crop field data such as climate and visual data to keep crops healthy
An Australian bushfire detection company that uses AI to provide surveillance of wildfires
This helps firefighting teams effectively manage and fight fires.
As the effects of climate change continue to worsen, it became essential to continue to invest in and support companies that are utilizing AI to create solutions to climate change. Data is growing at an exponential rate and that rate is not expected to slow down. Many companies are currently sitting on datasets that are so large that it is impossible for humans to handle them on their own.
It is clear that AI plays an important role for ecologists and researchers who are working to protect the earth and its ecosystems from climate change. Obviously, AI will not solve the problem of climate change, but it will play an important role on the path of ecologists and Climate-Tech companies.
A different research was conducted on labeling structures within a cell using live imaging and in 3D. For this task, a different approach was needed.
This is a goal that typically relies on fluorescent microscopy, which is problematic because with only a few colors to work with, the scientists will run out of fluorescent labels before they run out of structures. Additionally, the light used in this technique is harmful to living cells, causing them to die before the camera. This technique is also very expensive.
Using white light to image cells does not require labelling, thus resolving some of the issues that have previously been encountered with fluorescent microscopy. However, cells are simply harder to observe with this technique.
In this study, researchers sought a way to combine the benefits of both techniques. Artificial Intelligence (AI) can be used for identification of animals, identification of cyclones, and identification of biotic- abiotic factors in ecology. The researchers thought that AI could be used to image the cell and predict what fluorescent labels would look like. This is known as in silico labeling.
This AI model can be trained using 10-20 images, but larger datasets will of course lead to a better model. In some cases, thousands of images may be required.
It is important to note that to apply different cell types, you will need to supply different data for each cell type. In other words, you will need a variety of data in the dataset.
Unfortunately, the resulting images are not as good as the model can be when it comes to the realistic texturing of the nucleus and the cytoplasm.
Biologists and ecologists usually need to work with people with technical knowledge in AI and deep learning, while computer engineers who are interested in AI and ecology may need to work with ecologists.
An example of this is when an ecologist has a large dataset of data collected in a forest. This data may include information on bears in the area. It is common for scientists to have extensive imaging data, but they need the assistance of computer scientists in order to use Artificial Intelligence (AI) to identify specific animals that need to be inspected. In some cases, computer scientists are passionate about protecting the environment and humanity from the consequences of climate change and establish start-ups, in which case they need the help of ecologists to comprehend and analyze the data that has been processed by AI. An example of this is a study conducted by engineers on the invasions of the Great Lakes by Zebra and Quagga Mussels. Although attempts to control the mussels have been unsuccessful, environmental managers still need to know their location, number, and activity in order to accurately predict the effects of the invasion on the ecosystem and safeguard vital infrastructure. However, this is a costly and lengthy process.
Now, the team is working on a more cost-effective and time-efficient system using AI to analyse images and measure mussel abundance and biomass.
The team trained an artificial intelligence (AI) system on a dataset of photos taken by divers while studying a species of algae. The AI system was able to identify and automatically evaluate the number and biomass of each zebra (Zebra mussel) and quagga (Quagga) mussel in the dataset.
The training photos used to train the AI system showed only two-dimensional images of mussels. However, in nature, mussels can grow in all directions and stack on top of one another. Therefore, the team is now working on a three-dimensional imaging system using AI.
They also envisage an underwater drone which can provide real-time mussel location data and can also be modified to detect algae and fish.
Mussels can be a major threat to any water infrastructure, particularly agricultural and water intake systems where they can limit the movement of water and reduce the capacity of the system. Monitoring and handling the mussels is labour-intensive, so a high-speed and reliable artificial intelligence system that can identify the location of the mussels and their distribution over an area can help managers decide on the best areas to control the mussels or locate new infrastructure.
Researchers are also working on saving Europe’s forests. The European forests are being ravaged by a beetle that is no bigger than the grain of rice. The beetle is destroying trees faster than the trees can be killed to slow the spread of the insects. The best way to detect the pests and stop them is from space.
The European Spruce bark beetle (ESB) is a tiny insect, but it has a huge impact on forests. Last year alone, the beetles destroyed 5 million cubic meters (15 million cubic feet) of lumber, which is about 25% of the season’s potential yield. This means that even though these insects are part of our ecosystem, they can cause damage to their own ecosystem as well.
It can take up to a day to assess a 100 acre estate by foot, making it a very difficult task. To make this process easier, researchers decided to think outside the box and use satellite imagery to image the forests. An AI mdel was then trained to quickly and accurately identify infested areas, matching the information of those who do this manually and the images provided by satellites. The researchers found that this increased the effectiveness of the spotting of sick trees by 20%. This will save hundreds of thousand of cubic meters (15 millions) of lumber each year. This demonstrates the power of AI as it integrates with satellite imagery, which is likely to be useful even in urban environments.
Of course, forest-focused ecologists are also using AI for fires. In recent years, California has experienced the most devastating wildfires in modern history. As a result, state and federal authorities are using similar technologies to prevent, suppress and rebuild from wildfires. Many ecologists are working on similar projects as they are well-versed in forests and the ecosystems they are part of.
As climate change continues to wreak havoc on forests around the world, droughts, heat waves and pest infestations are becoming more frequent. In Germany, forests are experiencing the biggest decline since the 1980’s, with 43 percent of the total forest area already damaged. While this is worrying news, researchers are working on a system that will enable foresters to use AI for saving forests and managing them. The system is a cloud-based deep learning model that uses patterns and correlations between data.
There is no doubt that there is a growing demand for emerging technology among conservationists and engineers, as well as ecologists and agricultural researchers, as well as forestry scientists and forest scientists. In particular, AI is becoming increasingly popular in all fields of research, as it is indeed a powerful technology.
AI and ecology are expected to work hand in hand in the near future. As ecologists are almost always dealing with large data sets, they are well versed in the use of deep learning.