Monitoring ecosystems to ensure the future
Other sectors Food

How does biodiversity, the quality of air, soil and water change over time in different areas? Advances in digital technologies and gene-sequencing are enabling scientists to collect and analyze this data on an unprecedented scale in order to understand changes in our natural environment. Pulled together, this potentially vast collection of data could become the Internet of Nature, enabling real-time insights on how human activities affect nature. This continuous collection of data, next-generation environmental monitoring, is becoming an important tool to understanding the long-term impact to changes in our natural environment.

Environmental monitoring administers a program of recurring, systematic studies that reveal the state of the environment. It typically measures the physical quality of the air and water and the biodiversity of life to characterize the structure and function of the ecosystem. To perform environmental monitoring, we depend on technologies and methods for measuring ecosystems and their components in order to better understand their complexity and how they are affected by human activities.

Changes to the natural environment may also affect people dependent on the local environment for food and coexistence. Environmental monitoring could be used to help in identify threats such as toxic algal blooms which can affect fisheries and seafood safety. Only by acquiring and analyzing ecosystem data, can appropriate actions be taken to mitigate unwanted impact.  Cost-efficient processes to communicate monitoring results to the relevant decision makers to expedite the appropriate actions are other key components of environmental monitoring. Digital technologies are evolving and providing increasingly precise monitoring techniques to help us achieve this.

Current environmental monitoring technologies require manual labor combined with advanced instruments and lab processing tools.  Towards 2030, less intervention and manual labor will be required as new technologies and sensors will enable more granular data collection across a wider coverage area.

For example, automated unmanned devices will conduct frequent sampling and analyses based on molecular technologies. Chemical sensors will also allow for regular and automated monitoring at remote locations, whereas biosensors will be used to measure biological parameters. Next-generation monitoring technologies, including autonomous systems and vehicles fitted with sensors and cameras; earth observation satellites will provide new sources of data from previously inaccessible areas. This will enable real-time observations to be made and provide warnings when deviations from the baseline measurements are spotted.

‘The Environmental Sample Processor’ is an example of a such an unmanned robotic platform2.  Developed by Monterey Bay Aquarium Research Institute, the device enables biological measurements at sea in order to monitor changes in the key components of this underwater ecosystem. Developments are underway for the robot to detect changes to bacteria levels which can imply environmental pollution by oil3. This technology can also be used to monitor operational parameters as well as occurrence of pathogens in aquaculture.

As environmental sensor technologies evolve, significant improvements in the navigation systems of autonomous underwater vehicles will enable swarms of robots that move over the ocean like schools of fish, providing more detailed data on the ocean’s chemistry, currents and benthic communities (organisms living on or near sea bottom)4.

Monitoring genetic diversity

High-throughput sequencing of genetic material (both DNA or RNA) to identify or monitor species, including their structure and function in the ecosystem, will also play a key part in future environmental monitoring. Together, these technologies will help us build a reliable knowledge base for human environmental impact assessments and ecosystem restoration efforts towards 2030 and beyond. These activities are essential to enable sustainable environmental governance and the ecosystem restoration required to meet the UN’s SDGs.

The diversity of species is rapidly declining because of human activity. Between 80 and 90 % of eukaryotic species in our ecosystems are unknown and Earth may be in danger of losing these potentially essential sources of medicines and biomaterial5.  Monitoring the genetic diversity in ecosystems will provide vital knowledge for the conservation, protection and regeneration of biodiversity.

In 2030, our knowledge of the genomes of species in our ecosystems will have improved significantly as a result of the “moonshot“ biology project called the Earth Bio-genome project This project intends to sequence, catalog and characterize the genomes of all Earths’ eukaryotic diversity within 20305. The results of this project will inform a broad range of major issues, including the impact of climate change on biodiversity, the conservation of endangered species and ecosystems, and the preservation and enhancement of ecosystem services.

Using gene sequencing technologies

The rapid development of high-throughput gene sequencing technologies, together with plummeting costs, pave the way for the implementation of genome-based approaches in biomonitoring programs. Gene-sequencing detects the sequence of the four bases (adenine, thymine, guanine, cytosine) in DNA, which encodes genetic information.

The current cost of sequencing is cheaper than ever before, with an average vertebrate-sized genome costing $1,00 USD (2019) to draft, 1% of the cost of sequencing the first human genome5.  However, novel gene-sequencing technologies could allow for the rapid and cost-effective identification of the entire taxonomic composition of thousands of samples simultaneously6. Metabarcoding is a common approach, where the DNA from many different species from the same sample are identified by sequencing short DNA-fragments. These barcodes are then mapped to a reference library, becoming part of an Internet of Nature.

Environmental DNA

An interesting development is using this metabarcoding approach on environmental DNA (eDNA)7. eDNA refers to DNA extracted from the soil, water and air without isolating the organisms themselves. It relies on the ability to capture genetic signatures left behind by organisms through natural processes of shedding, excreting, decaying and offers a non-invasive means to identify species or communities associated with the environment.

Studies indicate that eDNA can be a highly accurate biomonitoring tool, where the stability of eDNA can vary between types of environmental sample. Water eDNA is a good candidate for biomonitoring since that signal indicates a recent species event (as eDNA degrades faster in water than other media). eDNA approaches can identify species that are difficult to identify using morphology alone and eDNA has been used to monitor ocean biology with high precision8.

Towards 2030

Digital technologies are evolving, and increasingly precise monitoring techniques enable us to monitor the state of nature with a much higher precision than ever before. Developments, however, are hindered by high costs compared to traditional approaches.  First-movers implementing the technologies are absorbing much of the investments at a high risk to their potential business model (similar to that experienced in the early development of electric cars by Think and market success achieved later by Tesla).

Another hindrance for the large-scale uptake of these technologies is the current lack of industry obligation to report their environmental impact with higher precision.  In addition, the potential for vast amounts of data collected presents further challenges related to storage and analyses, a familiar dilemma when it comes to big data. The large volume of data produced will increase the need for more digital competencies within industries, environmental consultants and governmental agencies to ensure quality in all parts of this new value chain of environmental monitoring data. At the same time, experts with domain knowledge of the areas being monitored will be key to verify that the correct parameters are being included in monitoring programmes and that the right conclusions and actions are drawn from the advanced analytical results.


Main author: Marte Rusten

Contributors: Tor Jensen; Thomas Møskeland

Editor: Ellen Skarsgård

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