The Challenges of Ecological Monitoring and the Rise of Automation
Conservation of marine ecosystems has been highlighted as a priority to ensure a sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, and tools to support decision-making. However, there are many roadblocks to achieving adequate and timely information on both the effectiveness and long-term success of conservation efforts, including limited funding, inadequate sampling, and data processing bottlenecks. These factors can result in ineffective, or even detrimental, management decisions in already impacted ecosystems.
An automated approach facilitated by artificial intelligence (AI) provides conservation managers with a toolkit that can help alleviate a number of these issues by reducing the monitoring bottlenecks and long-term costs of monitoring. Automating the collection, transfer, and processing of data provides managers access to greater information, thereby facilitating timely and effective management. Incorporating automation and big data availability into a decision support system with a user-friendly interface also enables effective adaptive management.
Harnessing the Power of AI for Automated Monitoring
Automation is defined here as the use of technology to replace or reduce human intervention. Automation has been adopted in many industries, from automotive to finance, by replacing manual efforts with computer programs or robotics. Machine learning (ML), a subset of AI, has been fundamental to automation. ML algorithms use experience through exposure to data to improve model performance and, as a result, can make accurate predictions from large volumes of data obtained in an automated framework.
After implementation, automated systems should require minimal input to report on the state of an ecosystem and are potentially more cost-effective. Novel monitoring approaches using automation and AI have consequently shown a marked decrease in running costs after implementing automated systems. For example, the automated processing of image-based data from coral reefs using ML technologies resulted in a 99% cost reduction over traditional methods, at 200 times the speed.
Therefore, the implementation of automated monitoring is likely to have high short-term costs but low ongoing costs, amenable to most funding cycles, and potential to overcome the funding barrier for long-term monitoring as well as expand data collection across greater spatial and temporal scales. These cost reductions demonstrate that harnessing the power of AI for automated long-term monitoring can be one of the solutions to conservation management funding constraints.
In addition to cost and time reduction, incorporating automation and AI into management pipelines can expand our ability to manage impacts on ecosystems effectively by providing data at the appropriate resolution to address management needs and inform policy. The additional data collected across greater temporal and spatial scales, and processed via automated monitoring methods, provides more information on the state of an ecosystem, which in turn allows for a better understanding of the environmental processes operating within the system.
This increased access to data, and subsequent flow-on effects, follow the theory of the data-information-knowledge-wisdom pyramid that can lead to more effective management decisions by incorporating more, and useful inputs into the decision-making process for managers to consider.
Overcoming the Challenges of Manual Monitoring in Marine Environments
The reliance on manual efforts for monitoring means that a high proportion of monitoring project budgets are spent on data collection, limiting the breadth and scope of a project. In addition to the high cost and effort which limits sample sizes, the requirement for manual data collection may also bias sampling towards sites that are easily accessible to humans, which is particularly relevant due to the limited accessibility of many marine or coastal environments.
These issues have meant that manual data collection in ecological sciences is rapidly being supplemented or replaced by remote sensing and automated methods to obtain coveted “big data”. Big data broadly refers to massive volumes of data that are not feasibly able to be handled using manual methods. More specifically, to be defined as big data it must possess the qualities of the “five V’s”; variety, volume, velocity, veracity, and value.
Using big data enables a better understanding of systems and processes as users have access to far greater sample sizes to accurately reflect a greater variety of real-world scenarios and increase statistical power in data analysis. The increased development, prevalence, and accessibility of new technologies have enabled researchers to access big data at higher spatial and temporal resolution using remote sensing.
Remote sensing is the science of collecting data via noncontact recording, often providing geospatial or environmental information. When implemented into wireless sensor networks, remote sensing is ideal for integrating into an automated end-to-end system. Remote sensing techniques have become increasingly sophisticated over the last decade, leading to a marked increase in obtaining big data quicker than manual methods and at a lower cost.
However, the scale at which RS networks often collect data may not always be useful for some active conservation projects. Monitoring animal biodiversity and fitness after the implementation of management actions is important when considering the functional recovery of an ecosystem, but is rarely attempted or considered in marine ecosystem conservation. Additionally, the collection of ecological data on the behaviour, abundance, and distribution of animals and plants, in marine environments, presents unique challenges due to limited accessibility and visibility that are often of less concern in terrestrial environments.
A solution to the scale and visibility constraints traditional RS provides is the use of portable devices that collect image-based data. Unmanned aerial vehicles (UAVs), commonly known as drones, can provide spatial information and a much more targeted and relevant scale to managers for active conservation projects. Airbourne or towed LiDAR systems can provide subsurface geospatial information at high resolutions, as well as ecologically relevant data on animal behaviour. The uptake of collecting underwater video footage has also been rapid in the last decade as they have become a cheap and effective way to collect large amounts of data in a non-invasive manner.
New, alternative, and novel technologies to provide ecological data have begun to emerge as a potential solution to obtain information on species with even less contact. For example, eDNA (DNA collected from environmental samples such as water and soil) can be used to observe genetic data showing the presence of species in an area without the need for extensive visual monitoring.
Unfortunately, this technology is still prohibitively expensive for many projects and requires further research to quantify its usefulness, underscoring the need for cost-effective ways to collect useful data. Additionally, manual data collection is hailed as an important tool for community engagement and education through citizen science. Therefore, removing this public data collection may have negative consequences. However, it is possible to integrate AI with citizen science, securing the benefits of both.
Overcoming Data Transfer and Processing Bottlenecks
Automated collection presents a key step in big data acquisition, but the ability to transfer high volumes of raw data to centralized systems for analysis requires innovative technological solutions. Automated data transfer enables researchers to continuously receive data from regions that may be logistically difficult or dangerous for humans to retrieve devices.
Automated data transfer suitable for long-term monitoring relies on the ability to send data to a centralized location for analysis, requiring an efficient means of wireless transfer. The internet of things (IoT) describes a connected network of physical devices (“things”) that can collect and exchange data over the internet, extending the reach where the wireless devices can be deployed.
Coupled with edge computing technology (a means of on-board data processing for remote devices), these technologies provide an elegant solution to process large image-based data files into compressed, processed data for transmission at lower costs. However, there are still challenges in data transfer and storage that limit the scalability of these technologies such as the relatively short range of transfer, high noise, and limited bandwidth capacity of underwater wireless sensor networks which are unique challenges in marine environments.
Automatically processing videos and images to obtain ecologically relevant data to detect trends is still emerging. Further research into the feasibility and scalability of onboard data processing using edge computing to effectively monitor marine ecosystems is still needed. Globally accessible data storage and sharing by use of cloud-based platforms can facilitate collaborative efforts and increase accessibility to existing information.
The Power of Deep Learning for Image Processing
Raw data must be processed or transformed into usable information for analysis. Deep learning techniques have been implemented to count 1.8 billion individual trees over an area of 1.3 million square kilometres from satellite images, allowing researchers to map the variability of crown diameter, coverage, and density with respect to land use and rainfall. Incorporating deep learning technology into this process meant it was completed within a few weeks; a task that would have taken years to achieve with traditional methods.
While spatial data is relatively easy to collect via remote sensing, ecological data on the long-term impact of habitat conservation efforts on marine animals has historically not been well documented. Deep learning may be a solution to the manual processing bottleneck faced by managers who rely on image-based data to inform management decisions.
One use of deep learning algorithms is rapidly processing large volumes of raw image-based data without the need for manual feature extraction unlike other traditional ML algorithms, and with greater accuracy. For instance, deep learning algorithms have been used to survey wildebeest abundance in Tanzania at a rate of approximately 500 images per hour. At this rate, future survey data are estimated to be processed in under 24 hours, whereas manual processing by a wildlife expert would take up to 24 weeks. Additionally, accuracy was not compromised, with the abundance estimate from deep learning within 1% of that from the expert manual analysis.
The classification of multiple coral fish species with high accuracy showed that this method was feasible in unconstrained marine environments, despite facing unique environmental challenges. Deep learning algorithms have also been demonstrated to be faster, more accurate, and more consistent than manual efforts in fish monitoring.
While these studies show promising results, further research into other camera methods, such as automatically detecting and sizing species using stereo-cameras, remains a gap in marine research. However, if successful, may further expand our ability to collect more ecologically relevant big data to assist conservation management by providing in situ biomass and size data of animals in impacted habitats.
Harnessing Big Data for Predictive Habitat Modeling and Decision Support
The implementation of an automated monitoring process, theoretically, provides managers with robust data at an appropriate resolution to effectively detect changes in an ecosystem over larger spatial and temporal scales. This may allow managers to obtain insights that were never available previously with the aforementioned limitations of manual monitoring methods.
One approach to maximise the benefits of automatically collected big data is data-driven modelling (DDM). DDM utilises big data and ML algorithms to find non-linear relationships and patterns between variables. DDM can produce more accurate models in near or real-time using big data than conventional modelling techniques. As such, DMM can give an accurate picture of the system as it is, instead of how researchers expect it to be directly analysing big data in real-time.
Nevertheless, understanding fundamental ecosystem processes before implementation is essential, as the potential omission of data describing a key process can be particularly problematic and result in inaccurate prediction outputs. The evolution of DDMs has stalled as ML techniques remain restricted to predicting rather than describing and investigating the underlying processes of model outputs.
Given the popularity of implementing highly accurate ML techniques due to the increasing accessibility and availability of big data and the demand for more “transparent” machine learning explanations, there is interest in combining the exploratory benefits and predictive capacity of ML algorithms with a mechanistic understanding of process-based models. This hybrid modelling approach may eventually result in a “best practice” approach to environmental modelling, assisting managers in effectively utilising and understanding data from an automated system.
As computational power advances and big data becomes ubiquitous in all disciplines, ML’s ability to adapt continuously and quickly to changing environments may provide near real-time prediction of complex environmental processes with great accuracy. Predictive models can be used to make forecasts about future ecosystem states and can inform decision-makers by comparing alternative management strategies and quantifying uncertainties.
Training ML algorithms by reinforcement learning (RL) using real, raw data may better support adaptive management and agile decision-making relative to supervised or unsupervised training of machine learning algorithms. RL algorithms do not require a pre-defined training dataset and instead, interact directly with their environment to identify optimal decisions to achieve a “goal” by being either “rewarded” or “punished” for certain decisions.
RL algorithms have been used in the context of environmental management and conservation decision-making, however, due to the historic lack of empirical data and computational power, modelling using reinforcement learning has previously been limited to using simulated data and theoretical approaches to management. However, the rise in computing power and increasing availability of big data means that RL can now be combined with artificial neural networks, i.e., “deep RL” to interact with real-world data and exploit the advantages of this training approach to assist in decision-making.
Bridging the Gap Between Automation and Effective Decision-Making
To facilitate effective management, information gathered from automated monitoring must be efficiently transferred to decision-makers. A technological solution for information transfer, understanding, and interaction may come from a tailored and management-oriented user interface at the end of the automation pipeline (UI).
A UI is a space where humans interact with machine processes and can be designed to be “user friendly” to assist interaction at an appropriate level of user understanding. Custom-built UI platforms, such as web applications, designed for environmental monitoring could provide managers with real-time information on the state and condition of their management area without the need for coding expertise.
The UI could display key information for managers as data summaries, such as trends in animal abundances over time or if specific variables are nearing manager-defined thresholds, as well as providing its own suggestions from automated, data-driven decision making. Additionally, the use of AI algorithms can note areas with data deficiencies, like the citizen science, bird monitoring app eBird, recognises data-poor regions and gamifies data collection in these areas for users to improve the spatial balance of data collection.
However, the development of tailored software can incur high initial costs in proportion to project budgets and may require collaboration across different areas of expertise, including managers, software developers, modellers, and researchers. Integrating automated monitoring and UIs may be a useful tool for long-term monitoring and management programs, which otherwise can be troubled by issues with staff and leadership turnover, ongoing training costs, and loss of skill sets.
Overcoming Challenges and Realizing the Full Potential of AI in Conservation
While new technologies have been identified as valuable tools in environmental monitoring to assist conservation and management efforts for many ecosystems, there are still roadblocks to overcome for efficient and cooperative environmental management at the implementation level. Democratisation of data requires efforts to make useful data accessible, including the effective management and appropriate storage for these data; a challenge that requires global cooperation.
Additionally, the implementation of mechanisms to evaluate the accuracy and precision of automatically collected and processed data are needed to provide tools for quality assurance and control of data provided by the automated system from the collection to processing phases. The cooperation and implementation of a standardised, systematic reporting framework for marine monitoring may assist in the transfer of knowledge between managers. Integrating AI technology with standardised reporting could allow managers to make informed decisions and share useful information to drive and improve management success.
Social factors may also act as a roadblock to the proliferation of automated monitoring. The reluctance or inability of groups to share data could slow global, or even local, cooperative research and impede quick and effective conservation efforts. Additionally, while the benefits AI has provided to businesses in many industries are increasingly evident, the hesitancy of in-house employees to implement AI initiatives will inevitably affect the efficacy of automated monitoring projects.
As funding for long-term monitoring projects remains scarce, the cost of purchasing, implementing, and running AI technology continues to decrease, making automation an attractive alternative for conservation management in marine ecosystems. We have shown that automated monitoring to obtain big data may assist in broadening our understanding of these relatively inaccessible marine ecosystems. Coupled with sophisticated machine learning algorithms to analyse data, automated monitoring can provide managers with a comprehensive, cost-effective, and constant supply of accurate information for long-term monitoring and optimal, adaptive management decisions, particularly where systems are changing due to anthropogenic influences.
Despite the current technological and social challenges facing the implementation of AI and automated systems in management, the unprecedented amount of data becoming available, coupled with advances in ML over the last few years can provide managers and researchers with the tools to create accurate and agile predictions. This will ensure appropriate and successful management outcomes with the aid of additional analysis and decision support applied to an adaptive management framework.
The more accessible and pervasive use of technology can encourage uptake and improve management outcomes and overall understanding of marine environments and ecological processes for conservation. As AI continues to evolve, its role in wildlife conservation is set to become even more impactful, offering hope for a future where endangered species are better protected, ecosystems are more closely monitored, and conservation efforts are more proactive than reactive.
