Harnessing AI and Big Data to Transform Conservation Efforts
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.
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.
We summarise the current state of artificial intelligence and automation techniques used in marine science and use examples in other disciplines to identify existing and potentially transferable methods that can enable automated monitoring and improve predictive modelling capabilities to support decision making. We also discuss emerging technologies that are likely to be useful as research in computer science and associated technologies continues to develop and become more accessible.
Our perspective highlights the potential of AI and big data analytics for supporting decision-making, but also points to important knowledge gaps in multiple areas of the automation processes. These current challenges should be prioritised in conservation research to move toward implementing AI and automation in conservation management for a more informed understanding of impacted ecosystems to result in successful outcomes for conservation managers.
The Need for Robust Monitoring and Adaptive Management
Understanding complex ecosystem processes that are imperative for decision-making and effective conservation management requires ecological insight over varying temporal and spatial scales. These insights include quantifying ecological responses to environmental change and providing ecological data to develop informed ecological syntheses and prognostic ecological models. Such syntheses and models act as platforms for collaborative studies, promoting multidisciplinary research and providing information to support evidence-based policy, decision making, and management of ecosystems, by implementing both passive and active conservation to achieve optimal conservation outcomes.
Both passive and active conservation approaches are important and complementary strategies to ensure the recovery of impacted ecosystems. Passive conservation approaches aim to lessen or remove the impact of environmental stressors to promote the natural recovery of habitats, and often address issues that may inform policy in areas such as poor water quality or pollution. Ongoing monitoring to determine the success of passive conservation approaches does not often require understanding complex ecological processes and is often the cheaper alternative.
Active conservation, such as restoration efforts, is often attempted at relatively smaller scales than passive restoration, however, this is where current efforts in management are often comparatively less economical and successful. Currently, the costs for active conservation efforts are high, and due to a number of uncertainties within the restoration process, the real costs are more likely to be 2-4 times higher than the reported global median cost of $80,000 USD per hectare.
Conservation management through ecosystem restoration of degraded habitats is of particular interest, with the United Nations hailing 2020-2030 as the Decade on Ecosystem Restoration. While the implementation of ML algorithms in statistical analysis in marine ecology is widespread, automated solutions for monitoring and management are rarely attempted, particularly for more localised or smaller-scale conservation projects.
Many marine active conservation efforts are expensive and achieve average results or fail. This may be due to insufficient resources to effectively monitor the ecosystem response long-term or to manage and adapt effectively after implementation. Managers often face challenges in obtaining data for active conservation over appropriate spatio-temporal scales and high resolutions due to several difficulties such as the long-term financial support required, and creating and maintaining an appropriate monitoring design to accurately detect changes in the environment.
Despite the need for long-term monitoring projects to determine the success of active conservation efforts, they remain uncommon, as ongoing funding, support, or partnerships are challenging to sustain. Funding agencies and investors are more likely to invest in new and innovative projects, which pressures researchers to pitch their projects as novel, rather than necessary monitoring.
Automated Monitoring and AI-Powered Decision Support
Automated monitoring facilitated by artificial intelligence (AI) can provide a cost-effective solution to provide tools for monitoring impacted and restored ecosystems over more relevant spatial and temporal scales. 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.
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 Barriers to Automation 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.
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. A solution to the scale and visibility constraints traditional RS provides is the use of portable devices that collect image-based data, such as unmanned aerial vehicles (UAVs), commonly known as drones.
Although areas can be surveyed using satellite or aerial drone imagery quickly and efficiently, this method for marine environments requires good water clarity and relies on the monitoring target utilising shallow waters, which may not provide information on sub-surface behaviours or distribution. 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, such as eDNA (DNA collected from environmental samples such as water and soil).
While the potential impact and importance of big data collection have been widely acknowledged in environmental monitoring, and the utilisation of technology has become more common, there are barriers to implementing automated data-collection networks in marine ecosystems. Some of these barriers are not ecosystem specific, such as lack of technical expertise, funding, transferability, accessibility, and even awareness of the existence of potentially useful technologies.
However, the added difficulties of accessibility and associated higher costs of effectively monitoring across appropriate spatial and temporal scales are exacerbated when managing marine environments. Additionally, new and novel technologies are becoming available to collect data remotely, however, these technologies often come with a higher cost associated, at times limiting the number of units that can be purchased, and in turn, limiting the sample size collected in monitoring projects which in turn creates uncertainty in the data collected.
Automation Across the Data Lifecycle
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 centralised location for analysis, requiring an efficient means of wireless transfer.
Currently, wireless technologies often utilise satellite or mobile phone networks to transfer environmental data, but they can be expensive and are limited in their deployment locations as they rely on proximity to these signals. 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. 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.
Raw data must be processed or transformed into usable information for analysis. This is particularly important for raw data that cannot be used without transformation into a format that computers can read, such as acoustic recordings, video and camera footage, and sonar. 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.
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. This capability, however, introduces challenges in understanding the level of error and confidence in the predictive model outputs, and the quality to which the data is processed may be different from the small-scale tests if biases are accidentally introduced.
Predictive Modeling and Decision Support for Adaptive Management
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.
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. 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.
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 can not only be used to optimise the autonomous nature of remote sensing networks at the data collection and transfer stages, but algorithms could also interact with continuous streams of real-world data and predict future ecosystem states.
This learning technique may give the much-needed empirical support to mechanistic frameworks which often face challenges in obtaining the required ecosystem state knowledge under the effect of potential management actions. Furthermore, RL can support iterative and near real-time forecasting of management outcomes, thereby supporting agile decision-making for effective and quick management applications.
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). 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. 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 Limitations and Driving Adoption
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.
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.
Despite these challenges, 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.
However, there are still many areas of research that need further investigation on the feasibility and scalability of these technologies before they are implemented into fully end-to-end automated monitoring systems. Despite this, “semi-automated” approaches where individual technologies can be adopted at different stages is currently feasible and may assist in immediate increases in information.
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. While AI holds great potential, it is crucial to exercise caution and not overly rely on AI without considering potential limitations and verifying results through rigorous scientific validation. The collaborative effort between AI and human experts is crucial for successful implementation in conservation management.
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