**4. Biomimicry and climate change**

The growing severity of the impacts of climate change demands a rapid and vast array of policy actions that both mitigate and adapt humanity and indeed all other flora and fauna to the impacts of these changes. The focus of mitigation is reducing or eliminating the increase of anthropogenic GHG emissions into the atmosphere. Adaption seeks to assist the world live with the inevitable climate change adverse events arising from global warming due to historic and present high GHG emissions. The question for many who seek to manage the climate change challenge is how to navigate the highly contested mitigation and adaptation policy and practice space. Contestations in this arena arise because managing climate change carries a mix political, economic and environment considerations around which humans rarely share similar views concerning the best options in maximising utility.

### *Biomimicry, Big Data and Artificial Intelligence for a Dynamic Climate Change Management… DOI: http://dx.doi.org/10.5772/intechopen.84406*

As debates continue, contestation sharpen and diminish, there is a growing focus on how humans can emulate nature's ability to heal itself as well as to adapt to environments both harsh and otherwise. This mimicking is the essence of the science of biomimicry. The term biomimicry emerges from a combination of the word *bios*, meaning life, and *mimesis*, meaning to imitate. Biomimicry is a discipline that studies nature's strategies and how humans can emulate these strategies to solve contemporary challenges in a sustainable manner. Benyus captures the essence of biomimicry stating:

The core idea is that nature, imaginative by necessity, has already solved many of the problems we are grappling with. Animals, plants, and microbes are the consummate engineers. They have found what works, what is appropriate, and most important, what lasts here on Earth. After 3.8 billion years of research and development, failures are fossils, and what surrounds us is the secret to survival [25].

The essence of this assertion is that nature is replete with examples that can inform human activities, in this case on climate change mitigation and adaptation. This biomimicry operates in three distinct but interlinked levels of (i) organism level mimicry, which mimics a specific organism; (ii) the behaviour level, which focuses on how an organism behaves to its larger environment and (iii) ecosystem level mimicry [26].

An example of organism level biomimicry is that of the Teatro del Agua outdoor theatre in the Canary Islands, which mimics the Namib desert beetle *Stenocara* in condensing moisture in sea breeze to generate fresh water that is collected and used in this theatre [27]. The focus of behaviour level biomimicry is not the organism per se but rather how that organism behaves in changing both the biotic and abiotic material and systems in its environment [26, 28, 29]. For example, the behaviour of the North American beaver (*Castor canadensis*) of blocking water flow in rivers creates wetlands that retain nutrients, which in turn leads a diversity in both the resident flora and fauna generating a resilient ecosystem [30].

While mimicking individual organisms or their behaviour may benefit efforts seeking to manage climate change, greater benefits accrue if the mimicry is systemic, that is, it covers an entire ecosystem. This approach is concerned with how systems in all individual organisms, the environment and its resources work and interact as a collective. Any important theoretical construct of the ecosystem level biomimicry is the ecosystem principle. The principles (**Table 1**) are an overly simplified representation of how ecosystems operate.

An important point from the table is that an ecosystem is a function of all individual organisms in a locale, their behaviour as individuals and relative to other organisms both of their kind and not of their kind within that system. More important and relevant to this chapter is the point that ecosystems seek to optimise the entire system rather than its components. This is important because it many mean that one component of the system may have to compromise its individual absolute efficiency to deliver a system-wide optimal outcome. The key to such an outcome is using limited resources only for functions that are critical and leaving the rest for others to do the same [4]. Mjimba [31] refers to such an approach as the concept of separating real needs and wants in redefining a new path to sustainable development.

Mimicking ecosystems can focus on both the function and process strategies of ecosystems. The functions of ecosystems relate to services that include the provision of food and medicines, soil formation, detoxification of gases and liquids and climate regulation, among others [32]. The focus on process strategies relates to ecosystem aspects that confer resilience to these systems. This pertains to how ecosystems work both at individual and collective levels, the inherent relationships in the system with the related feedback loops that deliver the capacity and capability of an ecosystem to self-correct and self-heal [4].


#### **Table 1.**

*Ecosystem principles.*

An understanding of the theories of evolution and/or adapting suggests that the self-correction and self-healing manoeuvres of an ecosystem make use of past and present data such as the weather (i.e. temperature, humidity and wind currents) to ensure that the ecosystem remains optimal. An analogous situation to such adjustments is the behaviour of animals like fish, birds and locusts moving in large numbers.

Moving group of such animals have to balance the need (or nature) to maintain close proximity simultaneously with their ability to change both direction and speed as a unit while avoiding colliding with both other group members and physical structures in their environment [7, 33]. This type of behaviour resides in the biological driven response of the individual animals, which manifest as a self-organised system [7]. The formations of these self-organised systems differ between and within the different types of birds, fish or insects as determined by the reasons for their movement and the population size of the group. For instance, in birds, a turn may or may not result in changes in the shape, density and volume of the flock and the positions individual birds take up in the flock [7]. Similarly, schools of fish change their formations based on the size of the schools. Very large schools of up to and more than 10,000 fish have subformations within the entire group. The entire school formations and its subformations change in response to predators and other external influences [33]. An important and relevant observation is that the reaction of individuals that actually sense either danger or an opportunity triggers similar reactions by other group members that may not have sensed the hazard or opportunity [33]. Humans too sometimes conform to this coordinated collective behaviour [34]. For example, the etiquette on the escalators up or down the City of London underground railway network is that as the escalator moves one can stand on the right-hand side and walk on the left-hand side. Largely this enhances the overall

### *Biomimicry, Big Data and Artificial Intelligence for a Dynamic Climate Change Management… DOI: http://dx.doi.org/10.5772/intechopen.84406*

human traffic movement efficiency by decreasing the number of movements to avoid collisions. This is also apparent in the flows of people moving in opposite directions in a street or other constricted spaces. Often the people extemporaneously organise themselves into lanes of uniform walking direction to enhance easier movement. What is interesting is that such arrangements develop without particular individuals either managing or broadcasting these activities or relevant information about them so that others may follow.

In all these forms of self-organised systems, the observed changes are (often) systemic and seek to optimise efficiencies for the entire system rather than its components. This is different from the aforementioned compromises that deliver suboptimal outcome in the conventional policymaking process of democratic societies. Another important feature of such systems is their ability to receive data continually, process this data to generate information that triggers adjustments that deliver rapid changes again seeking at attaining (eventually) optimal outcomes for the systems. Based on these and other observation propose the development of a policymaking machinery that learns and self-adjusts. Such machinery would be appropriate for managing some aspects of dynamic challenges such as those of climate change. This proposal rests on using recent technological developments to drive some aspects of policymaking. We focus on two developments here, Big Data and artificial intelligence, and use these to propose a biomimicry-based policy cycle model for managing the challenges presented by the climate change phenomenon.
