Fig. 3.1. A trade-off occurs between ease and focus versus complexity and impact when assessing system health. Although the complexity increases as the scale of the system increases, so too does the ability to have a larger impact on more system components.
As we frame our assessment, we must acknowledge that the assessment and desired state of the system are influenced by the lens of human perception (Fig. 3.2; Hanisch et al., 2012). For example, we will likely define health differently for agricultural land than for wilderness. Additionally, stakeholder values and expectations play a pivotal role in determining our goals and objectives (Berger-González et al., Chapter 6, this volume). For example, national parks or protected areas are often viewed by the public as ‘pristine and wild’ (Wall-Reinius, 2012). Thus, ensuring that system functioning meets the expectation of park visitors and that system attributes supporting that functioning are properly assessed is important in developing goals for the assessment and management of health within national parks. However, outside of the park, the public may have a very different view of what is acceptable. Ultimately, human beliefs, values and expectations influence every aspect of system health, and it is important that this recognition be incorporated into the health assessment as a co-production of transformational knowledge (see Box 3.1; Berger-González et al., Chapter 6, this volume).
Fig. 3.2. Any assessment of natural systems is based on and affected by the lens of human perception and values.
Mapping the system
Developing a system map is useful for holistically viewing a complex system in order to understand the interrelationships between the system components and their dynamic drivers as well as intervention points that can be used to change an outcome of the system. System mapping has been used on a variety of complex issues ranging from public transport use (Sedlacko et al., 2014) to inequalities in healthy eating (Friel et al., 2017). For all systems, defining the scale of interest is a critical first step for map development as the spatial and temporal scales will define what components, processes and interactions occur in the system and therefore warrant inclusion in the conceptual model. As the ecological scale of interest increases (Fig. 3.1), the map complexity increases.
When developing an ecological system health map, the determinants of health concept may assist in identifying the core attributes of the system upon which to focus. Wittrock et al. (2019) defined wildlife health as ‘the ability ... to realise full function, satisfy daily needs, and adapt to or cope with changing environments’. The adoption of a determinants of health approach would dictate that the system attributes that warrant inclusion in our system map are those that permit full function (as defined by stakeholders), satisfy daily needs and allow adaptation, which are all compilations of various system processes.
One of the most effective ways to map a system is to think hierarchically starting with the simplest ecological scale and build complexity upon it. In this way, each mapped ecological scale contributes to understanding the dynamics of the entire system, and the map reflects the multiple spatial and temporal scales that comprise the system. Graphical models are useful tools for visualizing the details and complexities of a system (Fig. 3.3). These models will often start with consideration of the processes and biotic and abiotic components affecting the individual. Once the individual scale has been mapped, the population scale, or next higher ecological scale, can be mapped. The map at the population scale expands on the map depicted for the individual by now adding in symbols for key processes that link individuals within the population and drive dynamics. These could include reproduction, intraspecific competition, disease transmission and genetic diversity, and, most importantly, could include biotic and abiotic components linked to full functioning of these processes. Once the population scale is complete, the scale could be expanded to the metapopulation and include processes such as immigration, emigration, genetic mixing, disease transmission and other variables along with their biotic and abiotic components. Mapping hierarchically allows us to evaluate the critical interdependencies so that actions intended to improve the health of one component of the One Health triad (e.g. people) do not result in unconsidered impacts on another component (e.g. wildlife).
Fig. 3.3. A simplified graphical representation of components affecting a system’s health. In this example, the largest hierarchical scale of concern is the metapopulation and the smallest scale is the individual. Boxes are used to represent known key processes or rates associated with health at each scale. The optimal rates at each scale are influenced by human values and expectations. Lines between boxes symbolize linkages among processes. Circles are used to represent necessary biotic and abiotic components that permit successful functioning of each process and are connected to their respective process with a line.
When adding features at each ecological scale to the system map, relationships among wildlife, domestic animals and humans warrant inclusion. Mapping human contributions to the system requires a great deal of consideration as intangibles such as human values as well as cultural and economic factors are widespread drivers of change in ecosystems (Berkes, 2004; McGinnis and Ostrom, 2014). Because humans have a direct and indirect effect on the functioning of many of the system’s components, it is important that humans are mapped not only as a member of the system, but that their expectations for the system and its components are included at each ecological scale as part of a transdisciplinary participatory process (Berger-González et al., Chapter 6, this volume). In essence, this requires us to map the social landscape (stakeholder mapping), and wrap that landscape around our depiction of the ecological system. Inclusion of the social aspects of the system in the assessment and associated management recommendations ensures that the system is assessed within the proper context and assures that the assessment accurately captures the opportunities and limitations imposed on the system by human beliefs, values and expectations. For example, suppose our system is a population of white-tailed deer (Odocoileus virginianus) within a given region. Our map of the social landscape at the population scale might include the expectation that this population will provide local indigenous populations with food via hunting. Therefore, the indigenous people would be added to our map and linked to our population via hunting. Similarly, there may also be the expectation that the population is managed so that disease transmission to livestock and damage to agricultural crops are minimized. In this case, livestock producers and farmers are added to the map and linked to the population through population thresholds associated with these expectations. The social landscape also would benefit from inclusion of stakeholder groups that value the population for non-utilitarian reasons such as wildlife viewing or those that see intrinsic value of deer in the landscape. Lastly, the various jurisdictions, management agencies or organizations previously identified when we framed the problem, and who managed the mapped components or processes, will need to be linked to the appropriate system attribute on the map. The final map will depict the entire system, including important components, processes, linkages and social consideration, and as such will reflect each sector of One Health and their interactions.
Methods such as participatory system mapping (PSM), which use a facilitated process to exchange knowledge and straightforward transdisciplinary processes (Berger-González et al.,
