Читать книгу One Health - Группа авторов - Страница 11
Оглавление3 An Ecological and Conservation Perspective
C. LEANN WHITE,1* JULIA S. LANKTON,1 DANIEL P. WALSH,1 JONATHAN M. SLEEMAN1 AND CRAIG STEPHEN2,3
1 US Geological Survey, National Wildlife Health Center, Madison, Wisconsin, USA; 2 Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada; 3 University of British Columbia, Vancouver, British Columbia, Canada
Introduction
Natural ecosystems are facing unprecedented threats that directly threaten human, animal and environmental well-being through decreases in critical ecosystem services (IPBES, 2019). The top five drivers causing the largest global impacts to biodiversity and ecosystem services include: (i) changes in land and sea use; (ii) direct exploitation of organisms (e.g. hunting and fishing); (iii) climate change; (iv) pollution; and (v) invasive alien species (IPBES, 2019). Although One Health acknowledges the link between the health of humans, animals and the environment, One Health discussions have historically focused on the prevention and control of infectious disease at the human–animal interface rather than these large-scale drivers of health. While One Health has succeeded in bringing awareness to the need for proactive disease control measures such as strengthened biosecurity and vaccine development (Roth et al., 2003; Zinsstag et al., 2009; Middleton et al., 2014; Machalaba et al., 2018), disease is only one component of health. In this chapter, we explore the potential for One Health to shift its focus from disease prevention to health promotion to more fully integrate solutions that protect the health of humans, animals and the ecosystems on which we all depend for our economies, livelihoods, food security and health. This shift would facilitate a more seamless inclusion of ecological health and environmental conservation in the One Health paradigm and can serve as the basis for a comprehensive approach to complex problems at the root of global health. A framework for creating and applying health metrics for wildlife and ecological systems is essential for measuring the success of actions aimed at maintaining or shifting systems to desired states.
The focus on disease in One Health
In the late 20th century the number of human infectious disease outbreaks increased globally (Smith et al., 2014). These outbreaks included a resurgence of diseases such as cholera, tuberculosis and viral gastroenteritis (Morse, 1995; Smith et al., 2014) and the emergence of diseases such as severe acute respiratory syndrome (SARS), bovine spongiform encephalopathy and Ebola heamorrhagic fever (Karesh and Cook, 2009). Many recent emerging pathogens were discovered to have animal origins (Jones et al., 2008), and their occurrence and spread were either directly or indirectly facilitated by humans. Through increasing global travel and trade, microbes began to rapidly traverse the globe (Morse, 1995). Infections such as SARS, which originated in small carnivores in Asia, spread via a hotel visitor to five continents in a matter of weeks (Karesh and Cook, 2009) and solidified the idea among the public health community that emerging zoonotic diseases could be a significant threat to global human health.
At a New York symposium in 2004, international health experts developed what became known as the ‘Manhattan Principles on One World One Health’ (Cook et al., 2004). These principles emphasized the need for ‘interdisciplinary and cross-sectoral approaches to disease prevention, surveillance, monitoring, control, and mitigation as well as to environmental conservation more broadly’. At a 2004 summit in Mexico City, Mexico the need for integration of human and animal health systems under the concept ‘one medicine’ was also proposed (Zinsstag et al., 2005). Although this was not the first time that interdisciplinary approaches to combating disease had been proposed, international support and continued emergence of new zoonotic diseases gave the One Health concept more widespread momentum and acceptance. For the next decade, the One Health community increased political and public awareness of the role of animals in disease emergence and brought about new investments such as the US Agency for International Development’s PREDICT programme, which focuses on wildlife most likely to carry new emerging infectious disease threats to humans. International collaborations such as the Global Early Warning System also formed to detect and assess health threats and emerging risks at the human–animal–ecosystems interface (FAO–OIE–WHO, 2019).
Prevention and control of diseases that traffic between wildlife populations, people and domestic animals have been, and will continue to be, important endeavours, particularly wildlife-associated infectious diseases with public health implications and pathogens at the wildlife–livestock interface. For example, elimination of fox rabies in Europe reduced rabies risk to humans and domestic animals (Freuling et al., 2013). On-farm biosecurity for poultry premises will continue to be important to prevent avian influenza transmission between wild birds and poultry (Lee et al., 2018). Efforts to prevent human diseases spilling into wildlife have also been important for maintaining endangered great ape (Hominidae) populations (Gilardi et al., 2017).
Despite its importance, prevention and control of infectious disease will not be sufficient to combat the myriad health effects caused by global forces such as climate change and loss of biodiversity and ecosystem integrity. A health-centric perspective acknowledges the importance of disease as one factor among many that influence the health of humans and animals (see Box 3.1). Scientists often gravitate towards reductionist approaches because observing a single component (e.g. disease) is simpler than trying to observe the entire system. However, the health effects of current One Health challenges, such as climate change loss of ecosystem function, will exceed the impacts of their effects on disease emergence and spread. The complexity of these issues will likely require a systems approach (Zinsstag et al., Chapter 2, this volume) to identify the multitude of drivers (including human values), their interrelationships and feedback loops, so that effective solutions and policy changes can be found (Table 3.1) (Zinsstag et al., 2011).
Table 3.1. Depending on the type of health-related issue, different approaches may be needed to address them. With a disease-centric approach the goal is often disease prevention or mitigation. Systems-oriented health-centric approaches focus on solutions that promote health for which disease prevention or mitigation is only one component. This table illustrates One Health issues associated with either a disease- or health-centric approach.
| Disease-centric approach | Health-centric approach |
|---|---|
| Pathogen spillover and spillback dynamics | Landscape change and habitat fragmentation |
| Prevention of diseases | Securing needs for daily living (e.g. food and water security) (Gordon et al., Chapter 25, this volume) |
| Eradication of pathogens | Climate and environmental change (Zinsstag et al., 2018; Stephen et al., Chapter 17, this volume) |
| Risk factors for diseases | Community resilience capacity Joint health-care provision to remote populations (Schelling et al., 2007; Häsler et al., Chapter 10, this volume) |
Box 3.1. A systems approach to conservation: black-footed ferret case study.
Black-footed ferrets (Mustela nigripes) are one of North America’s most endangered mammals. A major impediment to survival of this species is sylvatic plague (caused by the bacterium Yersinia pestis) as both ferrets and their major food source, prairie dogs, are highly susceptible to this disease. Due to the importance of this disease, sylvatic plague is one of the few wildlife diseases for which a vaccine has been developed and field tested. A combination of the vaccine and insecticides to reduce flea populations that carry the plague bacteria are used to control plague outbreaks on the landscape (Roth, 2019). Although disease management will be essential for survival of black-footed ferrets, it is not the only strategy needed to ensure their recovery. Ferret reintroduction efforts are also impeded by factors such as eradication of prairie dogs in some parts of their native range as they are considered agricultural pests (Casper et al., 2018). Human values such as landowner views of prairie dogs as pests will be critical to address to ensure the long-term recovery of ferrets.
Human, animal and ecosystem health
A shift in focus from a reductionist disease-centric view to a systems-oriented, health-centric model requires a common understanding of what falls under the umbrella of ‘health’. Human health has been variably defined as ‘freedom from disease’ (Boorse, 1977), ‘ability to perform valued social roles’ (Stokes et al., 1982), ‘complete physical, mental, and social well-being’ (WHO, 1948) and ‘capacity to adapt, respond to, or control life’s challenges’ (Frankish et al., 1996). One reason that the definition continues to evolve is that health is not an objective state, but rather a construct dependent on human ideas and values (Hanisch et al., 2012; Lerner and Zinsstag, Chapter 5, this volume). When defining animal health, the value and function of the animal in relationship to humans plays a role. For instance, the criteria used for evaluating the health of a pet dog, valued for companionship, will be different from those used when considering the health of a dairy cow, valued for production. Similarly, for wildlife, ideal survival rates, reproduction rates, or disease prevalence in a population will be determined by human values and are likely to differ for an endangered species compared with a nuisance species as well as among stakeholder groups (e.g. wildlife enthusiasts, animal rights activists, tourism facilities, livestock farmers, land managers and land owners).
Considering health as the capacity of populations to cope with change (Stephen, 2014) emphasizes characteristics that affect vulnerability and resilience (Obrist et al., 2010; Zinsstag et al., Chapter 31, this volume) and could be used to fortify health in the face of uncertainty rather than waiting to address hazards after they emerge (Stephen, 2016). In wildlife as in humans, capacity to cope with change is influenced by interacting biological, social, societal and environmental determinants. As such, health can be assessed indirectly by assessing these determinants, including hazards, such as disease, and promoters, such as biodiversity and habitat quality (Allen et al., 2011). To this end, determinants of wildlife health models based on a human public health model has been proposed (Wittrock et al., 2019). The determinants influencing wildlife health can include factors related to: (i) biological endowment (e.g. genetics, age, reproductive status, disease); (ii) social environment (e.g. population demographics, interspecies and intraspecies competition); (iii) access to needs for daily living (e.g. quality habitat and food); (iv) abiotic environment (e.g. climate and anthropogenic pressures); (v) direct mortality (e.g. hunting and predation); and (vi) human expectations (e.g. policies and stakeholder values). Importantly, these factors were considered likely to be of practical significance to both wildlife managers and the scientists who provide data on which management decisions are based (Wittrock et al., 2019). By considering all the factors that contribute to health, we can assess which are most easily influenced by available tools (e.g. management actions, development of new technologies, policy changes, or outreach programmes) and thereby develop a practical framework for the promotion of healthy wildlife populations.
The concept of ecosystem health has been the topic of numerous publications, conferences, organizations and educational curricula (Rapport and Maffi, 2011). However, a recent meta-analysis of ecosystem assessments in freshwater and estuarine environments (O’Brien et al., 2016) indicates that less than 15% of ecosystem health studies clearly defined the term or justified their choice of health indicators. While broad criteria for ecosystem health, including stability, sustainability, vigour, vitality, organization and resilience, have been proposed, it is unclear how or how often these concepts are being applied in a practical way by scientists or natural resource managers (Costanza, 1992; Rapport et al., 1998; Rapport and Maffi, 2011; Lu et al., 2015). It has also been argued that ecosystem health is not a matter of science, but a value-driven policy construct (Cumming and Cumming, 2015). Securing consensus on what constitutes health or thresholds for acceptable ecosystem changes are context-dependent undertakings informed by local priorities, perspectives and available information. While there are accepted thresholds for many ecosystem-level harms that affect human health (e.g. water quality standards and air pollution guidelines) there remains considerable debate and uncertainty on thresholds that indicate important changes to the health of the ecosystem itself or on ecological tipping points and self-enforcing feedback loops (see van Nes et al., 2016) that cause potentially irreversible changes to ecosystem function.
Establishing Metrics for System Health
For our purposes, the term ‘system’ is used to refer to any set of interacting biotic and abiotic components that form a unified whole. This could be at the level of an individual, a population or beyond. The metrics we propose follow a systems approach (Stoett, 2016) for examining health whereby the health of the system, as a whole, remains the focal point, while still considering the parts of the system (e.g. disease, predators, climate, food availability) and how they interact with one another.
A definition of health for any given system is a critical first step in the assessment and promotion of the system’s health. From the definition, we can develop metrics that can be used to assess health, monitor dynamic processes and guide management decisions. If the goal is to shift a system to a new desired state, examining metrics over time will be important for evaluating whether our efforts to improve health are having the desired effects.
To the extent possible, metrics warrant standardization when comparing health of different systems or measuring changes within a system over time. Creating comprehensive metrics allows us to bring the scientific method to bear on the assessment and promotion of system health. Recognizing the impossibility of generating metrics that encompass every situation and system, we describe a framework for creating metrics that can be tailored to various systems (Table 3.2).
Table 3.2. Steps in assessing system health.
| Steps in assessing system health | Questions to consider |
|---|---|
| Describe the goals and objectives of the assessment | Why are we measuring the system’s health? How will the information be used? |
| Describe the system | What are the ecological, spatial and temporal scales? What are the key jurisdictions and disciplines? What are the various stakeholder and rights holders’ expectations for how the system should function or what services it should provide? |
| Map the system | What are the major components of the system and their relationships with other components at each ecological scale? What are the determinants of health at each scale? What human expectations must be considered at each scale? |
| Create metrics for mapped system processes | What are the key rates and indices? Do thresholds for acceptable rates of change need to be negotiated among stakeholders? |
| Highlight processes that negatively or positively impact health | Why are these deficiencies or strengths present? Can actions be taken to reduce or strengthen these processes? |
| Added value of a systems approach (Zinsstag et al., Chapter 2, this volume) | Incremental benefit from choosing a systemic approach when compared to single sector (reductionist) approaches |
| Prioritize processes for management or monitoring efforts | Which system processes are best targets for management actions? Which processes can be used to monitor the system over time? |
Framing the problem
After system health has been defined as clearly as possible, the first step to measuring system health is to explicitly state the goals and objectives of the health assessment and to express the expected added value as compared with single sector approaches (Zinsstag et al., Chapter 2, this volume). The goals provide the justification for the resources needed to conduct an assessment and indicate how the results of the assessment will be used. Objectives describe the steps needed to achieve the goal.
To have well-articulated goals and objectives, the study system and scale of interest must be identified. The health assessment could be focused on a population, an ecological community, a metapopulation or an entire ecosystem. The complexity and associated effort of conducting the assessment, as well as the impact and degree of focus, will be directly linked to the ecological scale (Fig. 3.1). The spatial scale must also be defined. In some cases, the spatial scale will arise naturally from the ecological scale; in other cases, it may be determined by jurisdictional boundaries, available resources, or by some probabilistic sampling scheme underpinning the assessment. Next, the temporal scale of interest should be identified. The health of a system is dynamic, with the potential to change rapidly under various scenarios (e.g. introduction of novel diseases), making it important to determine the time frame over which the assessment will be applied. For example, are we interested in the change in a system’s health over the last 5 years, the last 3 months, or only the present state? Are there natural fluctuations or cycles that need to be considered? If we are comparing the current state of the system to a past state or another system, the functions or attributes that we are referencing also need to be specified. Similarly, the jurisdictions that the assessment will cross should also be identified. As we expand our definition of health, it becomes likely that aspects of the system will fall within the purview of multiple agencies. Lastly, when investigating systems, it is inevitable that multiple scientific disciplines and non-academic actors like communities and authorities will need to be incorporated, as a transdisciplinary participatory process (Berger-González et al., Chapter 6, this volume) into the assessment. These scientists and non-academic actors can provide requisite expertise needed to construct and implement the assessment.
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., Chapter 6, this volume) among a group of stakeholders or experts, may be useful for developing system health maps. PSM steps have been previously outlined (Sedlacko et al., 2014) and include defining the scope and boundaries of the system, system components, causal pathways, feedback loops, implications and knowledge gaps. The process may also be particularly relevant to development of system health maps because it allows for expression and inclusion of human values and a diversity of worldviews through development of mental models (Sedlacko et al., 2014). The National Cancer Institute’s (2007) report on tobacco control and the UK government’s obesity report (Butland et al., 2007) provide two detailed descriptions of systems map development and their use in directing needed interventions and policy changes for complex public health issues.
Once the system health map is developed, it will provide the structure for conducting health assessments and monitoring the effects of actions intended to improve system health. It is important to note that our understanding and, as a result, the map of the system will inevitably be imperfect and initially overly simplistic with missing components, unknown linkages and feedback loops, missing stakeholder groups, and misconceptions of system functioning. However, the system map can always be updated and improved as knowledge and understanding of the system grows.
System health metrics
Mapping the system at the scale of interest is the basis for development and selection of metrics. As each map is likely to be unique, a universal set of metrics is not possible. However, it will be important that metrics are based on the system map and correspond to the processes that are depicted. In other words, the metrics are an assessment of the system’s functioning as characterized by the degree to which each critical process is operating as expected or desired.
Many of the system’s processes can be measured in terms of rates (Fig. 3.3). For example, survival, immigration, emigration, reproduction, disease transmission and even stakeholder satisfaction are generally quantified in terms of their respective rates. Rates can be powerful for conducting assessments because there is often associated literature to help determine whether the measured rates are a positive or negative indicator of health. Rates can also be used as inputs into mathematical models to forecast the trajectory of the system. They allow direct comparisons between systems and can be used to measure changes over time. Similar to other studies of natural systems, the presence of epistemic and linguistic uncertainty warrants acknowledgement for system health assessments because factors such as population size are usually not precisely known and yet are needed as a denominator for many rates. Reviews of common types of uncertainty in studies of natural systems and methods to address them have previously been published (Regan et al., 2002; Milner-Gulland and Shea, 2017).
When system processes are difficult to measure directly, indices may be useful for indirectly measuring the process (Johnson, 2008). Indices can be quantitative or qualitative and should be used with caution because they rely on assumptions about how they relate to the process of interest. They may also be less sensitive than direct measurements or show some lag in responding to system dynamics. Despite these drawbacks, indices can be useful tools and, in some cases, may be the only means of assessing a particular process. An example of an application using indices as metrics of system health is the United States Department of Agriculture’s (USDA) tool for assessing the health of riparian systems (USDA, 2012), which uses a series of qualitative rankings associated with key processes. Questions in the tool include asking the user to rank the degree to which the riparian vegetation is composed of noxious weeds because their ‘presence or occurrence ... usually indicates a downward trend in ecological condition and riparian health’ (USDA, 2012). Regardless of whether the assessment tool is quantitative or qualitative, it will be important to continue to recognize that different stakeholders may have different thresholds for acceptable levels of change or system function.
Developing metrics to adequately assess how the system will respond to changes can be challenging, but it is critical that the ability of the system to adapt to change or the amount of change a system can absorb before it moves into a new state (i.e. resilience) be included in health assessments. Natural systems, regardless of ecological scale, are dynamic, and their ability to adapt to change is a key attribute of whether the system is fully functioning (Holling, 2001). Resilience of a system can be assessed by assessing its response to a perturbation (Holling, 1973). However, it is generally impractical or even unwise to experimentally perturb a system, so assessments require carefully choosing aspects of the system that provide evidence of how the system might respond to perturbations. This is where a system map becomes invaluable, because, if it has been constructed properly, it should highlight the processes that will drive change and associated system responses. For example, if we are assessing how a forest ecosystem will respond to fire disturbance, we might assess the quality of the seed bank, survey for the presence of invasive species, determine the degree to which species within the system are adapted to fire, look for evidence of past fires to understand the periodicity of fire in the system, quantify fuel loads, and so on. Using the information from various lines of evidence within the system, we can specify where in the adaptive cycle (Holling, 2001) the system currently lies and deduce how resilient the forest is likely to be to a new fire disturbance. We can also look for surrogate systems that may be similar to the system of interest but have gone through recent disturbances, and then draw conclusions from how the surrogate responded and apply them to how our system might be expected to behave or adapt under similar perturbation. Another useful set of tools for measuring resilience are mathematical models and computer simulation techniques that help forecast system responses to future change (Huyvaert et al., 2018). These models could be built from our system map, with system processes connected through specified mathematical relationships, and parameterized based on the values for the various metrics we described earlier. The simulation or model is then run and resulting trajectories for the system capture not only the current state of the system but also future states if processes/metrics remain static. The adaptive ability of the system can be assessed by varying system processes based on their likely response to some change and quantifying how the new forecasts of system states have been altered compared with forecasts when processes remained static. Regardless of the chosen tool, assessing the system’s adaptability cannot be ignored because it will have direct implications for system functioning and its associated health.
Once each of the system processes have been measured either qualitatively or quantitatively, processes that are not meeting stakeholder expectations or may be negatively impacting the health or function of the system will need to be highlighted. For these processes, a closer examination of why the process is not functioning to its expected potential is required. This entails examining the system map and identifying the biotic and abiotic components tied to these processes and determining where there are deficiencies. For example, if juvenile recruitment of a population is too low, resulting in forecasts of future population declines, a search for deficiencies in critical components will need to be made (e.g. a lack of thermal cover leading to nest failures). The identified deficiencies can become the target of future management actions to improve health and could drive the final recommendations arising from the health assessment.
A practical question associated with metrics and their development is where to obtain the information or data to parameterize or determine the values of the metrics. Information can be gathered in several ways. First, scientists can collect their own data by measuring the system directly. For example, data for estimating the survival rate of individuals within a population can be measured using radio-telemetry tags that alert the scientist to mortality events. Remote sensing techniques are an invaluable resource for measuring landscape level factors such as land cover, human infrastructure (e.g. roads), plant phenology and productivity, and climate and weather (Neumann et al., 2015). Key parameters rates (survival, immigration, emigration, reproduction, etc.) are also available in the published literature for many systems (or from similar systems) and can be used to begin assessing metrics to assess healthy versus unhealthy states for the system. Expert knowledge and opinion can also be informative, particularly when other sources of information are lacking. Experts may include noted scientists, natural historians and agency personnel, as well as non-scientific experts such as indigenous people, who have a long and rich connection to the natural system. We can also look to novel data streams arising from technological advancements. Web-based acquisition of information based on reports from citizen scientists or the general public may prove useful, particularly for large spatial or temporal scales, and may also help describe the social landscape of the system. Similarly, documenting human use of web-based services and searches may help in understanding and quantifying human beliefs, values or expectations of a system, as well as help inform impacts to human health of system processes. Most health assessments will need to avail themselves of many different sources of information to adequately measure the various features of the system map.
Mobilizing knowledge to action
Measuring the health status of a wild population or ecosystem has its own merits but without moving this knowledge into action, little will come of it. The gaps between knowing what to do to promote health across people, animals and environments and doing it can be wide and difficult to breach. Evidence can be used to change outcomes, but only if people apply it in practice and policy.
One Health practitioners can benefit from models of individual and social change that are found in the health promotion, marketing and business literature. The Health Belief Model, for example, is one of the most widely used conceptual frameworks for guiding human health interventions. The model contains several concepts that predict why people will take a health-promoting action, including perceived susceptibility to harm, benefits and barriers to change, cues to action and self-efficacy (Champion and Skinner, 2008). The Theory of Reasoned Action and the Theory of Planned Behaviour focus on individual motivational factors as determinants of the likelihood of performing a specific action (Ajzen, 1991). They focus on how attitudes, subjective norms and perceived control influence health behaviours. Theories and models such as these can help target interventions designed to encourage the adoption of health-protecting measures on an individual or collective level.
Multiple theories of change have been developed (Mitchell, 2013) and can be used to build a bridge between what we know, what we want to achieve, and the activities it will take to get us from mapping and measuring a system’s health to mobilizing what we learn into action. Given the complexity of ecosystem health, most actions will require involvement of researchers, local knowledge creators, managers, planners, beneficiaries and stakeholders at the start to develop consensus on the shared goals by explicitly documenting different views and assumptions and by helping people see how sharing their knowledge contributes to long-term positive impacts.
Knowledge mobilization is an active process of creating linkages between creators and users of information to produce value-added outcomes. The Knowledge to Action Framework was developed to help create and sustain evidence-based actions (Field et al., 2014). This framework, based on commonalities of over 30 planned-action theories, follows the premise that knowledge is best co-created by researchers and those who need to use the knowledge and includes both a knowledge creation and an action cycle as transdisciplinary research (Hirsch Hadorn et al., 2008; Berger-González et al., Chapter 6, this volume). The framework provides a series of steps to help mobilize available research into action while accounting for local context and explicit assessment of barriers and facilitators to use of the knowledge for creating changes to actions. Regardless of the type of knowledge user, knowledge mobilization requires a relentless dedication to understanding that user’s needs, and creating strategies and tools to engage, inform and motivate them under a variety of circumstances. A clear plan that outlines roles, responsibilities and authority to support action as well as secure the partnership and resources needed to sustain change are as essential as the efforts used to measure the health of people, animals or their shared ecosystems.
Ecosystem Health is Human Health
The connection between ecosystem health and human health is evident in the history of human morbidity and mortality, whose causes parallel land use, from mortality due primarily to predation, famine and vector-borne diseases in pre-modern societies, to the rise of infectious and waterborne diseases associated with agrarian settlements and industrial cities, and finally modern-day illnesses of sedentary lifestyles or those related to poor air quality (Rapport and Maffi, 2011). Today, increasingly rapid ecosystem degradation compounds these health effects through declines in basic ecosystem services such as provision of food and water (IPBES, 2019).
In addition to human population health, human cultural health is intimately tied to the ecosystems in which those cultures arose. Loss of biodiversity and ecosystem integrity is mirrored by loss of cultural diversity caused by similar anthropogenic pressures, including land-use change, exploitation or over-harvesting of resources, environmental contamination and introduction of non-native species (Rapport and Maffi, 2011). New fields of study concerned with cultural and linguistic conservation have developed in parallel with those of ecological conservation, pointing to what has been termed a ‘converging extinction crisis’ of social and ecological systems across the world (Harmon, 2002).
As the One Health community has highlighted in its work with emerging zoonoses of wildlife origin, and as the risk to humans from loss of ecosystem services confirms, the health of wild populations and wild places is of importance to more than just wildlife. By conveying the importance of environmental health as a source of human determinants of health (e.g. clean water and air), actions can be promoted that contribute to conservation goals as well as human health and prosperity (Wood and DeClerk, 2015). The challenge before us is to achieve a degree of consensus among various stakeholders about what health means for a given population or system, how it can be assessed, and how it can be promoted (Berger-González et al., Chapter 6, this volume).
There is no single answer to the question ‘What is health?’ for ecosystems or their various components, be it wildlife, cattle or humans. Nevertheless, health is at the very heart of One Health, and it is an important question to ask and answer as the One Health framework continues to evolve. Without an explicit definition of the desired state that resonates across all pillars of One Health, metrics for evaluating progress towards that state, and commitment to working towards that state for all stakeholders, and addressing how an added value of cooperation between human and animal health, environment and conservation sciences can be created, there can be no true progress. The One Health community, with its multidisciplinary approach and recognition of the interdependence of multiple complex systems, is well positioned to take up this question.Disclaimer: Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.
References
Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179–211.
Allen, C.R, Cumming, G.S., Garmestani, A.S., Taylor, P.D. and Walker, B.H. (2011) Managing for resilience. Wildlife Biology 17, 337–349. doi.org/10.9281/10-084
Berkes, F. (2004) Rethinking community-based conservation. Conservation Biology 18, 621–630.
Boorse, C. (1977) Health as a theoretical concept. Philosophy of Science 44(4), 542–573.
Butland, B., Jebb, S.Kopelman, P., McPherson, K., Thomas, S., et al. (2007) Tackling Obesities: Future Choices – Project Report, 2nd edn. Government Office for Science, London. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf (accessed 15 January 2020).
Casper, A.M.A., Hill, D.P. and Rathburn, M.K. (2018) Managing populations, diseases, and landowner perceptions of prairie dogs for black-footed ferret reintroduction. Case Studies in the Environment 2, 1–9. doi.org/10.1525/cse.2018.001131
Champion, V.L. and Skinner, C.S. (2008) The health belief model. In: Glanz, K., Rimer, B.K. and Viswanath, K. (eds) Health Behavior and Health Education: Theory, Research, and Practice.Wiley, San Francisco, California, pp. 45–65.
Cook, R.A., Karesh, W.B. and Osofsky, S.A. (2004) The Manhattan Principles on ‘One World, One Health’. Conference summary: One World, One Health: Building Interdisciplinary Bridges to Health in a Globalized World, 29 September 2004, New York. Available at: http://www.oneworldonehealth.org/sept2004/owoh_sept04.html (accessed 3 June 2020).
Costanza, R. (1992) Towards an operational definition of ecosystem health. In: Costanza, R. (ed.) Ecosystem Health: New Goals for Environmental Management. Island Press, Washington, DC, pp. 237–256.
Cumming, D.H.M. and Cumming, G.S. (2015) One Health: an ecological and conservation perspective. In: Zinsstag, J., Schelling, E., Waltner-Toews, D., Whittaker, M. and Tanner, M. (eds) One Health: the Theory and Practice of Integrated Health Approaches. CABI International, Wallingford, UK, pp. 38–52.
FAO–OIE–WHO (2019) Global Early Warning System for Health Threats and Emerging Risks at the Human–Animal–Ecosystems Interface. Food and Agriculture Organization of the United Nations–World Organisation for Animal Health–World Health Organization. Available at : http://www.glews.net (accessed 5 July 2019).
Field, B., Booth, A., Ilott, I. and Gerrish, K. (2014) Using the Knowledge to Action Framework in practice: a citation analysis and systematic review. Implementation Science 9, 172. doi: 10.1186/s13012-014-0172-2
Frankish, C.J., Green, L., Ratner, P., Chomik, T. and Larsen, C. (1996) Health impact assessment as a tool for population health promotion and public policy: a report submitted to the Health Promotion Development Division of Health Canada. University of British Columbia, Vancouver, Canada. Available at : http://catalogue.iugm.qc.ca/GEIDEFile/healthimpact.PDF?Archive=192469191064 (accessed 5 July 2019).
Freuling, C.M., Hampson, K., Selhorst, T., Schröder, R., Meslin, F.X.et al. (2013) The elimination of fox rabies from Europe: determinants of success and lessons for the future. Philosophical Transactions of the Royal Society of London B: Biological Sciences 368, 20120142. doi: 10.1098/rstb.2012.0142
Friel, S., Pescud, M., Malbon, E., Lee, A., Carter, R.et al. (2017) Using systems science to understand the determinants of inequities in healthy eating. PLoS ONE 12: e0188872. doi: 10.1371/journal.pone.0188872
Gilardi, K.V., Gillespie, T.R., Leendertz, F.H., Macfie, E.J., Travis, D.A.et al. (2017) Best Practice Guidelines for Health Monitoring and Disease Control in Great Ape Populations. International Union for Conservation of Nature and Natural Resources, Gland, Switzerland.
Hanisch, S., Riley, S. and Nelson, M. (2012) Promoting wildlife health or fighting wildlife disease: insights from history, philosophy and science. Wildlife Society Bulletin 36, 477–482.
Harmon, D. (2002) InLight of our Differences: How Diversity in Nature and Culture Makes Us Human. Smithsonian Institution Press, Washington, DC.
Hirsch Hadorn, G., Hoffmann-Reim, H., Biber-klemm, S., Grossenbacher, W., Joye, D.et al. (2008) Handbook of Transdisciplinary Research. Springer, Berlin.
Holling, C.S. (1973) Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4, 1–23.
Holling, C.S. (2001) Understanding the complexity of economic, ecological, and social systems. Ecosystems 4, 390–405. doi: 10.1007/s10021-001-0101-5
Huyvaert, K.P., Russel, R.E., Patyk, K.A., Craft, M.E., Craft, P.C.et al. (2018) Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals. Veterinary Sciences 5(4), 92. doi: 10.3390/vetsci5040092
IPBES (2019) Summary for Policy Makers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES Secretariat, Bonn, Germany. Available at: https://www.ipbes.net/system/tdf/spm_global_unedited_advance.pdf?file=1&type=node&id=35245 (accessed 31 May 2019).
Johnson, D.H. (2008) In defense of indices: the case of bird surveys. Journal of Wildlife Management 72, 857–868.
Jones, K.E., Patel, N.G., Levy, M.A., Storeygard, A., Balk, D., et al. (2008) Global trends in emerging infectious diseases. Nature 451, 990–993.
Karesh, W.B. and Cook, R.A. (2009) One world – one health. Clinical Medicine Journal 9, 259–260. doi: 10.7861/clinmedicine.9-3-259
Lee, D., Torchetti, M., Hicks, J., Killian, M., Bahl, J., et al. (2018) Transmission dynamics of highly pathogenic avian influenza virus A (H5Nx) Clade 2.3.4.4, North America, 2014–2015. Emerging Infectious Diseases 24(10), 1840–1848. doi: 10.3201/eid2410.171891
Lu, Y., Wang, R., Zhang, Y., Su, H., Wang, P., et al. (2015) Ecosystem health towards sustainability. Ecosystem Health and Sustainability 1, 1–15. doi: 10.1890/EHS14-0013.1
Machalaba, C.C., Salerno, R.H., Behravesh, C.B., Benigno, S., Berthe, F.C.J.et al. (2018) Institutionalizing One Health: from assessment to action. Health Security 16, S1. doi: 10.1089/hs.2018.0064
McGinnis, M.D. and Ostrom, E. (2014) Social-ecological system framework: initial changes and continuing challenges. Ecology and Society 19, 30. doi: 10.5751/ES-06387-190230
Middleton, D., Pallister, J., Klein, R., Feng, Y.R., Haining, J.et al. (2014) Hendra virus vaccine, a one health approach to protecting horse, human, and environmental health. Emerging Infectious Disease 20, 372–379. doi: 10.3201/eid2003.131159
Milner-Gulland, E.J. and Shea, K. (2017) Embracing uncertainty in applied ecology. Journal of Applied Ecology 54, 2063–2068. doi: 10.1111/1365-2664.12887
Mitchell, G. (2013) Selecting the best theory to implement planned change. Nursing Management 20, 32–37.
Morse, S.S. (1995) Factors in the emergence of infectious disease. Emerging Infectious Disease 1, 7–15.
National Cancer Institute (2007) Greater than the sum: systems thinking in tobacco control. Tobacco Control Monograph No. 18. NIH Publication No. 06-6085. US Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Bethesda, Maryland. Available at: https://cancercontrol.cancer.gov/brp/tcrb/monographs/18/m18_complete.pdf (accessed 15 January 2020).
Neumann, W., Martinuzzi, S., Estes, A.B., Pidgeon, A.M., Holger, D.et al. (2015) Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Movement Ecology 3, 8. doi: 10.1186/s40462-015-0036-7
O’Brien, A., Townsend, K., Hale, R., Sharley, D. and Pettigrove, V. (2016) How is ecosystem health defined and measured? A critical review of freshwater and estuarine studies. Ecological Indicators 69, 722–729.
Obrist, B., Pfeiffer, C. and Henley, R. (2010) Multi-layered social resilience: a new approach in mitigation research. Progress in Development Studies 10, 283–293.
Rapport, D.J. and Maffi, L. (2011) Eco-cultural health, global health, and sustainability. Ecological Research 26, 1039–1049.
Rapport, D.J., Costanza, R. and McMichael, A.J. (1998) Assessing ecosystem health. Trends in Ecology and Evolution 13, 397–402.
Regan, H.M., Colyvan, M. and Burgman, M.A. (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications 12, 618–628. doi: 10.1890/1051-0761(2002)012[0618:ATATOU]2.0.CO;2
Roth, J.D. (2019) Sylvatic plague management and prairie dogs – a meta-analysis. Journal of Vector Ecology 44, 1–10.
Roth, F., Zinsstag, J., Orkhon, D., Chimed-Ochir, G., Hutton, G.et al. (2003) Human health benefits from livestock vaccination for brucellosis: case study. Bulletin of the World Health Organization 81, 867–876.
Schelling, E., Bechir, M., Ahmed, M.A., Wyss, K., Randolph, T.F. and Zinsstag, J. (2007) Human and animal vaccination delivery to remote nomadic families, Chad. Emerging Infectious Diseases 13, 373–379.
Sedlacko, M., Martinuzzi, A., Ropke, I., Videira, N. and Antunes, P. (2014) Participatory systems mapping for sustainable consumption: discussion of a method promoting systemic insights. Ecological Economics 106, 33–43. doi: 10.1016/j.ecolecon.2014.07.002
Smith, K.F., Goldberg, M., Rosenthal, S., Carlson, L., Chen, J., et al. (2014) Global rise in human infectious disease outbreaks. Journal of the Royal Society Interface 11, 1–6.
Stephen, C. (2014) Toward a modernized definition of wildlife health. Journal of Wildlife Diseases 50, 427–430.
Stephen, C. (2016) Wildlife health: a foundation for preparedness for environmental change. The Canadian Veterinary Journal 57, 1095–1097.
Stoett, P. (2016) Wicked Problems, Dynamic Solutions: the Ecosystem Approach and Systems Thinking. Manual. United Nations Environment Programme, Ecosystem Approach and Systems Thinking, Massive Open Online Course, Concordia University, Montréal, Quebec, Canada. Available at: https://spectrum.library.concordia.ca/981447/1/EASTMOOCSOURCEBOOKfinal.pdf (accessed 5 July 2019).
Stokes, J., Noren, J. and Shindell, S.J. (1982) Definition of terms and concepts applicable to clinical preventive medicine. Community Health 8, 33. doi: 10.1007/BF01324395
USDA (2012) Riparian Assessment: Using the NRCS Riparian Assessment Method. Environment Technical Note No. MT-2 (Rev. 1). United States Department of Agriculture, Natural Resources Conservation Service, Montana, USA. Available at: https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs144p2_050752.pdf (accessed 18 July 2019).
van Nes, E.H., Arani, B.M.S., Staal, A., van der Bolt, B., Flores, B.M.et al. (2016) What do you mean, ‘tipping point’?Trends in Ecology and Evolution 31, 902–904. doi: 10.1016/j.tree.2016.09.011
Wall-Reinius, S. (2012) Wilderness and culture: tourist views and experiences in the Laponian World Heritage Area. Society and Natural Resources 25, 621–632.
WHO (1948) Preamble to the constitution of the World Health Organization as adopted by the International Health Conference, New York, 19–22 June 1946; and entered into force on 7 April 1948. World Health Organization, Geneva, Switzerland. Available at: https://www.who.int/about/who-we-are/constitution (accessed 5 July 2019).
Wittrock, J., Duncan, C. and Stephen, C. (2019) A determinants of health conceptual model for fish and wildlife health. Journal of Wildlife Diseases 55, 285–297. doi: 10.7589/2018-05-118
Wood, S.L. and DeClerck, F. (2015) Ecosystems and human well-being in the Sustainable Development Goals. Frontiers in Ecology and the Environment 13, 123.
Zinsstag, J., Schelling, E., Wyss, K. and Bechir, M. (2005) Potential of cooperation between human and animal health to strengthen health systems. Lancet 366(9503), 2142–2145.
Zinsstag, J., Dürr, S., Penny, M.A., Mindekem, R., Roth, F.et al. (2009) Transmission dynamics and economics of rabies control in dogs and humans in an African city. Proceedings of the National Academy of Sciences of the United States of America 106, 14996–15001.
Zinsstag, J., Schelling, E., Waltner-Toews, D. and Tanner, M. (2011) From ‘one medicine’ to ‘one health’ and systemic approaches to health and well-being. Preventive Veterinary Medicine 101, 148–156.
Zinsstag, J., Crump, L., Schelling, E., Hattendorf, J., Maidane, Y.O.et al. (2018) Climate change and One Health. FEMS Microbiology Letters 365(11).
