Opinion: Uncertainty in Wild Animal Welfare is Not an Intractable Problem and Welfare Biology is Well-Positioned to Tackle It

This piece is part of a short series on how our research team thinks about uncertainty. These posts represent the opinions of individual staff members, and not necessarily the organizational position of Wild Animal Initiative on uncertainty.

Jane Capozzelli

Summary

Uncertainty is not an intractable problem nor should uncertainty necessarily stop us from studying interventions to improve wild animal welfare. Restoration ecology has a long history of environmental interventions from which animal welfare advocates can draw to reduce uncertainty in interventions. One such tool is the reference system. Another is adaptive management. Ultimately, if interventions are designed as experiments in nature, then even failed interventions will generate knowledge to help animals in the future.

Introduction

I respect the cautious approach to uncertainty that many animal welfare advocates put forward as a valuable perspective. Nothing is known in ecological systems with 100% certainty (Walters and Holling 1990, Regan et al. 2005). Yet conservation biology remains an action-based science, operating under the principle that waiting to be more certain will do more harm than good (Soulé 1985). The same can be said for animal welfare interventions. If we wait to act, then there is ongoing animal suffering that continues to go unaddressed. Yet, though ecologists and animal welfare advocates face similar issues associated with uncertainty, researchers in these fields seem to approach the problem from different perspectives.

Restoration ecologists have only recently begun to emphasize that increasing certainty of the outcomes of interventions and understanding the causes of variability in nature is critical to turn restoration into a predictable and generalizable science (Hobbs and Norton 1996, Herrick et al. 2006, Brudvig 2017), even though restoration ecology has been around for nearly a century. Animal welfare advocates, in contrast, already recognize the importance of accounting for uncertainty in interventions. The broad support for addressing uncertainty in the animal welfare community is ultimately an advantage that will help us improve the lives of animals. To achieve this goal, we should consider adopting tools from restoration and conservation ecology that work and adapt them to welfare biology, regardless of whether these fields espouse the same values (Capozzelli 2019, Rowe 2019).

Reference ecosystems

The original tool for clarifying uncertainty in interventions is the reference ecosystem, defined as a model in nature which approximates the restoration target. This model identifies and communicates a shared vision of project targets and provides a basis for setting goals, optimizing activities, and monitoring outcomes as the project progresses (from McDonald et al. 2016).

The classical concept of the reference ecosystem emphasized a restoration trajectory moving toward a single target, composed of a desirable suite of native plant species which reflect the historical ecosystem before human degradation (Figure 1; see also Higgs et al. 2014 and McAlpine et al. 2016).

Fig. 1: Simplification of Bradshaw’s (1984) model of restoration, where an intervention project (the restoration) turns a degraded site, with nearly zero ecosystem function and community structure, to a more desirable state (the target) with increased community structure and function. Community structure generally refers to the number of species in the community and ecosystem function is the number of processes (e.g. pollination, nutrient cycling) these species provide. Reproduced from Zedler et al. 2007.

Fig. 1: Simplification of Bradshaw’s (1984) model of restoration, where an intervention project (the restoration) turns a degraded site, with nearly zero ecosystem function and community structure, to a more desirable state (the target) with increased community structure and function. Community structure generally refers to the number of species in the community and ecosystem function is the number of processes (e.g. pollination, nutrient cycling) these species provide. Reproduced from Zedler et al. 2007.

Oftentimes, as the number of species in a community increases, the number of functions these species provide to the ecosystem also increases (Petchey and Gaston 2002, Rapacciuolo et al. 2019). Historical systems are thus chosen for reference systems under the assumption that systems without human disturbances have the highest function and community structure. The same premise continues to underpin the use of historic reference ecosystems as restoration targets (Higgs et al. 2014).  In this sense, the reference system is a purpose-oriented tool aimed at “doing good” (Cooke et al. 2018). It is not used to turn back time to an arbitrary pre-human state (McDonald et al. 2016, Rowe 2019).

The classical model, however, has been criticized in the scientific literature, causing the concept of the reference ecosystem to evolve. Historic ecosystems are now thought of as a guideline which reduce uncertainty in restoration planning, not necessarily the best baseline (Clewell 2000, Choi 2004). The paradigm of the single target with a static species composition has shifted to that of reference dynamics, defined as a range of values for discrete ecological processes, which contain an average value as well as a measure of variance and range of natural variability. The new focus on reference dynamics emphasizes restoring the processes which maintain systems, species interactions, and variability in time and space rather than species composition alone (Falk 2006). The reference dynamics framework can also be used to understand the diversity of experiences of wild animals, if, for example, individuals of the same species are grouped based on the similarities of their life events (Alonso and Schuck-Paim 2017).

Historic reference states do not always encapsulate the best strategy to reach restoration targets, and many restoration and conservation scientists agree that it should not be the target in cases where it is no longer practical (Clewell 2000, Hobbs et al. 2009, Miller and Bestelmeyer 2016, Bestelmeyer et al. 2018; but see Murcia et al. 2014 and Doak et al. 2015). For example, it is well acknowledged that some parts of the earth have been inhabited by humans for more than a millennia and humans have been transforming ecosystems for hundreds, or even thousands, of years (e.g. Wilkinson 2004, Hobbs et al. 2006, Archibald et al. 2012). These ecosystems cannot be maintained in their historic state without frequent human disturbances. For example, North American grasslands can transition to shrublands unless prescribed fires are applied at least every three years (Ratajczak et al. 2016) and there is increasing evidence that grasslands evolved under frequent fire regimes maintained by prehistoric and indigenous peoples (Abrams 2006, Archibald et al. 2012). In these types of human-dominated environments, restoration work aims to integrate the human dimension into the ecosystem restoration plan and actively seeks strategies to entwine social and ecological systems (Miller et al. 2012, Standish et al. 2013).

Many “natural” alternative types of ecosystems can, and have, occurred throughout the history of any one environment (Cortina et al. 2006). For example, the Central United States has been dominated by both grasslands and forests over geologic time (Daubenmire 1978), so either ecosystem could be used as a historical reference system. In fact, temperate regions in North America and Africa have climates which support either grassland or woodland equally well (Olson et al. 2001, Sankaran et al. 2005). Yet, most restorationists aim to maintain grasslands in the Central U.S. because they are valued for their rarity (Brennan and Kuvlesky 2005, Hoekstra et al. 2005). 

Just as reference systems are used to increase the population sizes of animals, reference systems may be useful for improving the lives of animals, especially in cases where the goal is to incrementally make their lives better (i.e., shift them to a higher welfare-state). Critically, reference systems reduce uncertainty by demonstrating which dynamics are possible in nature as well as how those dynamics interact to create self-sustaining systems (reviewed by Jackson and Hobbs 2009 and Higgs et al. 2014). For example, reference ecosystems can indicate what the lives of animals were like before natural and anthropogenic changes occurred (Jackson and Hobbs 2009) and it is possible that, in some instances, animal welfare can improve by restoring ecosystems to the conditions under which they evolved (Beausoleil et al. 2016). Also, if there are several examples of the same type of reference ecosystem, a systematic review can illustrate the natural range of variability of responses to natural and anthropogenic events and provide a basis for the time-scale over which to expect a response to a similar intervention (Zedler and Callaway 1999, Jones 2010, Cosentino et al. 2014).

One way to apply reference systems to improve wild animal welfare is to: 1) study a range of historical or existing ecosystems, 2) assess which system provides animals with the best lives, and 3) use this system as a reference ecosystem to provide a basis to improve the lives of animals in other areas. The considerable knowledge that is contained in reference systems challenges the view put forth by some animal welfare advocates that uncertainty is an unsolvable problem. To my knowledge, these tools have never been applied to the problem of wild animal suffering even though they may reduce uncertainty in this context. 

It is possible that reference systems have not been applied to improve wild animal welfare because it remains unclear if reference systems for wild animals’ lives exist. Ecologists have traditionally focused on characteristics of populations, species, communities and ecosystems—such as trends in population size or community structure — as indicators of the responses of animals to anthropogenic and natural environmental changes (Cooke et al. 2013; e.g. Laidlaw et al. 2017 and Hof and Hjältén 2018). In contrast, researchers in animal welfare biology needs data on the life, death, behavior, and sources of stress in the lives of individual animals (e.g. Cooke and Sneddon 2001, Beausoleil et al. 2016). A review of the currently available data about individual animals may shed light on this knowledge gap and uncover information we can use to try to understand animals’ lives in nature.

Wild Animal Initiative is also undertaking a systematic review of conservation physiology to understand to what extent restoration ecology has targeted individual animals (following Cooke and Suski 2008). Physiology can reveal the specific mechanisms underlying conservation problems by identifying, for example, cause-and-effect relationships between environmental conditions and population declines or identifying the optimal range of habitats and stressor thresholds for different organisms (Cooke et al. 2013; e.g. Buxton et al. 2018, Tomlinson et al. 2018, Merrill et al. 2019). These physiological datasets may provide reference systems for us to understand how animals respond to different interventions at the level of the individual.

Adaptive management

Even if there are no adequate reference systems to draw baseline knowledge, we can still plan interventions to generate useful knowledge about how to improve the lives of animals using adaptive management strategies. Adaptive management is defined as an iterative decision-making process that 1) formulates management objectives, 2) implements actions to address these objectives, 3) monitors the results, and 4) adapts the intervention until the results are achieved (Walters and Holling 1990, Herrick et al. 2012; Figure 2).

Fig. 2: A conceptual map illustrating how adaptive management can advance prediction and certainty in restoration ecology. Once restoration goals are set, theory-based observations and experiments are implemented and tested with explanatory and forecasting models. New insights from this monitoring are used to inform the current restoration practices, to predict the outcomes of new restoration projects, and to revise pre-existing theories. Adapting theory leads to new ideas for observations and experiments in an iterative process. Reproduced from Brudvig 2017.

Fig. 2: A conceptual map illustrating how adaptive management can advance prediction and certainty in restoration ecology. Once restoration goals are set, theory-based observations and experiments are implemented and tested with explanatory and forecasting models. New insights from this monitoring are used to inform the current restoration practices, to predict the outcomes of new restoration projects, and to revise pre-existing theories. Adapting theory leads to new ideas for observations and experiments in an iterative process. Reproduced from Brudvig 2017.

Adaptive management is especially useful in situations where reference ecosystems do not exist, such as in novel ecosystems, which are defined as environments created by human activities and composed of new combinations of abiotic and biotic components (Hobbs et al. 2009). A city is one example of a novel ecosystem (Dearborn and Kark 2010). Because novel ecosystems have no natural analogues, recovery plans cannot be based on a historic reference system (Miller and Bestelmeyer 2016, Swartz 2018). However, they can still be incrementally improved through intervention projects designed under adaptive management principles.

Good adaptive management projects are designed as experiments in nature, because experiments show which interventions work, and why. Experimental design has three major components: 1) replication, managing multiple sites in the same way to estimate the variability in outcomes from a restoration; 2) repeatability, so others can recreate your work and also understand if any unique conditions have changed the outcome of the intervention; and 3) controls, where no intervention takes place, to disentangle the impact of the intervention from the effects of the background environment (Walters and Holling 1990, Brudvig et al. 2017). Ultimately, if interventions are designed as experiments in nature, then even failed interventions will generate knowledge to help animals in the future. Good adaptive management also acknowledges different sources of uncertainty and incorporates them into decision-making frameworks using structured-decision making, information-gap theory, looped learning, and other scenario-project tools (Figure 2; also reviewed by Regan et al. 2002, Halpern et al. 2006, Brudvig et al. 2017; for examples see Regan et al. 2005, Gerber et al. 2007, Hychka and Druschke 2017, Laidlaw et al. 2017, and Hof and Hjältén 2018). These tools further challenge the idea that uncertainty is an intractable problem.

The larger problem is that adaptive management is not always done in practice in ecological science because there is rarely sufficient funding for monitoring or adaptation (Bernhardt et al. 2007, Wortley et al. 2013, Hychka and Druschke 2017). By comparison, welfare biology is uniquely positioned to design every intervention to incorporate uncertainty into decision-making, monitor the effects of interventions, and adapt if we are not meeting our goals. With “challenging uncertainty” as a core value in welfare biology, I’m optimistic that our work will fill an important knowledge gap regarding how to cope with uncertainty in environmental sciences. This is a strength of the wild animal welfare movement, and a personal source of satisfaction for me as a researcher at Wild Animal Initiative.

Conclusion

Uncertainty is not an intractable problem because there are many tools that animal welfare advocates can adopt to mitigate uncertainty, even in cases where wild animal advocates and ecologists don’t share the same values. Reference ecosystems are one of the primary tools in restoration ecology for reducing uncertainty because they provide valuable baseline information that clarifies restoration objectives and demonstrates which dynamics occur in nature. To use reference systems to improve wild animal welfare, we could: 1) study a range of historical or existing ecosystems, 2) assess which system provides animals with the best lives, and 3) use that system as a reference ecosystem to provide a basis for improving the lives of animals in other areas. Another restoration tool which welfare biology can adopt when planning interventions is adaptive management, which emphasizes iterative learning through assessment, monitoring, and adaptation. Welfare biology can actually do better than conservation and restoration, as currently, ecological projects rarely have enough funding to monitor and adapt. With our strong focus on uncertainty, we can be sure to always design intervention projects with replication, repeatability, and controls, so that even failed interventions create knowledge to help animals in the future. Furthermore, we can contribute to the environmental sciences by illustrating how to incorporate uncertainty into all stages of projects by faithfully following best-practice standards in every project.

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