Spotlight
Thinking backwards can inform concerns about ‘incomplete’ data Margaret J. Grose Faculty of Architecture, Building and Planning, University of Melbourne, Grattan Street, Carlton, VIC 3010, Australia
Ecologists often feel that they need complete data before they are able to advise or make decisions. Thinking backwards, an idea from mathematics, suggests that we need to focus on the desired outcome to tell us which way to go for practical solutions for our ecological ambitions.
Dealing with data towards our ecological ambitions Recently, a researcher working with wastewater lamented a pressing need for more data before policies to save water can be designed [1]. This sense of a need for comprehensive data goes to the heart of scientific methods. Yet, having complete data may not be the most important issue for our ecological ambitions, which are surely focussed on solving problems, or on enabling decisions towards best ecological outcomes. Thinking about how better to cope with incomplete data is important because many of the most urgent ecological issues are in data-poor regions of the world. Importantly, hesitation in the face of perceived incomplete data is delaying management and leading to biodiversity loss [2]. For example, a coral reef biologist lamented to me that he had spent 8 years researching the details of a reef system but at the end of that time, one-sixth of the coral reefs in the world had been lost; the extensive data that he had collected appeared to give no solution to their survival. Thinking backwards Data collection can remain describing the problem (as my coral reef scientist found) rather than revealing or enabling a solution. We can get out of the problem-description phase by reversing the way we solve problems from data collection (where we hope to find solutions via patterns, distributions, and models) to one where we know the desired solution (which might be clean stormwater, elephant conservation, or increased biodiversity) and can work ‘backwards’ to see what variations on the components might achieve the outcome. This has been referred to as ‘thinking backwards’ and is a type of mathematical idea called an inverse problem [3]. Whereas normal problems have one solution, inverse problems have multiple solutions. Most ecological problems will have multiple answers. The multiple solutions of inverse problems fit with our understanding Corresponding author: Grose, M.J. ([email protected]). Keywords: incomplete data; inverse problems; speculation; noise. 0169-5347/ Crown Copyright ß 2014 Published by Elsevier Ltd. All rights reserved. http:// dx.doi.org/10.1016/j.tree.2014.07.007
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of ecosystems as being nonlinear, and unpredictable, and with historic and social dependencies. Inverse problems have a different emphasis from the recent discussions on data and the use of simple and complex models [4], which search for general patterns to answer questions such as ‘how might we improve elephant conservation?’, or of ‘backcasting’ to map out the feasibility of achieving an endpoint [5], or of optimal decisions and viability analysis in regard to uncertainty [6]. Inverse problems start off with the answer to a question, where the answer might be our ambitions for safer elephants. My experience of inverse problems is as a landscape architect and ecologist. Designers have to design with a desired answer in mind because the ‘answer’ (e.g., an ecologically functioning wetland) is known, but we have to work backwards to get what we want, despite incomplete data and a time deadline. Perhaps antithetically for ecologists, designers have to speculate in the absence of complete data; however, speculation means that designers deal with hesitancy in decision-making. Might thinking backwards assist ecologists to deal with hesitation, which might otherwise delay good ecological outcomes and management decisions? Getting away from the problem-description phase of a normal, linear problem accelerates the finding of solutions to data-poor ecological problems. This means better policies for environmental protection can be designed and made available before it is too late to enact protection. Thinking backwards might be particularly important in urban ecology, where clear ambitions, such as increased greening, arise in the complex setting of cities. Noise and data in the decision process The second component of inverse problems is identifying which data are important for achieving our ambitions. It is often difficult to ascertain what is and what is not important, and what types of information are important and what types are misleading or not greatly important, because they lie with noise in the system. In short, there can be a lot of noise amid the data that can distract us from the main ambition of perhaps clean wastewater, or elephant conservation, or greater urban biodiversity. Leonard Bernstein’s teaching philosophy was that the best way to know a thing is in the context of another discipline [7], and many new insights in science can be derived by posing a problem from one field to another. An analogy to understanding the noise in an ecological system can be drawn from Qantas Flight 32 from Singapore to Sydney in 2010 [8], when an explosion shattered an engine on a full Airbus 380, with shrapnel degrading or destroying vital systems. The pilot, Richard de Crespigny, ignored a
Spotlight barrage of computer error messages and action demands and focussed on his first instructor’s training admonition of ‘fly the plane’; the data barrage, although alarming, was largely about trivial systems or demands that ignored vital interconnections between the systems of the plane. De Crespigny filtered out messages that were important from those that were merely distractions from the ambition of keeping the plane flying and capable of landing. Although a plane is a human construct and biology is not and does not come with a handbook, we might still ask how we could work out what it takes to ‘fly the plane’ in ecological problems, so that we can arrive at our destination of making a sound ecological decision despite incomplete data or masses of competing data. Currently, ecologists working in population management can use elasticity analysis to provide information as to which life stages of an organism have the greatest impacts on a population, or on the predators of that population, and, thus, focus management efforts [9]. This has been likened to finding the Achilles’ heel of an organism [9] and ensuring that it is dealt with. In ecological problems, the data will include both scientific and cultural-social information, and may well include unquantifiable elements, such as local ecological knowledge. Absences of data or unquantifiable data might mean that we have to ‘fly blind’ and speculate, or risk being too late in our decision-making. Moving forwards despite incomplete data Importantly, worries about incomplete data might be misplaced if filtering out important influences from noise is the key to best outcomes; more data might just be noise. We do not know and will rarely know a system completely; such a thought might assist those who work in data-poor and scientifically poorly funded regions where information will always be lacking. In more data-rich regions, thinking backwards might enable us to use modern performative design ideas from the design professions to test biophysical impacts, such as in wetlands or modelling management strategies with the assistance of local government or community. The focus of research should be on our ability to name the ambition we have and to work on the uncertainties of noise amid the data [10] and the impacts of types of data (Box 1) upon our desired ambition. Such subtle shifts in emphasis away from the current focus on gaining complete species data might assist us in avoiding the failure of programs due to delays based on perceived absences of data [11]. A focus on filtering out
Trends in Ecology & Evolution October 2014, Vol. 29, No. 10
Box 1. A qualitative approach to thinking backwards about data and noise Decisions and speculation thinking backwards from a desired ecological outcome is made complex by important data, senses of incomplete data, and noise in the data. As a general approach to deciding what new data are likely to be important to collect and what data might remain ‘incomplete’, data might be filtered into locally dependent categories of: (i) robust (resilient to change); (ii) fulcrum (pivotal, changeable); (iii) scaffolding (impacts others via cascades); (iv) the game-changer; (v) dependents (on others); (vi) independents (uniques); (vii) externals (out of local control); (viii) outliers (possibly loud but distractions); (ix) dormants (arise if new conditions); and (x) camouflaged (friends or assassins).
noise might also assist in considering interventions aimed at species ecosystem structure and function, and enable us to address the identification of triggering points within conservation monitoring programmes [2]. Acknowledgements The author thanks Peter Hall FRS for discussion of inverse problems, Andre´ Stephan for a discussion on an earlier draft, referees, and editorial suggestions.
References 1 Cisneros, B.J. (2013) The data gap. Nature 502, 633–634 2 Lindenmayer, D.B. et al. (2013) Counting the books while the library burns: why conservation monitoring programs need a plan for action. Front. Ecol. Environ. 11, 549–555 3 Hadamard, J. (1902) Sur les proble`mes aux derivees partielles et leur signification physique. Bull. Univ. Princeton 13, 49–52 4 Evans, M.R. et al. (2013) Do simple models lead to generality in ecology? Trends Ecol. Evol. 28, 578–583 5 Manning, A.D. et al. (2006) Stretch goals and backcasting: approaches for overcoming barriers to large-scale ecological restoration. Restoration Ecol. 14, 487–492 6 Drechsler, M. and Burgman, M.A. (2004) Combining population viability analysis with decision analysis. Biodiv Conserv. 13, 115–139 7 Bernstein, L. (1973) The Unanswered Question: Six Talks at Harvard, West Long Branch 8 De Crespigny, R. (2012) QF32, Macmillan 9 Benton, T.G. and Grant, A. (1999) Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol. Evol. 14, 467–471 10 Tarantola, A. (2006) Popper, Bayes and the inverse problem. Nat. Phys. 2, 492–494 11 Martin, T.G. et al. (2012) Acting fast helps avoid extinction. Conserv. Lett. 5, 274–280
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Thinking backwards can inform concerns about ‘incomplete’ data Margaret J. Grose Faculty of Architecture, Building and Planning, University of Melbourne, Grattan Street, Carlton, VIC 3010, Australia
Ecologists often feel that they need complete data before they are able to advise or make decisions. Thinking backwards, an idea from mathematics, suggests that we need to focus on the desired outcome to tell us which way to go for practical solutions for our ecological ambitions.
Dealing with data towards our ecological ambitions Recently, a researcher working with wastewater lamented a pressing need for more data before policies to save water can be designed [1]. This sense of a need for comprehensive data goes to the heart of scientific methods. Yet, having complete data may not be the most important issue for our ecological ambitions, which are surely focussed on solving problems, or on enabling decisions towards best ecological outcomes. Thinking about how better to cope with incomplete data is important because many of the most urgent ecological issues are in data-poor regions of the world. Importantly, hesitation in the face of perceived incomplete data is delaying management and leading to biodiversity loss [2]. For example, a coral reef biologist lamented to me that he had spent 8 years researching the details of a reef system but at the end of that time, one-sixth of the coral reefs in the world had been lost; the extensive data that he had collected appeared to give no solution to their survival. Thinking backwards Data collection can remain describing the problem (as my coral reef scientist found) rather than revealing or enabling a solution. We can get out of the problem-description phase by reversing the way we solve problems from data collection (where we hope to find solutions via patterns, distributions, and models) to one where we know the desired solution (which might be clean stormwater, elephant conservation, or increased biodiversity) and can work ‘backwards’ to see what variations on the components might achieve the outcome. This has been referred to as ‘thinking backwards’ and is a type of mathematical idea called an inverse problem [3]. Whereas normal problems have one solution, inverse problems have multiple solutions. Most ecological problems will have multiple answers. The multiple solutions of inverse problems fit with our understanding Corresponding author: Grose, M.J. ([email protected]). Keywords: incomplete data; inverse problems; speculation; noise. 0169-5347/ Crown Copyright ß 2014 Published by Elsevier Ltd. All rights reserved. http:// dx.doi.org/10.1016/j.tree.2014.07.007
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Trends in Ecology & Evolution, October 2014, Vol. 29, No. 10
of ecosystems as being nonlinear, and unpredictable, and with historic and social dependencies. Inverse problems have a different emphasis from the recent discussions on data and the use of simple and complex models [4], which search for general patterns to answer questions such as ‘how might we improve elephant conservation?’, or of ‘backcasting’ to map out the feasibility of achieving an endpoint [5], or of optimal decisions and viability analysis in regard to uncertainty [6]. Inverse problems start off with the answer to a question, where the answer might be our ambitions for safer elephants. My experience of inverse problems is as a landscape architect and ecologist. Designers have to design with a desired answer in mind because the ‘answer’ (e.g., an ecologically functioning wetland) is known, but we have to work backwards to get what we want, despite incomplete data and a time deadline. Perhaps antithetically for ecologists, designers have to speculate in the absence of complete data; however, speculation means that designers deal with hesitancy in decision-making. Might thinking backwards assist ecologists to deal with hesitation, which might otherwise delay good ecological outcomes and management decisions? Getting away from the problem-description phase of a normal, linear problem accelerates the finding of solutions to data-poor ecological problems. This means better policies for environmental protection can be designed and made available before it is too late to enact protection. Thinking backwards might be particularly important in urban ecology, where clear ambitions, such as increased greening, arise in the complex setting of cities. Noise and data in the decision process The second component of inverse problems is identifying which data are important for achieving our ambitions. It is often difficult to ascertain what is and what is not important, and what types of information are important and what types are misleading or not greatly important, because they lie with noise in the system. In short, there can be a lot of noise amid the data that can distract us from the main ambition of perhaps clean wastewater, or elephant conservation, or greater urban biodiversity. Leonard Bernstein’s teaching philosophy was that the best way to know a thing is in the context of another discipline [7], and many new insights in science can be derived by posing a problem from one field to another. An analogy to understanding the noise in an ecological system can be drawn from Qantas Flight 32 from Singapore to Sydney in 2010 [8], when an explosion shattered an engine on a full Airbus 380, with shrapnel degrading or destroying vital systems. The pilot, Richard de Crespigny, ignored a
Spotlight barrage of computer error messages and action demands and focussed on his first instructor’s training admonition of ‘fly the plane’; the data barrage, although alarming, was largely about trivial systems or demands that ignored vital interconnections between the systems of the plane. De Crespigny filtered out messages that were important from those that were merely distractions from the ambition of keeping the plane flying and capable of landing. Although a plane is a human construct and biology is not and does not come with a handbook, we might still ask how we could work out what it takes to ‘fly the plane’ in ecological problems, so that we can arrive at our destination of making a sound ecological decision despite incomplete data or masses of competing data. Currently, ecologists working in population management can use elasticity analysis to provide information as to which life stages of an organism have the greatest impacts on a population, or on the predators of that population, and, thus, focus management efforts [9]. This has been likened to finding the Achilles’ heel of an organism [9] and ensuring that it is dealt with. In ecological problems, the data will include both scientific and cultural-social information, and may well include unquantifiable elements, such as local ecological knowledge. Absences of data or unquantifiable data might mean that we have to ‘fly blind’ and speculate, or risk being too late in our decision-making. Moving forwards despite incomplete data Importantly, worries about incomplete data might be misplaced if filtering out important influences from noise is the key to best outcomes; more data might just be noise. We do not know and will rarely know a system completely; such a thought might assist those who work in data-poor and scientifically poorly funded regions where information will always be lacking. In more data-rich regions, thinking backwards might enable us to use modern performative design ideas from the design professions to test biophysical impacts, such as in wetlands or modelling management strategies with the assistance of local government or community. The focus of research should be on our ability to name the ambition we have and to work on the uncertainties of noise amid the data [10] and the impacts of types of data (Box 1) upon our desired ambition. Such subtle shifts in emphasis away from the current focus on gaining complete species data might assist us in avoiding the failure of programs due to delays based on perceived absences of data [11]. A focus on filtering out
Trends in Ecology & Evolution October 2014, Vol. 29, No. 10
Box 1. A qualitative approach to thinking backwards about data and noise Decisions and speculation thinking backwards from a desired ecological outcome is made complex by important data, senses of incomplete data, and noise in the data. As a general approach to deciding what new data are likely to be important to collect and what data might remain ‘incomplete’, data might be filtered into locally dependent categories of: (i) robust (resilient to change); (ii) fulcrum (pivotal, changeable); (iii) scaffolding (impacts others via cascades); (iv) the game-changer; (v) dependents (on others); (vi) independents (uniques); (vii) externals (out of local control); (viii) outliers (possibly loud but distractions); (ix) dormants (arise if new conditions); and (x) camouflaged (friends or assassins).
noise might also assist in considering interventions aimed at species ecosystem structure and function, and enable us to address the identification of triggering points within conservation monitoring programmes [2]. Acknowledgements The author thanks Peter Hall FRS for discussion of inverse problems, Andre´ Stephan for a discussion on an earlier draft, referees, and editorial suggestions.
References 1 Cisneros, B.J. (2013) The data gap. Nature 502, 633–634 2 Lindenmayer, D.B. et al. (2013) Counting the books while the library burns: why conservation monitoring programs need a plan for action. Front. Ecol. Environ. 11, 549–555 3 Hadamard, J. (1902) Sur les proble`mes aux derivees partielles et leur signification physique. Bull. Univ. Princeton 13, 49–52 4 Evans, M.R. et al. (2013) Do simple models lead to generality in ecology? Trends Ecol. Evol. 28, 578–583 5 Manning, A.D. et al. (2006) Stretch goals and backcasting: approaches for overcoming barriers to large-scale ecological restoration. Restoration Ecol. 14, 487–492 6 Drechsler, M. and Burgman, M.A. (2004) Combining population viability analysis with decision analysis. Biodiv Conserv. 13, 115–139 7 Bernstein, L. (1973) The Unanswered Question: Six Talks at Harvard, West Long Branch 8 De Crespigny, R. (2012) QF32, Macmillan 9 Benton, T.G. and Grant, A. (1999) Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol. Evol. 14, 467–471 10 Tarantola, A. (2006) Popper, Bayes and the inverse problem. Nat. Phys. 2, 492–494 11 Martin, T.G. et al. (2012) Acting fast helps avoid extinction. Conserv. Lett. 5, 274–280
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