- Open Access
Developing accurate prediction systems for the terrestrial environment
© Lindenmayer et al. 2018
Published: 18 April 2018
In recent decades, meteorologists have made remarkable progress in predicting the weather, thereby saving lives and considerable sums of money. However, we are way behind when it comes to predicting the effects of environmental change on ecosystems, even when we are ourselves the agent of such change. Given the substantial environmental problems facing our living planet, and the need to tackle these in an ecologically responsible and cost-effective way, we should aspire to develop terrestrial environmental prediction systems that reach the levels of accuracy and precision which characterize weather prediction systems. I argue here that well designed, long-term monitoring programs will be key to developing robust environmental prediction systems.
Environmental prediction in terrestrial ecosystems is a tough challenge. No two parts of a landscape are the same and many factors drive, for example, the distribution and abundance of key elements of the biota and how they respond to a changing environment, especially changes resulting from human disturbance. Almost 60 years ago, Eugene Odum argued that because aquatic, marine and atmospheric environments are dominated by physical processes, it is far easier to develop prediction systems for them than for terrestrial ecosystems, which are dominated by biological processes . Nevertheless, there is increasing public expectation that the large sums of money spent on the environment will deliver the desired objectives, and this necessitates being able to accurately forecast what will happen in the future - for example what will occur when particular kinds of land management are adopted in projects aimed at achieving the widespread landscape restoration of formerly degraded forested land.
A key consideration is the design of monitoring and data collection programs. I have been involved in a recent (currently unpublished) study of population trends in Australian birds which found that, for an array of reasons, over 90% of datasets collected by agencies and volunteers were unusable. In fact, their inherent problems would likely make prediction worse, not better, because of the massive amount of “noise” which can obscure key signals in long-term species population trends. The lesson here then is that more data is not better data and big data is not a substitute for quality data.
Of course, there are always uncertainties in any predictions, including those shown in Fig. 1, although the relatively tight prediction intervals in projected changes in tree populations (in the absence of further fire or ongoing logging) suggest that alternative trajectories to the catastrophic future declines are highly unlikely. In fact, while there will likely always be ecological surprises in many ecosystems worldwide, they are far more likely to be detected early (when there are opportunities to do something about them) in those environments that have been subject to rigorously designed long-term monitoring and research . In other cases, projections may be inaccurate, but these too offer an opportunity for learning and identifying ways to make better predictions in the future. Many scientists consider that more and better lessons are learned from failures rather than successes in ecological and environmental science .
The value of long-term monitoring and research has been established in numerous studies over the past 30 years. For example, long-term studies tend to be more highly cited than shorter investigations . Despite the exceptional value of long-term work, monitoring is widely regarded as a “Cinderella” science—it is largely overlooked, the last thing funded, and the first thing cut when budgets are tight . Are there ways to secure long-term monitoring and research? One key way is for researchers to highlight that the data they are gathering from long-term monitoring and research have value for environmental prediction, as in the case of the example in Fig. 1. Another is to re-use the data in other practical and applied ways. For example, the data that underpin Fig. 1, as well as data from other allied monitoring programs in the Mountain Ash ecosystem, were used to develop economic and environmental accounts that highlight the economic value of a suite of natural assets, including water, carbon, timber, tourism, and biodiversity . Such accounts are best populated with robust long-term monitoring data. The key point is that demonstrating the manifold practical uses of monitoring will more likely help convince funders that it is important to maintain ongoing financial support for these programs.
Significant improvements in prediction systems will require far more terrestrial environments to be the target of robust, well designed, and long-persisting long-term monitoring and research programs. One way to do this is to ensure that monitoring is a fundamental part of all major environmental programs. Many past programs around the world, including those in which billions of dollars have been spent, have not been monitored or monitored very poorly (ranging from river and land restoration programs to agri-environment schemes ). Vitally important data have not been gathered and important insights and opportunities have been lost for environmental learning and for building environmental prediction systems. Demanding that all major environmental programs include robust, well designed long-term monitoring would provide the opportunity to gather the data that are fundamentally important to developing the environmental prediction systems so desperately needed for better tackling the numerous problems facing the world’s terrestrial ecosystems.
This paper was originally presented in a highly shortened form at the World Economic Forum at Davos, Switzerland, in January 2018.
The author wrote, read, and approved the final manuscript.
The author declares that he has no competing interests.
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