Indicators for monitoring and predicting conservation policy interventions

Problem statement

The fast pace at which humans are changing the environment endangers the biodiversity and ecosystem services on which we depend (1,2) Managing complex interactions to ensure nature thrives and continues to provide benefits to people requires integrative and interdisciplinary approaches to management that emphasise the complexities of whole social-ecological systems(3).

indicatorsEffective management of these systems involves measuring status and trends to inform management about the systems’ state as well as monitoring interventions’ successes and failures(4). Indicators that can be easily measured can act as simplified summaries of system condition and behaviour, utilising multiple or composite indicators to track and communicate complex systems(5). Indicators have been adopted for the Sustainable Development Goals and Aichi Targets to measure the progress of conservation policy interventions. However, the ability of such indicators to detect trends of interest, such as declines in threatened species or ecosystem function, remains poorly understood(6). Mechanistic models which include trophic interactions and human pressures (mostly marine, e.g. Atlantis, Ecopath with Ecosim, but also terrestrial, e.g. Madingley model) have been suggested as methods to evaluate and test biodiversity indicators.

Mechanistic models attempt to build an accurate description of the underlying processes within a system, accurately predicting an observed behaviour(7). In conservation, these models have been used to identify emergent behaviour patterns(8), make predictions under novel conditions (e.g. climate change)(9), and predict the outcome of human interventions at various scales(10,11). Their importance is increasing within the context of project design and planning, as they enable the assessment of various management interventions. 

As conservation becomes more accountable(12), design, monitoring and assessment are becoming standard steps in the planning process. Applying mechanistic models to predict outcomes is a powerful tool to improve project and policy design. However, there is lack of evidence for indicators that measure changes in a system and are suitable both for monitoring and predicting. The aim of this workshop is to propose a group of indicators and models that are suitable for both monitoring and predicting policy interventions. This set of indicators could potentially become standards in planning and monitoring policy interventions internationally, ultimately improving the effectiveness of global conservation efforts


At ICN 2018

During the workshop sessions for this specific theme, we will focus on a set of adopted indicators from the Sustainable Development indicatorsGoals and Aichi Targets, and we will identify indicators and methods for testing, as well as assessing gaps in representation and the costs and benefits of the assessment.

We aim at getting together a group of early career researchers with various backgrounds in order to produce an article, though the objective for the workshop is to have the layout for a manuscript and commitments from co-authors.

Some key research questions that will be addressed are the following:

  1. Which indicators could be tested, for both marine and terrestrial ecosystem?
  2. How can they be tested? Using which models?
  3. Where are subsequent gaps found in terms of representing biodiversity adequately? (across taxa, geography, function, scale)
  4. What are the costs and benefits of thorough indicator testing?
  5. What are the next steps?



  1. Millenium Ecosystem Assessment. Ecosystems and human well-being: a framework for assessment. (Island Press, 2005).
  2. Díaz, S., Fargione, J., Chapin, F. S. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biology 4, 1300–1305 (2006).
  3. Folke, C., Hahn, T., Olsson, P. & Norberg, J. Adaptive Governance of Social-Ecological Systems. Annu. Rev. Environ. Resour. 30, 441–473 (2005).
  4. Legg, C. J. & Nagy, L. Why most conservation monitoring is, but need not be, a waste of time. J. Environ. Manage. 78, 194–199 (2006).
  5. Dale, V. H. & Beyeler, S. C. Challenges in the development and use of ecological indicators. Ecol. Indic. 1, 3–10 (2001).
  6. Collen, B. & Nicholson, E. Taking the measure of change. Science (80-. ). 346, 166–167 (2014).
  7. Illius, A. w. Foraging and Population Dynamics. in Large Herbivore Ecology, Ecosystem Dynamics and Conservation (eds. Danell, K., Bergström, R., Duncan, P. & Pastor, J.) (Cambridge University Press, 2006).
  8. Harfoot, M. B. J. et al. Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model. PLOS Biol. 12, 1–24 (2014).
  9. Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Chang. 5, 215 (2015).
  10. Paillex, A., Reichert, P., Lorenz, A. W. & Schuwirth, N. Mechanistic modelling for predicting the effects of restoration, invasion and pollution on benthic macroinvertebrate communities in rivers. Freshw. Biol. 62, 1083–1093 (2017).
  11. Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).
  12. Ferraro, P. J. & Pattanayak, S. K. Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biology 4, 482–488 (2006).