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A manifesto for predictive conservation
If efforts to tackle biodiversity loss and its impact on human wellbeing are to be successful, conservation must learn from other fields which use predictive methods to foresee shocks and pre-empt their impacts in the face of uncertainty, such as military studies, public health and finance. Despite...
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Published in: | Biological conservation 2019-09, Vol.237, p.12-18 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | If efforts to tackle biodiversity loss and its impact on human wellbeing are to be successful, conservation must learn from other fields which use predictive methods to foresee shocks and pre-empt their impacts in the face of uncertainty, such as military studies, public health and finance. Despite a long history of using predictive models to understand the dynamics of ecological systems and human disturbance, conservationists do not systematically apply predictive approaches when designing and implementing behavioural interventions. This is an important omission because human behaviour is the underlying cause of current widespread biodiversity loss. Here, we critically assess how predictive approaches can transform the way conservation scientists and practitioners plan for and implement social and behavioural change among people living with wildlife. Our manifesto for predictive conservation recognises that social-ecological systems are dynamic, uncertain and complex, and calls on conservationists to embrace the forward-thinking approach which effective conservation requires.
•Conservation practice relies on being able to understand human behaviour.•Predictive science can inform conservation decision-making and practice.•Effective use of prediction can anticipate problems before they arise.•Predictive approaches can be used to test interventions prior to implementation.•The impact of interventions can be evaluated against predicted performance. |
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ISSN: | 0006-3207 1873-2917 |
DOI: | 10.1016/j.biocon.2019.05.059 |