Human activity is dramatically shaping all of Earth's natural systems, producing unprecedented challenges for people and nature. Climate disruption, altered hydrology, and ecosystem degradation reflect both threats to human wellbeing and changes in the ‘rules of the game’ that make management difficult. While ecologists, conservationists and environmental scientists clamor for radical action to reverse these threats, their own management actions in response to climate are too often business as usual. I hypothesize that restrictive and often unspoken mental models of ecological and environmental science are robbing these managers and their institutions of the flexibility required to respond to the Anthropocene's uncertain changes. The three most profound mental traps are: (1) an undue emphasis on historical reference points; (2) an ecological concept of resilience that fails to reckon with the Anthropocene's dynamism; and (3) a precautionary bias against new technologies and dramatic interventions. Caught in these mental traps, environmentalists too often reject entrepreneurial experimental approaches that could make them more relevant to policymakers, corporations and other institutions that seek to respond more proactively to impending disruption.
All resource management objectives or targets that place a heavy reliance on maintaining historical conditions should be re-evaluated in light of climate disruption and other directional environmental trends.
Management mandates should have clear guidelines on triage so that resources are not expended in efforts that are made futile by massive anthropogenic change.
The precautionary principle should be rethought in light of our inability to guarantee the safety of any new technology, but the need for the benefits of new technology.
NGOs and government agencies should consider interventions that proactively assist change and assist biological evolution.
Because uncertainty about change is huge, and idiosyncratic to local contingencies, regulatory frameworks or government incentives must find ways to allow flexible responses, and learn through networks of responders as opposed to following top-down recipes.