Treating knowledge as a conservation asset to resolve present-future biodiversity trade-offs.

Area of Science:

• Ecology
• Conservation Biology
• Evolutionary Biology

Background:

• Conservation planning faces resource limitations and uncertainty in species interactions.
• A key challenge is balancing phylogenetic diversity (PD) for evolutionary potential and functional diversity (FD) for ecosystem function.
• PD and FD are often weakly correlated, complicating fixed prioritization strategies.

Purpose of the Study:

• To develop a dynamic conservation allocation framework that adapts biodiversity proxies to the level of ecological knowledge.
• To determine whether adaptive strategies outperform fixed-weight approaches under varying levels of interaction certainty.
• To assess the impact of adaptive versus fixed prioritization on post-disturbance ecosystem diversity.

Main Methods:

• Developed a dynamic allocation framework where conservation objectives are fixed, but biodiversity proxies (PD or FD) adapt to knowledge.
• Utilized a 148-year Northeast Atlantic fish stomach time series for empirical evaluation.
• Employed simulations on synthetic food webs to test the framework across diverse ecological scenarios.

Main Results:

• The adaptive strategy consistently yielded higher post-disturbance diversity compared to fixed-weight PD-FD strategies and no-intervention baselines.
• PD-based rankings proved more robust under limited interaction knowledge.
• FD-based rankings became increasingly reliable as ecological interaction knowledge accumulated.

Conclusions:

• The trade-off between PD and FD in conservation prioritization should be dynamically adjusted based on the current level of ecological knowledge.
• Adaptive conservation strategies enhance ecosystem resilience and recovery following disturbances.
• Integrating knowledge acquisition into conservation planning is crucial for optimizing resource allocation and biodiversity outcomes.