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The unprecedented rate of extinction calls for efficient use of genetics to help conserve biodiversity. Several recent genomic and simulation-based studies have argued that the field of conservation biology has placed too much focus on conserving genome-wide genetic variation, and that the field should instead focus on managing the subset of functional genetic variation that is thought to affect fitness. Here, we critically evaluate the feasibility and likely benefits of this approach in conservation. We find that population genetics theory and empirical results show that conserving genome-wide genetic variation is generally the best approach to prevent inbreeding depression and loss of adaptive potential from driving populations toward extinction. Focusing conservation efforts on presumably functional genetic variation will only be feasible occasionally, often misleading, and counterproductive when prioritized over genome-wide genetic variation. Given the increasing rate of habitat loss and other environmental changes, failure to recognize the detrimental effects of lost genome-wide genetic variation on long-term population viability will only worsen the biodiversity crisis.
Conservation translocation projects must carefully balance multiple, potentially competing objectives (e.g. population viability, retention of genetic diversity, delivery of key ecological services) against conflicting stakeholder values and severe time and cost constraints. Advanced decision support tools would facilitate identifying practical solutions.
We examined how to achieve compromise across competing objectives in conservation translocations via an examination of giant tortoises in the Galapagos Islands with ancestry from the extinct Floreana Island species (
Chelonoidis niger). Efforts have begun to populate Floreana Island with tortoises genetically similar to its historical inhabitants while balancing three potentially competing objectives – restoring ecosystem services (sustaining a high tortoise population size), maximizing genome representation of the extinct C. nigerspecies and maintaining a genetically diverse population – under realistic cost constraints.
We developed a novel approach to this conservation decision problem by coupling an individual‐based simulation model with generalized additive models and global optimization. We identified several incompatibilities among programme objectives, with quasi‐optimal single‐objective solutions (sets of management actions) differing substantially in programme duration, translocation age, incubation temperature (determinant of sex ratio) and the number of individuals directly translocated from the source population.
Quasi‐optimal single‐objective solutions were able to produce outcomes (i.e. population size and measures of genetic diversity and
C. nigergenome representation) to within 75% of their highest simulated outcomes (e.g. highest population size achieved across all simulations) within a cost constraint of c. $2m USD, but these solutions resulted in severe declines (up to 74% reduction) in outcomes for non‐focal objectives. However, when all programme objectives were equally weighted to produce a multi‐objective solution, all objectives were met to within 90% of the highest achievable mean values across all cost constraints. Synthesis and applications. Multi‐objective conservation translocations are likely to encounter complex trade‐offs and conflicts among programme objectives. Here, we developed a novel combination of modelling approaches to identify optimal management strategies. We found that solutions that simultaneously addressed multiple, competing objectives performed better than single‐objective solutions. Our model‐based decision support tool demonstrates that timely, cost‐effective solutions can be identified in cases where management objectives appear to be incompatible.