Informing spatial conservation prioritization with species’ traits.

New Guinea, the most botanically diverse island on the planet, is the location for one of the boldest conservation initiatives. The Manokwari Declaration aims to achieve 70% conservation designation for the Bird's Head Peninsula. This is 40% higher than the 2022 Global Biodiversity Framework target. However, there is a lack of species occurrence data to support evidence of where biodiversity can be best protected. To address this, we integrated plant trait data from taxonomic descriptions in species occurrence models that can inform conservation planning. Inclusion of traits improved the performance of co‐occurrence models of ∼800 plant species across the 100,000‐km landscape. Traits generally improved model performance, but not all traits contributed equally (e.g., leaf size and red flower color most improved accuracy of occurrence prediction). Likewise, trait‐parameterized models tended to be most useful with rare species occurrence prediction, but this was inconsistent among traits. Under 70% protection, three‐quarters of the areas selected conserved trait diversity. Critically, trait diversity also increased the chances that areas at high risk of deforestation were selected as conservation priorities. Overall, we found that plant species’ traits, often key parameters of ecosystem function and resilience, improved spatial conservation planning. 新几内亚作为地球上植物多样性最丰富的岛屿, 正在实施一项最具雄心的保护计划。马诺夸里宣言旨在将鸟头半岛(Bird's Head Peninsula)的保护区比例提升至70%, 这一目标比《2022年全球生物多样性框架》目标高出40%。然而, 目前尚缺乏物种分布数据, 来指导生物多样性保护最适宜的选址。为此, 我们将植物分类学描述中的性状数据整合到物种分布模型中, 从而为保护规划提供依据。在10万平方公里的景观中, 纳入性状数据提升了约800种植物的共现模型的预测表现。性状数据普遍能够提升模型表现, 但贡献程度存在差异, 例如, 叶片大小与红色花朵特征对分布预测准确性的提升最为显著。类似地, 性状参数化模型在稀有物种分布预测中效果最佳, 但不同性状间的表现存在差异。在70%的保护比例下, 所选区域中有四分之三保护了性状多样性。关键的是, 纳入性状多样性还提高了高砍伐风险区域被选为优先保护区的概率。总体而言, 我们发现植物物种的性状(通常是生态系统功能与恢复力的关键参数)有助于优化空间保护规划。