Enabling wider use of mechanistic models for biodiversity forecasts

预测物种分布具有挑战性，但也是必要的。通常使用的基于模式的模型容易出错。最适合这项任务的机械模型因缺乏数据而受到限制。该项目旨在通过开发处理不完整性状数据和不确定性的新方法，使机械模型得到更广泛的使用。它期望产生关于物种特征如何定义它们生存的环境的新知识。预期成果包括提高能力，将机械模型应用于保护问题，沟通不确定性的方法和模型，为几十个物种的直接保护利益。这将有助于更可靠的生物多样性预测，支持更好的决策。

Forecasting species distributions is challenging yet necessary. The pattern-based models commonly used are error-prone. Mechanistic models, best equipped for the task, are limited by lack of data. This project aims to enable wider use of mechanistic models by developing new methods for dealing with incomplete trait data and uncertainty. It expects to generate new knowledge about how species’ traits define the environments in which they persist. Anticipated outcomes include enhanced capacity to apply mechanistic models to conservation problems, methods for communicating uncertainties and models for tens of species of immediate conservation interest. This will enable more reliable biodiversity forecasts, supporting better decision-making.