Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Area of Science:

• Ecology
• Conservation Biology
• Biodiversity Science

Background:

• Biodiversity conservation relies on accurate species distribution data, but rare species pose significant sampling and modeling challenges.
• Global change exacerbates extinction risks for rare species, highlighting the need for improved predictive tools.
• Conventional species distribution models struggle with rare species due to limited occurrence data and spatial biases.

Purpose of the Study:

• To provide a guide for scientists and practitioners on predicting rare species distributions.
• To clarify how different types of species rarity influence data collection and modeling choices.
• To improve the assessment and prediction of rare species distributions by integrating survey design and modeling.

Main Methods:

• Summarizing how species rarity categories affect occurrence and distribution data based on survey design (spatial distribution and sampling protocols).
• Clarifying suitable species distribution models based on different data types.
• Highlighting insights from systematic species-targeted sampling and hierarchical models.

Main Results:

• Study designs must carefully integrate species sampling and modeling steps tailored to rarity types and data availability.
• Different rarity forms yield distinct occurrence and distribution data influenced by sampling strategies.
• Hierarchical models coupled with systematic sampling can correct for biases in rare species distribution data.

Conclusions:

• Effective prediction of rare species distribution requires a holistic approach, linking survey design to modeling techniques.
• Understanding the trade-offs between data quantity, quality, and model complexity is crucial for robust biodiversity assessments.
• This work offers a framework for navigating methodological developments to enhance rare species distribution predictions.