Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models

Area of Science:

• Community ecology
• Ecological statistics
• Biodiversity monitoring

Background:

• Quantifying temporal trends in species assemblages is crucial for understanding ecological dynamics.
• Existing methods often fail to account for incomplete sampling and imperfect species detection.

Purpose of the Study:

• To develop and demonstrate novel analytical methods for quantifying temporal trends in species abundance.
• To address the challenges of incomplete sampling and imperfect detection in ecological datasets.

Main Methods:

• Developed a bootstrapping procedure to test for non-random temporal trends in species abundance.
• Created a hierarchical model to estimate species-specific abundance trends and detection probabilities.
• Applied methods to long-term datasets of stream fishes and grassland insects.

Main Results:

• Bootstrap tests indicated significantly heterogeneous temporal trends in both fish and insect assemblages.
• The hierarchical model identified species with increasing, decreasing, or stable abundance over a decade.
• Demonstrated the capability of the methods to handle undetected species.

Conclusions:

• The developed methods provide a robust framework for analyzing temporal changes in ecological communities.
• These analytical advancements are broadly applicable to various ecological datasets.
• The findings highlight the importance of accounting for detection probability in trend analysis.