The accelerating global biodiversity crisis demands robust and adaptable tools to evaluate conservation effectiveness and track population trends, particularly for rare and threatened species. In Switzerland, where a significant proportion of native plant species are endangered, effective monitoring and analysis of population data is critical. This study evaluates the applicability of the TRends and Indices for Monitoring data (TRIM) method, originally developed for ornithological data, to the context of rare plant conservation. Using a combination of simulation-based analyses and empirical applications across 34 vascular plant species in French-speaking Switzerland, TRIM’s performance was assessed under varying data quality conditions.

Simulation results showed that TRIM produced reliable models only when datasets contained less than 70% missing data and fewer than 60% missing years, with monitoring periods spanning at least 11 years. However, the method showed reduced ability to identify significant declines and stable trends under incomplete conditions, possibly due to data structure rather than limitations inherent to the method. When applied to real-world datasets, a valid trend model could be produced for only 38% of the analysed species. These results highlight the current limitations of existing datasets, especially in terms of temporal continuity, standardisation, and completeness.

The findings emphasise that TRIM can be a valuable tool for analysing plant population trends, but only if monitoring protocols are adapted to its data requirements. Standardisation of abundance measures, long-term data collection, and regular monitoring of representative populations are essential. Without these conditions, trend analyses may be unreliable or misleading. This work therefore positions TRIM as a promising method for conservation of threatened plants, provided that its application is tailored to the specific challenges of plant data.