Castilleja tenuiflora is a facultative root hemiparasitic plant that has colonized a disturbed lava field in central Mexico. To determine the effects of hemiparasitism on the population dynamics of the parasite, we identified a set of potential hosts and quantified their effects on the vital rates of C. tenuiflora during 2016–2018. Connections between the roots of the hemiparasite and the hosts were confirmed with a scanning electron microscope. Annual matrices considering two conditions (with and without potential hosts) were built based on vital rates for each year, and annual stochastic finite rate growth rates (λs) were calculated. Plants produced more reproductive structures with hosts than without hosts. A Life Table Response Experiment (LTRE) was performed to compare the contributions of vital rates between conditions. We identified 19 species of potential hosts for this generalist hemiparasite. Stochastic lambda with hosts λs = 1.02 (CI = 0.9999, 1.1) tended to be higher than without them λs = 0.9503 (CI = 0.9055, 0.9981). The highest elasticity values correspond to survival. LTRE indicated that the most important parameters are survival and fecundity; the total contribution of fecundity (0.0192) to the difference in growth was three times lower than that of survival (0.0603). Piqueria trinervia was the most abundant host, and C. tenuiflora had a higher lambda with it than with other species. Individuals can grow alone, but hosts can have a positive effect on the vital parameters of C. tenuiflora and on λ.

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Detection probability (p) in plants is imperfect in natural conditions due to several factors. This imperfect detectability is rarely accounted for in the estimation of demographic parameters, such as survival probabilities (S) or transition rates between different life states or size classes (ψ), which may result in inaccurate quantitative information about plant populations. In this study, we used previously collected data of five plant species belonging to different families with contrasting life forms and habitats (Flaveria chlorifolia, Mammillaria hernandezii, Neobuxbaumia macrocephala, Govenia lagenophora, and Castilleja tenuiflora), data simulations, multi‐state models (a demographic tool that explicitly accounts for p), and direct estimation of survival and transition rates (i.e., assuming perfect detection) to identify in which species, states, or demographic parameters the bias caused by ignoring our imperfect detectability is more severe. Detection was imperfect (p < 1) for all our study species. In general, ignoring detection probabilities yielded underestimated survival and transition rates in all five species. Biases caused by assuming perfect detection were also large and significant, mainly in inconspicuous life states and size classes, such as seedlings and dry individuals. In contrast, considering detection probabilities resulted in fewer underestimated survival and transition rates, with smaller and mostly nonsignificant biases. Intriguingly, some transitions were overestimated even when accounting for detection probabilities. Our findings highlight the importance of considering that detection of most plant species is imperfect in the field, even in species that are apparently conspicuous, to avoid incorrect inferences about plant populations.

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