Instant death, slow death and the consequences of assumptions about prolonged dormancy for plant population dynamics

Abstract

Summary Many perennial plants experience prolonged dormancy, meaning they do not grow above‐ground for one or several growing seasons. When plants disappear (fail to sprout) and have not been recorded to re‐emerge, they either have died or are alive and dormant. In demographic studies of such species, researchers have been forced to make assumptions about death versus dormancy. Little is known about the consequences of these assumptions for predictions from the population models used in the studies. Here, we define survival of dormancy‐prone plants in three distinct ways: Separate, Instant and Slow death. In the Separate death model, plants are assumed to die either in emergent or in dormant stages. Death can also be described as a process of disappearance followed by dying, either in the first year below‐ground (Instant death) or with constant mortality rate in any given dormant year (Slow death). Using simulated data with known parameter values, we test whether survival and re‐emergence rates are confounded in these life‐history models. Using a general model and models for two orchid species (Epipactis atrorubens and Isotria medeoloides), we test how different assumptions about dormancy (Instant and Slow death) affect predictions about population dynamics: population growth rate, generation time, relative reproductive values, life expectancies, and sensitivity and elasticity of population growth rate to stage transitions and vital rates. Our results confirm that survival and re‐emergence rates of dormancy‐prone plant species are difficult to estimate; parameters were separable only with the assumptions of Instant and Slow death. Both theoretical and empirical analyses show that the predictions of population growth rate and generation time do not depend on the assumptions made about the fate of the plants after their disappearance. Synthesis. Assumptions about dormancy affect some, but not all predictions, about plant population dynamics. In the studies of species with unobservable stages, assumptions about dormancy should be carefully defined and possible consequences of these assumptions for the predictions of the population model should be evaluated. Nonetheless, if we are only interested in overall population viability, ad hoc models of prolonged dormancy are sufficient as a first step.

Comparing demography inferred from age vs. stage in a perennial plant

“…High juvenile survival occurred because many 2‐yr‐old plants were dormant and so hardly any mortality was assigned to 2‐yr olds (cf. Alahuhta et al 2017). Age‐specific fertility increased for both age‐ and age‐and‐stage‐based models, while, in the age‐from‐stage model, it stayed constant after age 3 (Appendix : Table S3).…”

“…Dormancy periods of this species usually last one or two years (Lesica and Crone 2007). If a plant was not seen on the third year after the last sprouting event, it was assumed to be dead, and we assumed that death occurred during the first year after the disappearance (instant death, sensu Alahuhta et al [2017]). Therefore, we excluded the data of the first three and the last two years to be able to separate re‐emergent plants from new recruits, and dormant and dead plants, respectively.…”

Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale

“…2014; Alahuhta et al . 2017). The novelty of our findings is in linking these trait‐climate patterns to life history strategies and demonstrating that shorter species characteristic of colder regions can exhibit slow life‐histories more usually associated with larger woody plants in warmer regions.…”

“…However, both height and seed size have associated resource costs (Chave et al 2009;Moles 2018), and in freezing environments woodiness and height are associated with loss of hydraulic function (Zanne et al 2014). Conversely, herbaceous species in extreme cold environments survive by senescing cheaper non-woody tissues, exhibiting long periods of below-ground dormancy, and/or avoiding extreme air temperatures beneath a snow layer (Zanne et al 2014;Alahuhta et al 2017). The novelty of our findings is in linking these trait-climate patterns to life history strategies and demonstrating that shorter species characteristic of colder regions can exhibit slow life-histories more usually associated with larger woody plants in warmer regions.…”

Using statistics to design and estimate vital rates in matrix population models for a perennial herb

“…As the statistical modeling is likely to be a more objective way for choosing categories than expert opinion, it might more accurately predict the long‐term population growth rate. However, this modeling approach is less frequently used in practice (but see e.g., Jäkäläniemi et al, , Gremer, Crone, & Lesica, , Alahuhta et al, ), and we are not aware of studies that have compared estimates of stochastic population dynamics between matrix models based on expert opinion and statistical modeling.…”