Demography beyond a dissertation: profiting from well replicated and long term data

Although there are thousands of studies of population dynamics (Crone et al. 2011, Merow et al. 2014), most occur at small spatial scales and span few years (Menges 2000, Crone et al. 2011, Salguero-Gómez et al. 2015). We are persuaded that limited spatial replication and short study intervals can hinder our ability to adequately understand and predict population dynamics. In a recent publication (Quintana-Ascencio et al. accepted) we used an unusually detailed, spatially expansive, long-term dataset to unravel complex interactions between landscape patterns and ecological disturbances affecting a species’ distribution and demography. We assessed the effects of landscape factors on the population dynamics of Hypericum cumulicola, a pyrogenic, endangered Florida scrub endemic plant species. We took advantage of an ongoing 22-year study, that has monitored many populations (sampled in 15 independent habitat patches) with a rich disturbance history (14 independent fires), well-described habitat requirements (for open gaps in Florida scrub dominated by Ceratiola ericoides) and a strong spatially patterned landscape.

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We use of a long-term dataset to decouple effects of disturbance from population and year effects. The effects of disturbance regimes on population dynamics can be challenging to study because of the large amount of longitudinal data required. Long-term studies may be necessary to avoid misleading conclusions built on chronosequence-based short-term studies. Even when such long-term data are available, decoupling the effects of time since disturbance from year and population effects (e.g. climate, biotic interactions) requires datasets with replication across space and time.

We have shown before that H. cumulicola vital rates were strongly related to fire, the predominant ecological disturbance. Yet, in the reference study, we provide further evidence that vital rates were also affected by small scale landscape patterns such as elevation that alter the distance to the water table and larger scale patterns of patches in the landscape such as patch aggregation (e.g. survival was higher in large aggregated patches).

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Overall, our results indicated the importance of metapopulation dynamics. H. cumulicola occupancy and population growth predictions were highest in large, aggregated habitat patches. Large habitat patches may have several advantages for metapopulation dynamics, including a larger target for rescues, greater diversity of microhabitats, and the ability to support larger populations less prone to demographic or environmental stochasticity (Hanski, Moilanen & Gyllenberg 1996). Larger habitat patches may also be more likely to support larger populations of the dominant shrub Florida rosemary, which may act as a foundation species for H. cumulicola and other herbaceous plants that are vulnerable to competition from resprouting shrubs. Model predictions of abundance were most reliable for patches with the extremes of patch area or aggregation. In this study, predictions of occupancy and abundance were frailer for habitat patches with small and intermediate size and aggregation. We have previously documented that unoccupied patches can be suitable habitat for H. cumulicola. These results indicate that limited dispersal and unfavorable matrix habitats can synergistically contribute to colonization failure. Few studies have integrated population models and landscape level environmental drivers to characterize species distributions, although this approach has great promise for assessing the consequences of environmental changes

The distribution of species across landscapes ultimately reflects the interaction of demography with landscape and disturbance properties. However, both demographic inertia (e.g., long life span, dormant stages) and landscape history (e.g. environmental legacies) may create lags in responses. Therefore, realized species distributions (reflecting past interactions) may have different patterns than current vital rates. We used this detailed long-term monitoring to assess the relationships between demography and landscape-level drivers to disentangle the potential causes of realized species distributions. H. cumulicola occupancy peaked at higher elevations in larger patches but many vital rates peaked at lower elevations. This may reflect lags in demography such as the role of seed banks in allowing populations to persist between disturbances. Better understanding of the spatial and temporal dynamics of seed dormancy and dispersal and the role of environmental factors on their variation will greatly benefit our understanding of regional plant population persistence. In addition, landscape changes in extreme microsites (e.g. the largest open patches) may lag behind other landscape patterns. Short-term or limited studies in areas with these persisting patches may be biased, as patches where all plants have died cannot be a source of data. This demographic ghost of mortality past may explain unexpected demographic patterns in chronosequence studies.

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Many critiques of demographic modeling have pointed out that models do a poor job of predicting beyond more than a few years (Crone 2013). By incorporating common drivers such as weather, habitat suitability, disturbances, landscape patterns, and land management, we can improve our understanding of temporal and spatial variability in demography (Ehrlén & Morris 2015). Weaknesses of population models can be attributed to their frequent dependence on short spans of data (Menges 2000) and on their frequent blindness to underlying drivers (Ehrlén & Morris 2015). The scaling up of local demography to landscapes, accomplished by a combination of approaches, can lead to more effective management for ecological diversity.

Pedro F Quintana Ascencio and Eric S Menges




Crone, E.E., et al. (2011) How do plant ecologist use matrix population models? Ecology Letters, 14, 1-8.

Ehrlén, J. & Morris, W.F. (2015) Prediction changes in the distribution and abundance of species under environmental change. Ecology Letters, 18, 303-314.

Hanski, I., Moilanen, A. & Gyllenberg, M. (1996) Minimum viable metapopulation size. American Naturalist, 147, 527-541.

Menges, E.S. (2000) Population viability analyses in plants: challenges and opportunities. Trends in Ecology and Evolution 15, 51-56.

Merow, C. et al. (2014) Advancing population ecology with integral projection models: a practical guide. Methods in Ecology and Evolution 5, 99-110.

Quintana-Ascencio, P.F., Koontz, S., Smith, S. Sclater, V. David, A., Menges, E. S. (2018) Predicting landscape-level distribution and abundance: Integrating demography, fire, elevation, and landscape habitat configuration. Journal of Ecology, Accepted manuscript online: 2 APR 2018 12:00AM EST | DOI: 10.1111/1365-2745.12985

Salguero-Gómez, et al. (2015) The COMPADRE plant matrix database: an open online repository for plant demography. Journal of Ecology 103, 202-218.








Attacking the question of vegetative dormancy with COMPADRE

Vegetative dormancy is the tendency that some herbaceous plants have to forego sprouting for one or more years at a stretch. It is a remarkably common condition among the terrestrial plants, having been found in over 20 plant families, and over 100 plant species. The real number is likely to be far higher, because our knowledge of its extent is limited by the availability of demographic studies documenting it. The mechanisms responsible are also not fully understood, and likely vary across plant species.

The first studies to document dormancy that I am aware of go back at least to the 1940s, with work by Carl Olaf Tamm (Tamm, 1948). Carl was developing demographic studies of many European perennials and often noted that some individuals seemed to “disappear” for varying numbers of years from the aboveground population. In some cases, these absences were explained in terms of high levels of herbivory, while in others, no explanation was evident, except that the plants just did not seem to sprout. Intriguingly, these absences were most often noted in orchids, as in Carl’s work and later work by others (Hutchings, 1987; Tamm, 1956; Wells, 1967, 1981). This pattern led John Harper to note in his famous plant ecology textbook: “An odd feature of the depletion curves for the orchids is that the number of survivors appears to go up as well as down! Clearly the number of survivors can never increase. The explanation is that the orchids appear to be capable of disappearing from the above-ground population for a year, or perhaps two. …It may be that this habit is more common than we know” (Harper, 1977).



The yellow lady’s slipper orchid (Cypripedium calceolus), which grows throughout Eurasia, is a strongly dormancy-prone species of conservation concern.

Visions of undead plants aside, this particular quote by Harper hints at the crux of the issue with vegetative dormancy: just as the dead cannot go back to life, the dormant also cannot photosynthesize and (sexually) reproduce. Yet, there is a tendency to think of plants as lovely, green things that produce pretty flowers… except of course for those plants that cause us allergies and other inconveniences. Further, without reproduction, isn’t dormancy a time when evolutionary fitness drops (i.e. no reproduction!)? Dormancy therefore tests our own common sense, both because it forces us to acknowledge that plants are not necessarily organisms that need to produce leaves and photosynthesize, and because it makes us consider how exactly a seemingly maladaptive condition could be so common.

Our recent paper in Ecology Letters is an attempt to understand dormancy in a way that might lead to answers (Shefferson et al., in press). Up until this study, studies of dormancy were typically demographic studies of one plant species, indeed typically just one population. Here, we developed our own large dataset of characteristics of these studies, and of the plant species and populations that were under investigation in each case. We strengthened this literature-based dataset with several tens of individual demographic datasets which we analyzed in a standardized way. These standardized analyses included generalized linear mixed model assessments of the impacts of size, reproduction, and sprouting on all vital rates estimable from each dataset, and covering not simply two year intervals (as is the case in virtually all plant demographic studies), but three, in order to better assess the impacts of individual history on vital rates. The three-year approach to analysis is one that I have worked with a great deal and find very useful, because it allows the exploration of the impacts of growth trajectories on vital rates, for example the impacts of large, rapid growth between two years on future survival or reproduction (Shefferson, Warren II, & Pulliam, 2014). We also estimated mean life expectancy, maximum (and median) dormancy duration, and the mean proportion of plants dormant per year, as well as a number of other metrics. This served as the raw material for an analysis of roughly 300 populations and over 100 plant species as to what sorts of patterns we could find in dormancy at the global level.

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A volunteer steward monitors the rare orchids of a county forest preserve near Chicago, Illinois, USA.

Our results were striking, and most notably that dormancy does appear to be driven by common factors across the plant kingdom. Precipitation levels, temperature experienced, herbivory levels, and even latitude determined the levels of dormancy observed, as did the nutritional status of the plant, and its growth form. Trade-offs were strongly linked to dormancy, most notably whether there were any costs of sprouting, such as increased mortality due to greater visibility to herbivores, or a loss of future reproduction due to exhaustion of resources on a sprout in a given year. Costs of growth were also evident, and were documented as costs to survival, reproduction, or sprouting on the basis of growth (i.e. positive change in size) between the previous two years. These results should give biologists new leads to pursue in addressing the ultimate mechanisms behind dormancy.

The COMPADRE Plant Matrix Database was a big help in this analysis. Although the main analyses were performed using our own dataset, COMPADRE was used in two ways. First, in a number of cases, the authors of studies incorporated in our own study did not make their demographic data available publically except through COMPADRE. Thus, in about 20-30 cases, we used projection matrices documented in COMPADRE to supplement what we know about a particular study from the associated paper. Second, we used COMPADRE to test the hypothesis that vegetative dormancy has a common evolutionary origin. We did this by supplementing our own dataset with the identities of herbaceous perennial plant species noted in COMPADRE as not exhibiting dormancy. We then assessed the evolution of vegetative dormancy across the plant kingdom by mapping dormancy status onto a tree of all of these species, procured from the Open Tree of Life Project (Michonneau, Brown, & Winter, 2016). This analysis led us to discover that there were likely many evolutionary gains, as well as losses, of dormancy, and thus that there are likely many genetic bases to the trait.

In the end, our project did something that studies of individual populations could not do – find a common signal across wild-collected data in a typically unobservable life stage, showing what exactly drives this fundamentally interesting but very bizarre phenomenon. We believe this macroecological perspective can greatly alter our understanding of the natural world, and we welcome further studies along these lines.

Richard P. Shefferson

University of Tokyo



Harper, J. L. (1977). Population biology of plants. New York, New York, USA: Academic Press.

Hutchings, M. J. (1987). The population biology of the early spider orchid, Ophrys sphegodes Mill. I. A demographic study from 1975 to 1984. Journal of Ecology, 75(3), 711–727.

Michonneau, F., Brown, J. W., & Winter, D. J. (2016). rotl: an R package to interact with the Open Tree of Life data. Methods in Ecology and Evolution, 7(12), 1476–1481. doi:10.1111/2041-210X.12593

Shefferson, R. P., Kull, T., Hutchings, M. J., Selosse, M.-A., Jacquemyn, H., Kellett, K. M., … Whigham, D. F. (in press). Drivers of vegetative dormancy across herbaceous perennial plant species. Ecology Letters. doi:10.1111/ele.12940

Shefferson, R. P., Warren II, R. J., & Pulliam, H. R. (2014). Life history costs make perfect sprouting maladaptive in two herbaceous perennials. Journal of Ecology, 102(5), 1318–1328. doi:10.1111/1365-2745.12281

Tamm, C. O. (1948). Observations on reproduction and survival of some perennial herbs. Botaniska Notiser, 1948(3), 305–321.

Tamm, C. O. (1956). Further Observations on the Survival and Flowering of Some Perennial Herbs, I. Oikos, 7(2), 273–292. doi:10.2307/3564927

Wells, T. C. E. (1967). Changes in a population of Spiranthes spiralis (L.) Chevall. at Knocking Hoe National Nature Reserve, Bedfordshire, 1962-65. Journal of Ecology, 55, 83–99.

Wells, T. C. E. (1981). Population ecology of terrestrial orchids. In H. Synge (Ed.), The biological aspects of rare plant conservation (pp. 281–295). New York, New York, USA: John Wiley & Sons.