Beyond white noise: the colors of noise in long-term COM(P)ADRE data

When ecologists build simulations of populations of plants or animals, we usually add in randomness, because populations are always changing in ways that are impossible to fully pre-determine. This randomness is usually white noise, which means that the random values at each point in time are uninformed by the values that came before. Each time, the slate is wiped clean and a new value is drawn from scratch. This is a nice and easy way to generate randomness, but the problem is that populations usually don’t fluctuate with white noise, but with red noise.

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Red noise is a kind of randomness where each random value is positively correlated with the ones that came before. That is to say, it’s randomness with a memory: when it starts to go on a downward trend, it tends to continue that downward trend. You can see this in the images above, where the red noise is much less “wiggly” than the white noise. Blue noise is the opposite of red noise: it also has a memory, but it is negatively correlated to previous points and tends toward the opposite of what happened before.

Population ecologists have already studied colored noise in population growth. Populations usually exhibit red noise in their fluctuations. This is important to know because red noise tends to increase extinction risk: if a population size happens to wiggle downward, then it tends to continue in that direction, until it might eventually crash. But overall population size is just one part of the picture. Population size comes from the combination of births (fertility) and deaths (mortality) of individuals of different ages and life stages. In our study, we wanted to find out if fertility and mortality in different life stages exhibit colored noise in their year-to-year fluctuations, and if they do, how that might change the way we simulate populations.

Nature is full of colored noise, but it can be hard to measure, because you need at least fifteen continuous time points to get an accurate read on noise color, and most datasets on populations are short-term. Fortunately, the COMADRE and COMPADRE databases have detailed population data on a variety of organisms, some of them long-term. We gathered all the long-term datasets we could find, and measured the noise color in fertility and mortality at different life stages. Here are two examples.

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The one on the left is the mountain gorilla, and the one on the right is a plant called velvety goldenrod. Each column in the matrix represents an age, youngest at the left and oldest at the right (velvety goldenrod usually only lives one or two years.) The first row of the matrices is fertility at that age, and the other rows are survival. In both of these species, you can see that fertility and survival have very different noise colors.

Each matrix is also outlined in a color that indicates the noise color for overall population growth. You can see that the noise color for the population growth isn’t always obvious from the noise color of individual fertility and survival squares within the matrix. In the gorilla, the population growth is strongly red noise, like the fertility rates, while in the goldenrod the growth rate has nearly white noise, somewhere in between the blue noise of the fertility and the red noise of survival.

We hope our work will open the door for other researchers to learn more about colored noise in fertility and survival. To that end, we created a package in R for modeling populations with colored noise. It’s called, appropriately, colorednoise.

Julia Pilowsky

The findings and applications of this work can be found in a recent publication here:

Pilowsky & Dahlgren. 2019. Incorporating the temporal autocorrelation of demographic rates into structured population models. Oikos DOI 10.111/oik.06438


Can demographic data be borrowed across species? Probably not for plants

A common challenge in conserving endangered species is not knowing enough about the species itself. Information about the basic biology of the threatened species is critical for helping to determine how well the species is doing and exploring possible strategies for recovery. In particular, information on survival and fecundity rates at different life stages can be used to identify which stages may need the most protection. Demographic rates can also be used to project what the population will look like in the future and under different possible recovery strategies. Unfortunately, data on survival and fecundity rates are rarely available, especially for endangered species, because they are gathered through detailed population monitoring over several years or more. In absence of these data, managers and scientists often suggest “borrowing” data from similar species to fill those data gaps. For this to work, however, the species that are considered similar would need to have demographic rates that are nearly the same or follow similar patterns.

In our recently published study, we tested this assumption. We asked whether plant species that are similar (closely related and/or have similar biological traits) would have similar demographic rates. We used matrix population models from the COMPADRE Plant Matrix Database to estimate three types of demographic rates: intrinsic population growth, stage-specific survival, and fecundity rates, for populations from 425 plant species. We constructed a hierarchical Bayesian model to predict these demographic rates based on maximum plant height and as many life history traits as we were able to find (which was unfortunately not very many for plants!). We also estimated the strength of the phylogenetic signal in these rates, which would tell us the extent to which closely related species would have similar demographic rates. By modelling the different populations for the same species hierarchically, we were also able to look at how demographic rates varied within a species compared to among different species. This provides insight into whether it would be more appropriate to borrow data from a different population of the same species compared to borrowing data from a similar species.

We found that demographic rates were not well-predicted based on phylogenetic relatedness and traits. Phylogenetic signal was weak for population growth and survival rates, but was relatively strong for fecundity rates (Pagel’s lambda = 0.71, 95% CI = 0.43, 0.85). Demographic rates also varied greatly among populations of the same species. For intrinsic population growth, the largest sources of variation in our model were the unexplained variation among species (after accounting for traits and phylogeny), and unexplained variation among populations of the same species (compare “species error” and “population error” in Fig. a; taken from Fig. 2 in the paper). This indicates the traits we included in the model were not good predictors of population growth, and adding other species-level traits may improve the predictions for this demographic rate. However, intrinsic growth rates also varied a lot among populations of the same species. For the other demographic rates we tested, the unexplained variation among populations was actually higher than that among species (e.g., for adult survival shown in Fig. d). This means that differences in survival and fecundity rates among populations of the same species was as great or greater than the differences among different species. This finding suggests that these vital rates are highly dependent on population-level factors such as the specific environmental conditions for the given location and year.

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Our results suggest that borrowing data from similar species, or even a different population of the same species, may not be useful in a conservation context. This is so because the demographic rates for a closely related or similar species could actually be very different from that of the focal species, and using those rates could lead to very different conclusions about how a population is doing. These findings are consistent with previous studies that have asked similar questions, yet the idea of borrowing data continues to crop up because the lack of data is so pervasive. Demographic monitoring can be costly and time consuming, and therefore it is rarely done (or done well), especially for threatened species. We hope our study can help to promote more direct species monitoring to help inform species conservation and management.

Judy Che-Castaldo

Research Scientist at Lincoln Park Zoo – Chicago, USA

Core committee member of COMPADRE & COMADRE



Che-Castaldo, J.P., C.C. Che-Castaldo, M.C. Neel. 2018. Predictability of demographic rates based on phylogeny and biological similarity. Conservation Biology

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.

Introducing our brand new research network coordinator

Hola a todos! My name is Haydée Hernández Yáñez, and I am the new research coordinator for the COMPADRE/COMADRE database. I am very happy to have recently joined the team. I am keen to contribute to the expansion of this remarkable repository of matrix population matrices for plants and animals under the auspices of a recent NSF grant on biotechnology.


I am an ecologist interested in conservation science, spatial ecology, and species interactions. Through my research, I have worked with both plants and animals, in the field and on data analyses. While I was an undergraduate, I worked as a field assistant in the Yucatan Peninsula in Mexico, collecting blood samples from Yucatan wrens (Campylorhynchus yacatanicus), a bird species endemic to Mexico. I also worked as a research assistant collecting field information on plant interactions with ants, as well as constructing mutualistic networks of floral visitors and their host plants. I thoroughly enjoyed working on network analysis and learning about the importance of species interactions. Following my passion for plant-animal interactions, I enrolled in a MSc at the University of Missouri-St. Louis, where I researched the role of herbivores and soil on plant distribution and abundance in a Costa Rican wet forest. It was a great opportunity for me, as I learned more about the role of biotic and abiotic factors in shaping communities.

After graduating, I took a job as a research intern at the GIS lab of the Smithsonian Conservation Biology Institute. There, I applied statistical models to understand the geographical distribution and habitat requirements of the giant panda and Arabian bustards. I wholeheartedly enjoyed working in the area of spatial ecology and with animals (granted, I only saw their data points)! During my time there, I also worked on the database of the Smithsonian’s Movement of Life Initiative, which collects movement data received from collared animals in the field, such as Scimitar-Horned Oryx in Chad and Asian elephants in Myanmar. This experience has made me realize how important spatial ecology is as a tool for conservation science.

I’m currently catching up with the vast literature on population biology and matrix population models. It is a rather steep learning curve (e.g. see the lengthy data-entry protocol, and user guidelines of COMPADRE and COMADRE), but I am convinced that soon I’ll be fully caught up and able to actively shape COMPADRE and COMADRE into new exciting directions. I look forward to working with the team, data contributors, and users to make of the COM(P)ADRE databases an even better repository that will help us in our continual journey of understanding species’ biology.

Haydée Hernández Yáñez


Twitter: @Haydeshka


What makes some plant species more vulnerable to extinction than others? With the current extinction crisis in mind, this question is becoming increasingly important. Because for more than 90% of known plant species the current conservation status is still unassessed (IUCN, 2017) and with a lot of species yet to be discovered (Pimm & Raven, 2017), understanding the factors that are associated a high extinction vulnerability may help to prioritize populations for conservation. Examples of such factors are low reproductive rates and a high susceptibility to environmental disturbance. While there has been considerable effort in parameterizing and compiling demographic data, as demonstrated by the COMPADRE/COMADRE initiative, such data are available only for a relatively small amount of species.

This leads us to the start of our research (de Jonge et al., 2018), which was recently published in Biological Conservation. Inspired by the theory of allometric scaling (West, Brown, & Enquist, 1999), and by the recent work by one of our colleagues on mammals and birds (Hilbers et al., 2016), we decided to attempt to use functional traits to estimate demographic rates, and subsequently extinction risks, in plants. To do so, we derived the maximum growth rate for many species from the COMPADRE Plant Matrix Database (Salguero-Gómez et al., 2015), and  then we combined these with maximum plant height reported in the TRY database (Kattge et al., 2011). To estimate the susceptibility to environmental disturbances, we also calculated the variance in population growth rate for time-series reported in COMPADRE. As expected, plant height is negatively related to both maximum population growth rates and the susceptibility to environmental disturbances.

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Figure 1. Relationships between plant height and maximum population growth rate and variance in population growth rate derived from combining the COMPADRE Plant Matrix Database with the TRY plant trait archive (de Jonge et al., 2018).

So now we know that large plant species indeed grow slower but also more stable than their smaller counterparts. But what does this mean for extinction vulnerabilities? Generally, being less susceptible to disturbances is better. However, being able to increase population sizes quickly can help populations recover after environmental disturbances. To see how these relationships with plant height add up, we calculated the Mean Time to Extinction (MTE) and Probability of Extinction (PE) as a function of height. And there you have it… bigger does seem to be better… all else being equal. Of course there are plenty of other variables that are probably more important than height, but these might not be measured as easily as height.

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Figure 2. The probability of extinction (PE) as a function of plant height calculated using the derived relationships between demographic rates and plant height (de Jonge et al., 2018). Solid black line gives the median PE and shading shows the 90% confidence interval. Two plant species for which the PE could be calculated directly are shown, left: Solidago mollis and right: Eremophilia maitlandi

Our work demonstrates how linking large databases such as COMPADRE/COMADRE and functional trait databases (see also Adler et al. 2014) can reveal relationships between traits and demography. While COMPADRE already offers a huge collection of plant demographic, long-term monitoring studies on populations of large plants are still in short supply. Furthermore, during our research, we often found that the overlap in species between the different databases was too small to successfully combine them. For example, we were not able to investigate the role of other traits, such as seed mass or wood density, because there were not enough species occurring in COMPADRE for which such data were available in TRY. Increasing the species overlap might be done by integrating the demography and functional traits of species in one database. Such, multi-faceted global databases would help us to better understand the relationships between functional traits and demography.

Melinda M.J. de Jonge
PhD Student
Department of Environmental Science
Radboud University Nijmegen
PO Box 9010, 6500 GL Nijmegen, The Netherlands

Adler, P. B., Salguero-Gómez, R., Compagnoni, A., Hsu, J. S., Ray-Mukherjee, J., Mbeau-Ache, C., & Franco, M. (2014). Functional traits explain variation in plant life history strategies. Proc. Nat. Acad. Sci. USA, 111(2), 740-745. doi: 10.1073/pnas.1315179111

de Jonge, M. M. J., Hilbers, J. P., Jongejans, E., Ozinga, W. A., Hendriks, A. J., & Huijbregts, M. A. J. (2018). Relating plant height to demographic rates and extinction vulnerability. Biological Conservation, 220, 104-111. doi:

Hilbers, J., Schipper, A., Hendriks, A., Verones, F., Pereira, H., & Huijbregts, M. (2016). An allometric approach to quantify the extinction vulnerability of birds and mammals. Ecology, 97(3), 615-626.

IUCN. (2017). IUCN red list of threatened species. Version 2017.1.

Kattge, J., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., . . . Wirth, C. (2011). TRY – a global database of plant traits. Glob. Change Biol., 17(9), 2905-2935. doi: 10.1111/j.1365-2486.2011.02451.x

Salguero-Gómez, R., et al. (2015). The COMPADRE Plant Matrix Database: an online repository for plant population dynamics. Journal of Ecology 103, 202-218

Pimm, S. L., & Raven, P. H. (2017). The fate of the world’s plants. Trends in Ecology & Evolution, 32(5), 317-320.

West, G. B., Brown, J. H., & Enquist, B. J. (1999). The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science, 284(5420), 1677-1679.

Accounting for clonality in plant demography – a story of an unintended article

The story of this article started already some three years ago at the BES symposium Demography beyond populations organised by Rob Salguero-Gómez and colleagues. Two of us were sitting in the auditorium and listening to talks, when almost simultaneously we had the same idea. “Do clonal species differ in their demographic characteristics? They definitely should, given their completely different way of growth! It must be quite easy to find out; we just merge CLO-PLA (the database of plant clonal traits) and COMPADRE.” Therefore, we started to work enthusiastically on the task even in the auditorium. Upon return from the in many aspects great Symposium, we met with Tomáš and told him of our idea and he said that he had had a very much the same idea for some time as well. The three of us set out to work and in a couple of months, we assembled the core of analyses of demographic characteristics of Central European clonal and non-clonal plant species.

So far, the story of the article had been very much as if narrated by a university PR department, yet here, the problems started to emerge. Results of our analyses indicated several effects of clonality and shoot ontogeny type on demographic characteristics, but taken together they did not enable us to come up with a coherent discussion of our paper. Jitka and Zdeněk started to suspect that there must have been at work biases in demographers’ choices of study species. They went through the original works, which the matrix population models (hereafter MPM) in COMPADRE are based on, and found that there are at least three fundamental ways how demographers study demography of clonal species (Fig. 1). Some researchers did not incorporate clonal propagation into MPMs at all. Others treated clonal propagation as a part of mother plant’s growth, while yet others saw clonal propagation as another reproductive pathway similar to generative reproduction. Interestingly and unfortunately as well, there indeed had been a bias in demographers’ decisions (Table 1). Clonal propagation of species with monocyclic shoot ontogeny type was more likely to be neglected, while in species with polycyclic shoot ontogeny type, i.e. overlapping shoot cohorts, clonality was often considered a part of mother plant’s growth. Only clonal propagation of species with monopodial shoot ontogeny type, which typically possess distinct leaf rosettes, was seen as means of reproduction.


At this point, the three of us ended many times in a debate, which of these three approaches is the right one to treat clonal propagation. Finally, during one such heated debate, Tomáš reached out into the bookshelf for now-classical Population biology of plants by J.L. Harper. Harper tackled the problem of defining an individual in the clonal plant’s population by defining a ramet, i.e. such rooted part of the genetic individual, which can live through all life-cycle stages. Reviewing existing demographic studies of clonal species through this prism indicated that in most species clonal propagation needs to be viewed as another mode of reproduction albeit with different genetic and evolutionary consequences. Treating clonal propagation as reproduction does not seem to be a suitable option only in species with very strong integration of ramets (e.g. Carex humilis).

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As we were reviewing the original literature on demography of clonal species, a huge wealth of mainly practical MPM applications unfolded to us and we definitely see this as one of the main assets of MPMs. By drawing recommendations of the ways of clonal propagation incorporation into MPMs, we hope to help to raise the utility of MPMs also for asking general questions about plant demography and evolutionary biology.

Zdeněk Janovský, Jitka Klimešová and Tomáš Herben


The resulting publication of this initiative can be found here:

Janovský, Klimešová & Herben. Accounting for clonality in comparative plant demography growth or reproduction? Folia Geobotanica 1-10



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Illuminating the world’s demographic dark corners

Perhaps by pure coincidence, I was the first user to have downloaded COMPADRE when it first went live, back in 2014. At that time, Rob Salguero-Gómez and I were at the same airBnB, and ready to attend the EvoDemoS meeting at Stanford. At that time and place, we were less than 60 km from the nearest matrix population model in the database (Calochortus tiburonensis; (Fiedler 1987)). My Ph.D. fieldwork brought me considerably closer, and not just because I worked with historic matrix population models collected near the field station. Instead, yellow-bellied marmots (Marmota flaviventris (Ozgul et al. 2009, 2010)) scratched and gnawed under my cabin’s floorboards every day. In short, my thinking about demography has been shaped not only by very well-studied places, but also in them.

It will come as no surprise to most readers of this blog that certain regions of the world and branches of the tree of life are more strongly represented than in biological databases, whereas other parts and taxa are almost entirely unsampled. COMPADRE and COMADRE are no exception (Salguero-Gómez et al. 2015, 2016). Indeed, we lose a lot of information by only considering the current state of our field’s geographic bias. We stand to learn much more if we could see its trajectory. By analogy, the power of matrix models stems from connecting static snapshots of a population’s state to reveal the motion of life histories and population dynamics as they unfold in time. We can use an analogous approach to animate our progress towards illuminating the dark corners of demography. I was curious what these patterns would show, so I did just that using the >50 years of georeferenced matrix population models contained in the latest releases of COMPADRE and COMADRE.

We most commonly think about spatial biases as hotspots of research activity, but what happens when we flip that question around and ask where the least studied places are? To do this, I wrote an R script that divides the Earth’s surface into pixels and measures the distance from each pixel to the nearest matrix population model* (Fig. 1)—the brightest pixels show populations where a matrix model has been constructed. Using this approach, it is rather easy to find the place that is the most isolated from the illumination of demography.

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Fig. 1. The current darkest corners of demography for (A) plant matrix population models in the COMPADRE Plant Matrix Database and (B) animal matrix population models in the COMADRE Animal Matrix Database. The most isolated points from all matrix population models are shown as colored dots (blue square: of the whole Earth, green triangle: of land masses, yellow circle: of land masses excluding the largely uninhabitable Antarctica).

In the most recent versions of the sister datasets, Henderson Island and Rapa Nui (Easter Island) are the most isolated lands from a matrix model for plants and animals, respectively**. To me, two other patterns are apparent when the data are presented this way. First, plant demographers (Fig. 1A) have better sampled the tropics than have animal demographers. Part of this may stem from the fact that many animals are almost certainly harder to mark and recapture in a dense tropical forest whereas plants are more easily re-found. The second pattern that strikes me is that animal demographers have sampled oceanic islands much more thoroughly than plant demographers. Again, this may be related to the higher mobility of animals and the desirability of a closed population (e.g., sheep wrangling on St. Kilda vs. anywhere on the mainland). However, it may also be related to the fact that many pelagic animals use remote islands as breeding locations (e.g., the Laysan albatross). Most animal matrix models are age structured (Salguero-Gomez et al. 2016), so marking animals at birth is made considerably easier if they all gather in one place to raise young.  You may have other candidate explanations for these differing spatial biases, and I’d certainly be keen to hear them.

By animating these maps over time, we gain a much deeper understanding of how demographers have been illuminating demographic dark corners, and we can see how bringing demographic models to new areas changes the arrangement of the darkest corners (Fig. 2).




Fig. 2. How the demographic darkest corner has moved since the advent of matrix population models (1965-2016). Plant matrix models from COMPADRE (above) and animal matrix models from COMADRE (below) are shown separately with symbols as in Fig. 1. The color gradient rescales each year to better highlight the least known areas.

Finally, we can condense this information into a plot of how the distance to the most isolated place has decreased over time as more demographic studies have been conducted (Fig. 3).

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Fig. 3. Decline in the maximum (solid line) and median (dashed line) distances away from a matrix population model for COMPADRE (left) and COMADRE (right) over time. Colours show different subsets where blue is any point on land or water, green is restricted to points on land, and yellow is restricted to points on land excluding Antarctica. Note that the maximum possible isolation distance on Earth is approximately 20,038 km.

Even though the current version of COMPADRE has more than twice the number of unique population locations than does COMADRE, the demographic dark places for plants have remained darker for plants than for animals since the late 1980s. Moreover, Henderson Island has remained the most isolated point from plant demography for almost 20 years! The picture is a bit more optimistic if we look at the median distance from a matrix model. In just over 50 years, we have sampled such that half of the world is <1500 km from a plant matrix model and is <1300 km from an animal matrix model.

It is worth asking what, if anything, we are missing with these geographic biases. For plants, we are certainly under-sampling some disturbance regimes and life histories that tend to be more common on small remote islands. For example, <<1% of taxa in COMPADRE are modelled as truly dioecious (i.e., separate sexes) compared to ca. 5% of plant species that have this sexual system (Renner 2014). Improved sampling of oceanic islands—several of which have elevated frequencies of dioecious species (Sakai and Weller 1999)—could simultaneously reduce both the geographic and life history biases in the database. For animals, sampling is sparsest in some of the most species rich ecosystems on the plant like the Amazon, the Congo, and tropical southeast Asia. It is hard to imagine how better sampling in these areas could fail to reveal unique and surprising insights.

As I now write this from my postdoc position in Zurich, I’m again finding myself in a demographic bright spot. The nearest matrix population model is an hour away by train (white stork, Ciconia ciconia; (Schaub et al. 2004)). Rapa Nui, on the other hand would be 34 hours by plane***, and Henderson Island is all but impossible to reach without a private yacht. Still, we can and should work to target more accessible places that are nonetheless demographic dark spots. Moreover, we can bolster the utility of these databases by targeting clades and life history strategies that are underrepresented, but that will have to remain the subject for a future post.

Will Petry

Postdoctoral researcher at ETH Zurich


*This is a raster-based approximation of Voronoi polygons. It’s slow and clunky, but it has the advantage of accounting for the Earth’s ellipsoid shape. I used the WGS84 ellipsoid and cells that are 1/12° (+/-9.25 km) on each side.

**And here I reveal my own terrestrial bias.

***Assuming no connections go awry.


Literature cited

Fiedler, P. L. 1987. Life history and population dynamics of rare and common mariposa lilies (Calochortus Pursh: Liliaceae). Journal of Ecology 75:977–995.

Ozgul, A., D. Z. Childs, M. K. Oli, K. B. Armitage, D. T. Blumstein, L. E. Olson, S. Tuljapurkar, and T. Coulson. 2010. Coupled dynamics of body mass and population growth in response to environmental change. Nature 466:482–485.

Ozgul, A., M. K. Oli, K. B. Armitage, D. T. Blumstein, and D. H. Van Vuren. 2009. Influence of local demography on asymptotic and transient dynamics of a yellow‐bellied marmot metapopulation. The American Naturalist 173:517–530.

Renner, S. S. 2014. The relative and absolute frequencies of angiosperm sexual systems: Dioecy, monoecy, gynodioecy, and an updated online database. American Journal of Botany.

Sakai, A. K., and S. G. Weller. 1999. Gender and sexual dimorphism in flowering plants: A review of terminology, biogeographic patterns, ecological correlates, and phylogenetic approaches. Pages 1–31 in M. A. Geber, T. E. Dawson, and L. F. Delph, editors. Gender and sexual dimorphism in flowering plants. Springer Berlin Heidelberg.

Salguero-Gómez, R., O. R. Jones, C. R. Archer, C. Bein, H. de Buhr, C. Farack, F. Gottschalk, A. Hartmann, A. Henning, G. Hoppe, G. Römer, T. Ruoff, V. Sommer, J. Wille, J. Voigt, S. Zeh, D. Vieregg, Y. M. Buckley, J. Che-Castaldo, D. Hodgson, A. Scheuerlein, H. Caswell, and J. W. Vaupel. 2016. COMADRE: a global data base of animal demography. Journal of Animal Ecology 85:371–384.

Salguero-Gómez, R., O. R. Jones, C. R. Archer, Y. M. Buckley, J. Che-Castaldo, H. Caswell, D. Hodgson, A. Scheuerlein, D. A. Conde, E. Brinks, H. de Buhr, C. Farack, F. Gottschalk, A. Hartmann, A. Henning, G. Hoppe, G. Römer, J. Runge, T. Ruoff, J. Wille, S. Zeh, R. Davison, D. Vieregg, A. Baudisch, R. Altwegg, F. Colchero, M. Dong, H. de Kroon, J.-D. Lebreton, C. J. E. Metcalf, M. M. Neel, I. M. Parker, T. Takada, T. Valverde, L. A. Vélez-Espino, G. M. Wardle, M. Franco, and J. W. Vaupel. 2015. The COMPADRE plant matrix database: An open online repository for plant demography. Journal of Ecology 103:202–218.

Schaub, M., R. Pradel, and J.-D. Lebreton. 2004. Is the reintroduced white stork (Ciconia ciconia) population in Switzerland self-sustainable? Biological Conservation 119:105–114.




New version of the COMPADRE & COMADRE portal is coming up

Just like winter, a new version of COMPADRE & COMADRE is coming too. This work has been going on for a couple of years now, but we are starting to make some tangible progress and we are excited to share where the project stands at the moment. We assume that you are familiar with the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database in their current format. Navigating to presents you with information about the databases, the team behind them and a direct download link to a single R-data file for either COMPADRE or COMADRE. There is also a table for the ‘waiting list’ of species that are soon to be added to the sister databases, but the current format does not offer searchable menus to explore the data before downloading them.

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The current COMPADRE & COMADRE data portal

Behind the scenes, we have been building a new way of presenting and accessing COMPADRE and COMADRE, whereby you’ll be able to explore the data through the data portal. Here is a quick rundown on some of the features:

1. Query, then download:

You will be able to query the database so that before you download the data, you can work out whether the data are useful to you. We will have developed tables to search by species and publication and will be extending this to advanced filtering through all metadata so that the user gets just what s/he needs.


Table for searching by species, able to filter by growth type and search terms

2. Explore demographic info across the tree of animals and plants:

Come and explore among multiple kingdoms, orders and families, worm your way through the tree of life and see what species are related to one another, and where we have (and not) demographic data. Currently, we are finalising a click-through explorer gathering a short summary from Wikipedia, but there are plans to integrate the incredible OneZoom taxonomic explorer into the user interface.


Click-through taxonomic explorer

3. Educational pages for all ages, sizes and stages:

We are dedicated to teaching all st/ages about the wonders of demography and connecting maths with the living environment.

For every species page, we are developing a georeferenced interface to show the locations of the populations of the study species. We also include species occurrence data from GBIF to give a general view of the distribution of the species. In addition, we provide relevant metadata about the species to be able to contextualise the matrix model.


Map showing the locations of studied populations, the small yellow squares are GBIF occurrence data.

4. More hierarchy, but easier to manage and navigate:

The database’s hierarchical structure is being incorporated into how we present the data. Beneath the species information, each population is listed with summary statistics about how many matrices it contains, its ecoregion and for how long it was studied. Expanding this reveals each of the matrices that were produced from studying this population. Expanding the matrices shows the matrix model in full, with clickable tabs to select whether you wish to view the full matrix, fecundity matrix, survival matrix or clonal matrix.


The contents of a single population with multiple matrices. One matrix has been expanded to show the values in the matrix.

5. Giving credit where it is due:

At the bottom of the page, you can see details about the publications from which the MPMs were taken from, and directly link to the original journal article via DOI. We hope that the users of COMPADRE and COMADRE will continue to credit the tremendous work done by the original sources of the demographic information.


A list of all publications contributing data for Cirsium pitcheri

Intended users of the database

We hope that this project will make the database more accessible to a wider range of users. These improvements to the COMPADRE & COMPADRE data portal resource are aimed at:

  • Students and entry-level population modellers who want to explore matrix populations models (MPMs).
  • Scientists who aren’t primarily studying population dynamics or demography but are working with a study species and would like to know more about their study species.
  • Comparative demographers who are looking to use a large chunk of the data available but might want to examine a particular matrix or species as to whether they will include it in this analysis. The user interface will provide an easy way of viewing the data and linking to the literature source.
  • Ecologists who are looking to construct MPMs but would first look at previously constructed matrices for similar species or study motivations to help guide their study design.
  • School groups who are interested in life-cycles and we want to hook them into a life long obsession of COMPADRE and COMADRE.
  • Other managers of ecological databases that might want to share data and collaborate.

The Compadrino Zone

The compadrinos are the real workforce behind the demographic data. These are a group of MSc, BSc, PhD and postdocs students supervised by the core committee. They carefully inspect published papers, extract relevant information, email authors for extra information and clarification, and help implement the error-checking before the data go live on the open-access portal. The total COMPADRE & COMADRE workload is split amongst nodes located at different institutions across the globe. This poses a logistical challenge as to how to manage the data which have been currently been tackled with cumbersome spreadsheets.


A form for entering information about a publication, with an auto-fill from DOI javascript button.

The work we are doing will also improve the behind-the-scenes data digitisation workflow. Compadrinos will now have access to easy-to-use friendly web forms and be able to use the user interface to help identify errors. We will also be able to track changes to the database over time and make it a more streamlined, enjoyable, and less error-prone experience. That’s all well and good but as a user of the database, why should you care about this? These improvements should result in an increased flow of data from publications to database, leading to a greater quantity of data, expanding the taxonomic breadth of the database.

We hope that once the new portal has been instituted, we will move onto a new functionality: providing users with the ability to upload their data directly. The hope is for COMPADRE & COMADRE to become the DRYAD of stage-structured demography. Of course, all data that th users will upload will still need careful curation, and every datum will continue to go through our error-checking and data standardisation protocols.

Technical details

Finally, a few details of the project:

  • The newest iteration of the COMPADRE and COMADRE database will be hosted on a server at the University of Exeter and mirrored among all nodes of the COMPADRE/COMADRE digitalization network.
  • The web application is built on a python-based framework called Flask and the database is being migrated from the original R-data object into a MariaDB database.
  • Following the open-access philosophy behind COMPADRE & COMADRE, the present also is an open source project, and the source code is available on our Github repository:


Now we’ve whetted your appetite, you’re probably wanting to know how long before you can delve into this exciting new resource? Our first milestone is to finalise the process by which compadrinos input and process data to make sure everything functions as expected and the data are flowing from publication to the database without a hiccup. This is our focus for the next couple of months. Following that, we will change focus to improving the user interface and data download tools for end users, hoping to launch late 2017.

Simon (@simon_rolph) and Danny (@dannylbuss)

Image uploaded from iOS.jpg
Danny and Simon hard at work

Follow COMPADRE (@compadreDB) and COMADRE (@comadreDB)

Have questions about COMPADRE & COMADRE, or would like to send us your data? Please contact us at

About the writers of this blog: Danny Buss is a Computing Development Officer (CDO) at the University of Exeter in Dave Hodgson’s research group, and Simon Rolph is PhD student at the University of Sheffield in Rob Salguero-Gómez’s research group. A few weeks, ago we convened in Sheffield to work together on an important upgrade of the COMPADRE data portal. We must also recognise Francesca Sargent’s huge contribution towards this project, previously a CDO at the University of Exeter.

A signature of life history in the stochastic dynamics of structured populations

Different species have different average life histories. Such a variation is apparent from key species characteristics like longevity, age of maturity, or the number of offspring produced per clutch. Matrix population models such as those in the COMADRE Animal Matrix Database allow us to calculate and explore the diversity of such characteristics across species, and understand how they are connected.

In a recent study published in Oikos, my colleague and I examined another key life history characteristic: demographic variance.  This trait provides a measure of the total amount of stochasticity in a given life history arising from inherent randomness in survival, offspring production, and other individual-level demographic processes. Typically, species with a high demographic variance are short-lived and can produce many offspring per reproductive event, such as mice. Long-lived species, who typically produce only one offspring at a time, such as elephants, tend to have a much lower demographic variance.

Using matrix population models from 24 mammal species from COMADRE, we calculated their demographic variance. We first considered how demographic variance is related to generation time (see Figure 1).  As expected, there was a strong correlation with generation time, where species whose populations take longer to renew its individuals had a much lower demographic variance. 

screen-shot-2017-03-05-at-05-01-38Figure 1. Demographic variance plotted against generation time, calculated using demographic information for mammals and birds from the COMADRE Animal Matrix Database.

We then considered the temporal correlation in the stochastic population dynamics, which arise because of short-term fluctuations in the demographic structure.  For each matrix model we estimated the autocorrelation function, which describes the degree to which two population growth increments tend to be similar for different time steps. This function is different for each model, and represents a signature of the life history and demographic stochasticity (see examples in Figure 2).  A main result from our study is that the sum of these correlations over time lags describes the impact of population structure on the demographic variance.

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Figure 2. Autocorrelation functions for two of the mammal population models used in the study – a signature of the life history.  The sum of this function describes the impact of the demographic structure on demographic variance (which can be positive or negative, depending on the structure).

Thanks to COMADRE, we were able to demonstrate this result using the matrix models of different mammals.  Future research may also consider applications to other taxa (e.g. birds, as in the Figure 1) and other kinds of life histories.

Yngvild Vindenes

Science committee member of COMPADRE & COMADRE


Vindenes, Y. and Engen, S. In press. Demographic stochasticity and temporal autocorrelation in the dynamics of structured populations. Oikos DOI: 10.111/0ik.03858