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).

Screen Shot 2017-11-23 at 09.25.06

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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We got the grant!

On August 5, the COMPADRE/COMADRE team was awarded an NSF grant to further develop our matrix databases. The funded project, “An Open-Access Global Repository of Plant and Animal Demographic Data”, will be led by Judy Che-Castaldo at Lincoln Park Zoo in Chicago, IL. This funding comes from the Advances in Biological Informatics program and will increase the function of the database and make it more user-friendly.


Celebrating our grant success at the ESA meeting in Portland, OR

There are three main parts to the funded project. In the first part, we will finish transferring our data into a relational database, which will run more efficiently and be less error-prone than our old system of spreadsheets. A second part will be to build a data-entry portal that our digitization team will use, so that the data input will be consistent across our digitization nodes around the world. Down the line, this portal will be opened to other researchers who can then contribute their own matrix data. The third part of the project will be to refresh our database website to make it more accessible to a wide range of users, including researchers, teachers, students, and conservation managers.

In addition to improvements to the database itself, we will also be bringing on board a project coordinator who will oversee data digitization and communication across all of our participating nodes. Together, we will develop educational materials and hold user engagement workshops at several scientific conferences each year to spread the word and encourage even greater use of our demographic matrix data for research and in classrooms.

We are so excited about this next step in the COMPADRE/COMADRE project! We hope you will follow along and give us your feedback as we continue to make our databases better and more useful for you.

By Judy Che-Castaldo

Our upcoming workshop in Portland, OR

Over the last few years we have run numerous workshops on using the COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database, and on matrix population models (MPMs) more generally.

Where better to run our next workshop than the upcoming Ecological Society of America (ESA) meeting in Portland, Oregon?

This yearly conference brings together academics, students, and practitioners for a few days of talks and workshops on ecology and allied fields. Attendance is in the thousands — the last time it was in Portland (2012) the meeting drew an amazing crowd of 5000! Although not all attendees will have an interest in MPMs (shame!), there are sure to be more than a handful who’d like to know more.

To help with this, this year we are running a half-day workshop entitled “Introduction to Matrix Population Models and Comparative Population Biology Using the COM(P)ADRE Matrix Databases“.

Drawing from experience garnered over the last few years we will take attendees on a five hour journey from the very basics of matrix modelling to comparative MPM analysis using R. The expert instructors are drawn from the COM(P)ADRE committees and include Owen Jones (Uni Southern Denmark), Roberto Salguero-Gomez (Uni Oxford), Judy Che-Castaldo (Lincoln Park Zoo) and Iain Stott (Uni Southern Denmark).

ESA 2017 attendees were given an opportunity to book for the workshop when they registered for the main conference. However, it should be possible to register as a last minute attendee on-site.

If you can’t make it this time, rest assured that we will continue to run similar workshops regularly at relevant meetings/conferences. We also run them on request: We blogged about one of those here.

Here’s looking forward to some matrix modeling fun in a few days!




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

Using demographic data to help recover endangered species

One of the biggest hurdles in conserving endangered species is that most of the time, we know very little about them. Often managers would not know how many individuals currently exist for a given species, let alone more detailed biological information such as how long individuals live, how frequently they reproduce, or how well the young survive.

Fortunately, this is precisely the kind of data that is being made publicly available in COMPADRE & COMADRE for over thousands plant and animal species worldwide. As recently noted in an article in Frontiers in Ecology and the Environment, such demographic data can be used to help manage threatened species. One way to do this is through population viability analysis (PVA), in which we build demographic models to project a population’s future trajectory. By making some simplifying assumptions, we can use these models to assess extinction risk and to compare the relative impacts of different management options.


A marked adult of the endangered Puerto Rican parrot Amazona vittata. This species is a target for conservation efforts based on models using demographic information. Credit: Tanya Martínez (

As a research scientist at Lincoln Park Zoo in Chicago, I am working to facilitate and conduct population viability analyses (PVAs, for short) to help managers make science-based decisions. Although our team focuses mostly on PVAs for zoo animal populations, we also work with wild populations and recovery programs for endangered species. An example is the critically endangered Puerto Rican parrot (Amazona vittata), for which we have modeled the dynamics of the captive breeding population in aviaries managed by the U.S. Fish and Wildlife Service (Earnhard et al. 2014). The goals of this PVA included assessing the demographic and genetic status of the population, and comparing different release strategies in order to maximize the number of releases to the wild while also maintaining a viable aviary population. The results from this analysis have helped to shape management actions for this species, for example releasing young individuals rather than adults. We are now updating this analysis to include new demographic information that have been collected since 2012, and to determine the number of releases that can be sustained under the population’s current breeding rate.

With the creation of more large databases such as COMPADRE & COMADRE, we may find that there is more known about the biology of endangered species than we first thought. However, just because the data and tools are available doesn’t mean they will be used. There is still a lot of work to be done in terms of applying and translating the science to help managers make the best, informed decisions for conservation.

Dr Judy Che-Castaldo

Research Scientist at Lincoln Park Zoo, Chicago

Core committee member of the COMPADRE & COMADRE databases


Citations and more resources

Earnhardt, Joanne, Jafet Vélez‐Valentín, Ricardo Valentin, Sarah Long, Colleen Lynch, and Kate Schowe. “The Puerto Rican Parrot Reintroduction Program: Sustainable Management of the Aviary Population.” Zoo Biology 33(2): 89–98. DOI:10.1002/zoo.21109.

Miner Murray, Meghan. 2017. Zoo data may help bolster wild populations. Frontiers in Ecology and the Environment. DOI: 10.1002/fee.1453

Lincoln Park Zoo. Population Viability Analyses for zoo populations. URL