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).

 

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Iso_COMADRE.gif

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 http://www.compadre-db.org/ 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.

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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.

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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.

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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.

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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.

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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.

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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: https://github.com/Spandex-at-Exeter/demography_database

Timeline

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 compadrecontact@gmail.com

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

Reference:

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.

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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 (tanyamariemartinez@gmail.com).

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

Demographic extrapolations: how far can/should we go?

Just because you can (extrapolate models using the available demographic data in COMPADRE and COMADRE, and other sources) doesn’t mean you should. Shaun Coutts and colleagues have published a work in Ecology Letters asking how far can one extrapolate demographic outputs within and across species based on demographic knowledge, geographic and phylogenetic distance.

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The academic answer is here: SR Coutts, R Salguero‐Gómez, AM Csergő, Buckley YM (2016) Extrapolating demography with climate, proximity and phylogeny: approach with caution. Ecology Letters. doi: 10.1111/ele.12691

The lay summary is here: Shaun Coutts’ Research Site

The power of comparative demography: booms and busts

Jenni McDonald’s work on transient dynamics using COMPADRE was recently recognized with the Postdoctoral Excellence Award of the Plant Population Ecology of the Ecological Society of America. Congrats Jenni! Here we leave you with a short summary of her work.

 

The natural world is not static and populations don’t exist in a vacuum. Environmental variation is inevitable for wild populations. Consequently, the vital rates, stage structures and dynamics of every wild population will change through time. Earlier this year our paper linking stochastic dynamics into contributions from transient dynamics (driven by non-stable stage structures) and asymptotic dynamics (caused by changes in vital rates) was published in Journal of Ecology.

The idea of transients being important within stochastic environments was not a new one. However, we built on previous work by exploring absolute dynamics, which accounts for the strength of opposing asymptotic and transient effects. We also used a large-scale comparative database to test this hypothesis, using data from 277 plant populations across 132 species from the COMPADRE Plant Matrix Database. This comparative framework opened up exploration of evolutionary and ecological patterns. Our key result was that transients are ubiquitous in plant populations, contributing to half the dynamics in stochastic environments.

Understanding transients is vital for management of both pest species and those of conservation concern. Perturbations in the environment, such as fire, harvesting, disease epidemics and weather changes, will mean that populations are rarely at stable stage structure. Given the contribution of transients to population growth, ignoring non-stable population structure will have implications for management as population growth will be different from that predicted by the long term population growth rate. Consequently, an understanding of transient boom (accelerated population growth) and bust (reduced population growth) could be exploited by managers and conservationists to maintain persistence (or cause extinction) of wild populations. Transients may also provide an explanation as to how some species thrive in a variable landscape, whereas others suffer population declines, for example endangered species potentially may be those who respond poorly to demographic disturbance. Harnessing the power of comparative analysis enabled us to explore evolutionary and ecological patterns and start to shed light on these possibilities.

The COMPADRE Plant Matrix Database provides open access to thousands of plant population projection matrices parameterised from empirical data previously dispersed throughout peer-reviewed and grey literature. COMPADRE is the ideal resource to explore transients across populations varying in evolutionary history, growth form and life stage complexity. We found that both transient contributions and asymptotic contributions are influenced heavily by the number of life stages modelled. This could mean that species with complex life histories are able to bounce back from demographic disturbances; alternatively, this observation may be an artefact of modelling design. We found no phylogenetic signal in the contribution of transients to stochastic growth, nor clear patterns related to growth form. Plant populations have a tendency to boom rather than bust in response to variable environments. This raises the future question; have populations evolved to bounce back from disturbance?

Our research also highlights the value of large-scale databases. The power of comparative demography allowed us to ask questions regarding the impact of non-equilibrium stage structure on stochastic population dynamics and reveal patterns that would not have been deduced from other means. Empirical data on the life histories of living organisms stored in COMPADRE can contribute to a diverse spectrum of research areas and is of relevance to scientists working in the fields of conservation, ecology and evolution. In addition to the ability to ask new questions, the free and instant access of the database removes any logistical obstacles that many researchers may face in terms of field work and laboratory studies. Such a resource has the potential to inspire new scientific insights, while also embracing the diversity of work life patterns of scientists – undoubtedly a powerful resource.

Jenni McDonald

Postdoctoral researcher at Exeter University

 

New COMPADRE & COMADRE versions are out!

Yesterday, coinciding with the symposium “Landscape demography: Population dynamics across spatial scales” at the 2016 Ecological Society of America annual meeting, where we gave the talk “Global plant and animal demography: tearing the curtain and filling up the gap“, we released the new two versions of the sister databases: COMPADRE version 4.0.0 and COMADRE version 2.0.0. These can be downloaded fully open-access at www.compadre-db.org.

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Our goals, at COMPADRE & COMADRE: to digitise and standardise matrix population models published and communicated to us by population ecologists, to error-check, fix and complement the information with metadata (e.g. taxonomy, phylogeny, biogeography), and to make it open access. In other words: to bring the field demographic data to your computer.

What’s new in them? More matrix population models, more species, more metadata! The COMPADRE Plant Matrix Database now contains 695 unique taxonomically accepted plant species from 819 published or personally communicated studies, adding up to a total of over 7,000 population matrix models. Similarly, the COMADRE Animal Matrix Database now contains 405 taxonomically accepted animal species outsourced from 508 studies, with a total of 1,927 matrix models.

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Summary statistics of the new versions of COMPADRE and COMADRE: more species, more matrix population models, and more metadata.

The increase in species and number matrices adds up to an unprecedented geographic cover. Clearly, however, geographic and taxonomic biases do exist in the databases, and we encourage users to carry out demographic research using matrix population models in under-explored areas (e.g. Belgium, Ireland, Italy, Greece, Russia, Morocco, Ecuador, Philippines, etc)

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Geographic location of the studies where GPS information was provided in the publications, showing the global coverage of COMPADRE and COMADRE (Salguero-Gómez et al. in prep.)

In addition to the new species and matrix models, we have also archived new variables. For instance, the latitudes and longitudes are no longer provided as separate degrees, minutes and seconds, but rather provided as two vectors (Lat and Lon), which is a more manageable format. For instance, try the following in your R console, after you have downloaded the Rdata objects and loaded them onto R:

load("~/WhereverYouSaveYourStuff/COMPADRE_v.4.0.0.RData")
map("world",col="white",fill=T,bg="white",xlim=c(-175,176),ylim=c(-60,85),border="gray",mar=rep(0,4))
points(compadre$metadata[,c("Lon","Lat")])

We’ve also renamed some other variables to unify the organization of plants and animals, for instance:

load("~/WhereverYouSaveYourStuff/COMADRE_v.2.0.0.RData")
table(comadre$metadata$OrganismType)

load("~/WhereverYouSaveYourStuff/COMPADRE_v.4.0.0.RData")
table(compadre$metadata$OrganismType)

 

More information about the databases, their organisation, the way we digitise, complement, error-check and release information, and some useful workshop materials and R functions (incoming R package… stay tuned!) can be found here.

Happy COM(P)ADRE-ing!

The COMPADRE & COMADRE core committee

     Rob Salguero-Gomez – The University of Sheffield & Max Planck Institute for Demographic Research (MPIDR)

     Owen Jones – Southern Denmark University (SDU), MaxO

     Ruth Archer – Exeter University

     Yvonne Buckley – Trinity College Dublin

     Judy Che-Castaldo – Lincoln Zoo

     Hal Caswell – University of Amsterdam

     Tom Ezard – University of Southampton

     Dave Hodgson – Exeter University

     Alex Scheuerlein – MPIDR

     Jim Vaupel – MPIDR & SDU, MaxO

 

 

 

 

Drivers of realized population dynamics and COM(P)ADRE

In a paper recently published in Ecology Letters, our brand new COM(P)ADRE science committee member Dave Koons, together with David Iles, Michael Schaub and our core committee member Hal Caswell, present a set of transient life table response experiments (LTREs) for decomposing realized population growth rates into contributions from specific vital rates and components of population structure. Unlike previous LTREs, the transient versions do not require assumptions about a constant environment or stationary environmental variation. Rather, they embrace the non-stationary environmental conditions (changing mean, variance, or both) created by climate and landscape change.

By applying their transient LTREs to a diverse array of simulated life histories, the authors reveal that established concepts in population biology will require revision because of reliance on asymptotic approaches that do not address the influence of unstable population structure on population growth and mean fitness in time-varying environments. Going forward, the repository of longitudinal demographic studies in COMPADRE and COMADRE will be necessary for testing these predictions, and applying transient LTREs to real-world conservation and management problems.

For popular press coverage of our paper, see here.

Dave Koons

Assoc Prof Utah State University

COMPADRE & COMADRE science committee member

Koons, D.N., D.T. Iles, M. Schaub, and H. Caswell. 2016. A life history perspective on the demographic drivers of structured population dynamics in changing environments. Ecology Letters. DOI: 10.1111/ele.12628

 

A Cornish demographic extravaganza

It was a rainy summers day in Cornwall, when a rare occurrence took place: comparative demographers gathered in the masses. They brought ideas, laughter, heaps of data, and collaborative thoughts and, despite some uncooperative travel troubles, they got their heads down and began to tackle the demographic problems of the 21st century.

The 10-day Cornish demographic extravaganza began with an introductory workshop where early career academics working with the COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database educated the ‘scientists of the future’, aka MRes, PhD students and postdocs, about the importance of vital rates for conservation success. Vital rates you say? These rates describe how individuals within a population grow, reproduce and die; this information can be used to determine how likely a population is to expand, shrink, invade or even become extinct. Although encouraging scientists early on their career that mathematical ecology and comparative (desk-base!) studies are the key to our conservation problems is challenging at times, the workshop went swimmingly and the lecturers empowered the protégé demographers to follow in their academic footsteps.

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Ruth Archer, Jenni McDonald, Owen Jones (left photo) and Rob Salguero-Gomez ran a 3-day workshop on the comparative power of the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database at the Cornwall campus of the University of Exeter for some ca. 20 graduate students and postdocs.

This 3-day demographic teaser was followed by the main event. A thrilling international opportunity funded by the European research council, to bring together a world-class set of researchers with a focus on comparative demography and life history evolution, to work together for a full week.

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The group of international researchers (Left-to-right, top: Phil Wilson, Raymond Tremblay, Danny Buss, Eelke Jongejans. Bottom: Brigitte Tenhumberg, Drew Tyre, Elizabeth Crone, Rob Salguero-Gomez, Yvonne Buckley, Dave Hodgson, Judy Che-Castaldo, Veronica Sommer, Satu Ramula, Owen Jones, Simon Rolph and Iain Stott) on a day out in the field appreciating the local flora and fauna of Cornwall while discussing future directions of plant and animal demography.

The agenda was set by the SPAND_EX king, der Hodgsonmeister, who, in collaboration with the international researchers, discussed the need to highlight the “sins” found throughout the demographic literature, and demonstrated them to the scientific community. Gluttony, greed, sloth spilled onto the blank canvas…..wrath, envy, pride, shortly followed by lust…….. 7 sins turned into 10, than 12, 13 and even more……. this posed a big problem for the team. Seven sins…….. 14 crimes, 7 sins……. 14 crimes. The team pondered about this, the facts did not match the catchy title…… a testing problem to be solved by one of the newest members of the team COMPADRINO Simon Rolph (see photo above).

Day 2 began with the CDO duo – Computing Development Officers, Francesca Sargeant and Danny Buss, at the COMPADRE Exeter node, spilling the beans on the transformation of the COMPADRE database into a modern, speedy, query-able SQL storage machine. This raised much discussion: who was going to secure the funding? is the database going to transform into something more?  will COMADRE follow the same footsteps as COMPADRE? will the CDO team really go back down to nil by October this year?… All of these conversations were then taken by the COMPADRE core committee to the 3rd annual committee, which took place in Odense, Denmark, the week after.

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Danny Buss (left) and Francesca Sargent (right) discuss with the group of international demographers the latest developments that they have championed during the last year at the COMPADRE Exeter node: COMPADRE is currently undergoing an internal re-make, with SQL capabilities, and graphical displays for teaching purposes, with interactivity with IUCN and other open-access repositories.

The excitement began in the final days, where the mathematical challenges, unanswered ecological questions and exciting comparative techniques spilled from the minds and onto the whiteboards……. could the team come up with some solutions to……. Phylogenetic gap analysis for ecological traits……… a better understanding of the complex relationship between environmental drivers and demographic rates…….. or even, the burning answer to why does phylogenetic signal differ for demographic traits between fauna and flora, are plants and animals really that different?

After plenty of discussions, food, Cornish dolphins, botany and statistical analysis…….. the team was ready to begin their outputs. I hope you are all looking forward to some highly interesting publications to be released over the next year or two – I know I am!

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More ecological inspirations for the research that took place on comparative animal and plant demography at the Cornwall campus of Uni Exeter. Left to right: dolphin! Danny Buss, Yvonne Buckley, Judy Che-Castaldo, Raymond Tremblay, Dave Hodgson, Eelke Jongejans, Veronica Sommer, Brigitte Tenhumberg, Drew Tyre, Elizabeth Crone & Owen Jones. Not shown individuals: Rob Salguero-Gomez (behind the camera), and Satu Ramula (top level of boat).

A huge thank you goes out to the team at NERC who provided the funding for such an extraordinary collaborative event, I really hope we can re-convene together again next year.

Danny Buss, Computer Development Officer

COMPADRE – Exeter node

COMPADRE & COMADRE in a world of big data and comparative science

A blog post by Maria Paniw

Take a look at recent publications in peer-reviewed journals or popular science magazines and you cannot miss the two big trends: big data and comparative analyses. Recent reviews suggest that future groundbreaking, socially relevant science will be achieved through large collaborative efforts bringing together multiple datasets for a global comparison of ecological phenomena. Recently (April 2016), a group of researchers working on various topics related to life-history strategies got a taste of how open-access outputs of such collaborative efforts can be used for comparative analyses. And using the COMPADRE and COMADRE databases, together with other (see below) open-access data repositories was a big part of the experience.

Young academics from around the world met in the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany for a week-long workshop titled “Comparative Approaches in Ecology and Evolution” as part of the programme of International Advanced Studies in Demography. The course was a unique opportunity to learn how to extract information from various open-access databases. The teaching was done, via lectures and, importantly, lots of R code (!), by an impressive number of instructors – all experts in functional ecology, demography and/or phylogenetic analyses. All work had one goal: examining global drivers of life history strategies using robust statistical tools on large datasets. In the process, participants were introduced, among many others, to CLOPLA, a database on clonal traits in plants, or DATLife, providing mortality and fertility data for numerous species.

 

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Participants and instructors of the workshop “Comparative Approaches in Ecology and Evolution” organized by Rob Salguero-Gómez at the MPIDR in Rostock, Germany. Instructors included: Dr Scott Chamberlain (UC Berkeley, USA), Dr Kevin Healy (Trinity College Dublin, Ireland), Dr Owen Jones (Southern Denmark University, Denmark), Jean-François Lemaitre (CNRS, Lyon), Prof Bruce Kendall (UC Santa Barabara, USA), Prof Jitka Klimešová (Institute of Botany, Academy of Sciences of the Czech Republic), Prof Dmitrii Logofet (Russian Academy of Sciences, Moscow, Russia), Dr Alejandro Ordoñez (Aarhus University, Denmark), Dr Rob Salguero-Gomez (University of Sheffield, UK), Dr Alexander Scheuerlein (Max Planck Institute for Demographic Research), Dr Iain Stott (Max Planck Institute for Demographic Research, Germany) and Prof Jean-Michel Gaillard (CNRS, Lyon, France).

The COMPADRE Plant Matrix Database, along with COMADRE Animal Matrix Database, played a central role throughout the workshop. Of course, the fact that the core team of both databases organized the event played a part in this. At the same time, it was evident that the hard work put forth to make COM(P)ADRE easily accessible to researchers – including the ease to download the data and the detailed manuals – paid off. Working in groups, participants were encouraged to develop projects related to life-history analysis using open-access data – and several groups decided to work with COMPADRE/COMADRE. These projects included investigating environmental drivers behind demographic variability, correlating matrix projections of extinction probability with the IUCN Red List, or tracing phylogenetic signals in life-history traits. Preliminary outputs of these projects were promising – stay tuned to read about papers that will surely come out of these projects.

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The 24 participants (from a total of 20 countries!) and instructors working together on their group projects examining various aspects of life history variation among plants and animals worldwide.

To sum up, the workshop was a great success, despite the “spring weather” in Rostock, and will hopefully be repeated in the future.

Maria Paniw

PhD candidate at the Universidad de Cádiz, Spain