Stage-dependent Demographic Modelling at Your Finger Tips

Post provided by EELKE JONGEJANS and ROB SALGUERO- GÓMEZ

Soay sheep: an organism that can be modelled with two-sex dynamics. ©Julian Paren

Soay sheep: an organism that can be modelled with two-sex dynamics. ©Julian Paren

Typically, ecology courses contain at least a day of matrix population models. So most ecologists are somewhat familiar with how simple life cycles (and complex ones) can be depicted and analysed using matrix models. Briefly, these models represent what happens to individuals over a certain time interval (do they die? do they reproduce? if so, how much?). What individuals do in the context of these models can then be used to study the dynamics of a population.

Often, individuals are classified by size in matrix models, as small individuals tend to have different survival, growth and reproduction rates than large ones. But how many classes do you need to model the dynamics of a size-structured population properly? Instead of choosing arbitrary size class boundaries, Easterling, Ellner and Dixon (2000) came up with the idea of using continuous size variables and integrals to define a population model… and that’s how the first Integral Projection Model (‘IPM’ for us friends) came to be.

Naturally, for the development of a new demographic tool to prove useful to the scientific community, it must be flexible enough to be ‘one-size-fits-all’… and the needs of ecologists, evolutionary biologists and conservation biologists – who have to date used extensively size-based matrix models – are rather variable in size, colour and shape. Continue reading

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Demography and Big Data

Post provided by BRITTANY TELLER, KRISTIN HULVEY and ELISE GORNISH

Follow Brittany (@BRITTZINATOR) and Elise (@RESTORECAL) on Twitter

To understand how species survive in nature, demographers pair field-collected life history data on survival, growth and reproduction with statistical inference. Demographic approaches have significantly contributed to our understanding of population biology, invasive species dynamics, community ecology, evolutionary biology and much more.

As ecologists begin to ask questions about demography at broader spatial and temporal scales and collect data at higher resolutions, demographic analyses and new statistical methods are likely to shed even more light on important ecological mechanisms.

Population Processes

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Traditionally, demographers collect life history data on species in the field under one or more environmental conditions. This approach has significantly improved our understanding of basic biological processes. For example, rosette size is a significant predictor of survival for plants like wild teasel (Werner 1975 – links to all articles are at the end of the post), and desert annual plants hedge their bets against poor years by optimizing germination strategies (Gremer & Venable 2014).

Demographers also include temporal and spatial variability in their models to help make realistic predictions of population dynamics. We now know that temporal variability in carrying capacity dramatically improves population growth rates for perennial grasses and provides a better fit to data than models with varying growth rates because of this (Fowler & Pease 2010). Moreover, spatial heterogeneity and environmental stochasticity have similar consequences for plant populations (Crone 2016). Continue reading

Issue 7.2: Demography Beyond the Population

Issue 7.2 is now online!

Sagebrush steppe in eastern Idaho, USA

© Brittany J. Teller

The February issue of Methods is now online! As you may have seen already, it includes the BES cross-journal Special Feature: “Demography Beyond the Population“. There are also eight other wonderful articles to read.

We have four articles in the Demography Beyond the Symposium Special Feature. You can read an overview of them by two of the Feature’s Guest Editor Sean McMahon and Jessica Metcalf here (Sean and Jessica are also Associate Editors of Methods).

If you’d like to find out more about each of the individual papers before downloading them, we have blog posts about each one. Daniel Falster and Rich Fitzjohn discuss the development of plant and provide some advice on creating simulation models in Key Technologies Used to Build the plant Package (and Maybe Soon Some Other Big Simulation Models in R). There is a look back at the evolution of Integral Projection Models from Mark Rees and Steve Ellner in How Did We Get Here From There? A Brief History of Evolving Integral Projection Models. In Inverse Modelling and IPMs: Estimating Processes from Incomplete Information Edgar González explains how you can estimate process that you can’t observe. And keep an eye out for Brittany Teller’s blog post coming next week!

Don’t wait too long to get the Demography Beyond the Population Special Feature papers though, they’re freely available for a limited time only

Continue reading

Inverse Modelling and IPMs: Estimating Processes from Incomplete Information

Post provided by Edgar J. González

In demography, a set of processes (survival, growth, fecundity, etc.) interacts to produce observable patterns (population size, structure, growth rate, etc.) that change over time. With traditional approaches you follow the individuals of a population over some timespan and track all of these processes.

Demographic patterns and processes (Click to expand)

Demographic patterns and processes (Click to expand)

However, depending on the organism, some processes may be very hard to quantify (e.g. mortality or recruitment in animals or plants with long lifespans). You may have observed the patterns for the organism that you’re studying and, even better, measured some, but not all, of the processes. The question is: can we use this limited information to estimate the processes we couldn’t measure? Continue reading

How Did We Get Here From There? A Brief History of Evolving Integral Projection Models

Post provided by MARK REES and Steve Ellner

The Early Days: Illyrian Thistle and IBMs

Illyrian Thistle

Illyrian Thistle

Back in 1997 MR was awarded a travel grant from CSIRO to visit Andy Sheppard in Canberra. CSIRO had been collecting detailed long-term demographic data on several plant species and Andy was keen to develop data-driven models for management.

Andy decided Illyrian thistle (Onopordum Illyricum) would be a good place to start, as this was the most complicated in terms of its demography. The field study provided information on size, age and seed production. The initial goal was to quantify the impact of seed feeders on plant abundance, but after a few weeks of data analysis it became apparent that the annual seed production per quadrat was huge (in the 1000s) but there were always ~20 or so recruits. This meant that effects of seed feeders (if any) occurred outside the range of the data, which wasn’t ideal for quantitative prediction.

So the project developed in a different direction. Onopordum is a monocarpic perennial (it lives for several years then flowers and dies) and Tom de Jong and Peter Klinkhamer had recently developed models to predict at what size or age monocarps should flower, so it seemed reasonable to see if this would work. Continue reading

Key Technologies Used to Build the plant Package (and Maybe Soon Some Other Big Simulation Models in R)

Post provided by RICH FITZJOHN and DANIEL FALSTER

Our paper in Methods in Ecology and Evolution describes a new software package, plantplant is an individual-based simulation model that simulates the growth of individual trees, stands of competing plants, or entire metacommunities under a disturbance regime, using common physiological rules and trait-based functional trade-offs to capture differences among species.

Non-Linear Processes and Thousands of Plants

Since the development of gap models in the 1970s (e.g. Botkin 1972), researchers have been using computer simulations to investigate how elements of plant biology interact with competition and disturbance regimes to influence vegetation demography, structure and diversity. Simulating the competitive interactions among many thousands of plants, however, is no easy task.

Despite widespread recognition of the importance of key non-linear processes — such as size-structured competition, disturbance, and trait-based trade-offs — for vegetation dynamics, relatively few researchers have been brave (or daft) enough to try and incorporate such processes into their models. The situation is most extreme in theoretical ecology, where much contemporary theory (e.g. coexistence theory, neutral theory) is still built around completely unstructured populations.

Features of plant

Key processes modelled within the plant package.

Key processes modelled within the plant package.

The plant package attempts to change that by providing an extensible, open source framework for studying trait-, size- and patch-structured dynamics. One thing that makes the plant model significant is the focus on traits. plant is one of several attempts seeking to integrate current understanding about trait based trade-offs into a model of individual plant function (see also Moorcroft et al 2001Sakschewski et al 2015).

A second feature that makes the plant software significant, is it that is perhaps the first example where a computationally intensive model has been packaged up in a way that enables widespread usage, makes the model more usable and doesn’t  sacrifice speed.

In this post we will describe the key technologies used to build the plant software. Continue reading

Methods Beyond the Population

Post Provided by SEAN MCMAHON and JESSICA METCALF

Demography Beyond the Population” is a unique Special Feature being published across the journals of the British Ecological Society.  The effort evolved from a symposium of the same name hosted in Sheffield, UK last March. Both the meeting and the Special Feature were designed to challenge ecologists from a range of fields whose research focuses on populations.

The participants were charged with sharing how they are pushing the work they do beyond the stage where the population is the focus into research where the population is just the beginning and the focus spans scales, systems and tools. This encompasses a broad suite of biological research, including range modelling, disease impacts on communities, biogeochemistry, evolutionary theory, and conservation biology. The meeting was a great success, and this Special Feature should be equally valuable to the broad readership of the BES journals.

Methods in Ecology and Evolution has a special place in the Special Feature, hosting four papers. These papers not only introduce new efforts in population biology, they provide the methods that other scientists can use to implement them. With the tools provided by these four papers, researchers will be able to advance forest modelling, evolutionary theory, climate change biology and statistical inference of hidden population parameters.  Seriously good stuff! Continue reading

Demography Beyond the Population Webinar: Register for Free Now

Webinar logoRegister for FREE for the first ever BES Publishing webinar based on our forthcoming Demography Beyond the Population Special Feature.

This hour long webinar will begin at 1pm (GMT) on Tuesday 1 March. It highlights some of the excellent articles soon to be published in the British Ecological Society journals Special Feature entitled “Demography Beyond the Population”. The Special Feature is a collaborative effort including articles in all six BES journals. This is the first time such a large ecological collaboration has been attempted worldwide. Using a cross-journal approach has allowed us to highlight the strongly interdisciplinary nature of the field of demography to its fullest potential as well as to lay down the foundations for future directions at the interface of ecology, evolution, conservation biology and human welfare. The webinar has several international speakers and will discuss the articles in the Special Feature and the implications for demography research going forward. Continue reading