Editor Recommendation: Lianas and Soil Nutrients Predict Fine-Scale Distribution of Above-Ground Biomass in a Tropical Moist Forest

Post provided by Laura Graham



Datasets used by quantitative ecologists are getting more and more complex. So we need more complex models, such as hierarchical and complex spatial models. Typically, Bayesian approaches such as Markov chain Monte Carlo have been used. But these methods can be slow, making it infeasible to fit some models.

New developments in Integrated nested Laplace approximation (INLA) have made some of these complex models much faster to fit. Dedicated R packages (R-INLA and inlabru) make coding these Bayesian models much more straightforward. Also, INLA lets you fit of a class of models which allow for computationally efficient and flexible modelling of spatial data. Continue reading


Editor Recommendation: Assessing Strengths and Weaknesses of DNA Metabarcoding-Based Macroinvertebrate Identification for Routine Stream Monitoring

Post provided by Andrew R. Mahon

The use of molecular methods for monitoring and surveillance of organisms in aquatic and marine systems has become more and more common. We’ve since expanded this technology this through using both captured whole organisms and collecting/filtering environmental DNA (eDNA).  These methods naturally migrated from single species, active surveillance methods towards using high throughput sequencing as a method of passive surveillance via metabarcoding.

In this virtual issue, the article “Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring” by Vasco Elbrecht et al. provides an excellent overview to the field. It also helps to clarify the work being done to provide interested groups, including management agencies, with the best practices for utilising these new methods for monitoring and surveillance.  This work will help the field, particularly for those searching for rare species of organisms in aquatic systems.

I’d recommend this paper to all researchers and management groups interested in applying metabarcoding techniques to answer both experimental and applied questions. The design of this article will provide both experienced researchers and those new to the field with important information to further this rapidly expanding field.

To find out more about, read the full Methods in Ecology and Evolution article ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring

 This article is part of ‘Practical Tools: A Field Methods Virtual Issue’. All articles in this Virtual Issue will be available for a limited time.

Editor Recommendation: A Practical Guide to Structured Expert Elicitation Using the IDEA Protocol

Post provided by Barbara Anderson

Today is International Women’s Day to mark the occasion I have the privilege of recommending, ‘A practical guide to structured expert elicitation using the IDEA protocol by Victoria Hemming et al. The IDEA behind the IDEA protocol – ‘Investigate’, ‘Discuss’, ‘Estimate’ and ‘Aggregate’ – is to provide a framework for Structured Expert Elicitation.

As a quantitative ecologist, I sometimes attempt to model species’ abundance and distribution changes in response to environmental change. Often these are species that, for one reason or another, we know a lot about. They may be high profile species of conservation concern, or have some economic or cultural importance. Some are simply model species that many people have studied because they’re easy to study because many people have studied them. Just as often though, we’re missing crucial data on one or more parameters. Frustratingly we don’t always have the time or resources to collect the new ecological or biological data required. Continue reading

Editor Recommendation: A Multi-State Species Distribution Modelling Framework for Species Using Distinct Habitats

Post provided by Jana McPherson

© Amélie Augé

© Amélie Augé

Correlative distribution models have become essential tools in conservation, macroecology and ecology more generally. They help turn limited occurrence records into predictive maps that help us get a better sense of where species might be found, which areas might be critical for their protection, how large their range currently is, and how it might change with climate change, urban encroachment or other forms of habitat conversion.

It can be frustrating, however, when species distribution models (and the predictive maps they produce) don’t adequately capture what we already know about the habitat needs of a species. A major challenge to date has been to represent the environmental needs of species that require distinct habitats during different life stages or behavioural states. Rainbow parrotfish (Scarus guacamaia), for example, spend their youth sheltered from predators in mangrove areas before moving onto coral reefs, and European nightjars (Caprimulgus europaeus) breed in heathland but require access to grazed grassland for foraging. Correlative distribution models confronted with occurrence records from both life stages or behavioural modes tend to produce poor predictive maps because they confound these distinct requirements. Continue reading

Editor Recommendation: How Do Trait Distributions Differ Across Species and Their Environments?

Post provided by Pedro Peres-Neto

The rise of trait ecology led to many quantitative frameworks to understand the underlying rules that determine how species are assembled into local communities from regional pools. Ecologists are interested in understanding whether environmental features select for particular traits that optimise local fitness and regulate species co-existence.

In ‘Assessing the joint behaviour of species traits as filtered by environment’, Erin Schliep and her co-authors aimed to develop a joint probabilistic model under a Bayesian framework to help explain the correlations among traits and how trait distributions differ across species and their environments. The end product is a model of trait-environmental relationships that takes full advantage of information on intra- and interspecific variation typically found within and among species.  Continue reading

Editor recommendation: Predicting Animal Behaviour Using Deep Learning

Post provided by Jana McPherson

Common guillemots were one of the species used in this study. ©Richard Crossley

Common guillemots were one of the species used in this study. ©Richard Crossley

Understanding key habitat requirements is critical to the conservation of species at risk. For highly mobile species, discerning what is key habitat as opposed to areas that are simply being traversed (perhaps in the search for key habitats) can be challenging. For seabirds, in particular, it can be difficult to know which areas in the sea represent key foraging grounds. Devices that record birds’ diving behaviour can help shed light on this, but they’re expensive to deploy. In contrast, devices that record the birds’ geographic position are more commonly available and have been around for some time.

In their recent study entitled ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds,’ Ella Browning and her colleagues made use of a rich dataset on 399 individual birds from three species, some equipped with both global positioning (GPS) and depth recorder devices, others with GPS only. The data allowed them to test whether deep learning methods can identify when the birds are diving (foraging) based on GPS data alone. Results were highly promising, with top models able to distinguish non-diving and diving behaviours with 94% and 80% accuracy. Continue reading

Editor Recommendation – HistMapR: Rapid Digitization of Historical Land-Use Maps in R

Post provided by Sarah Goslee

For an ecologist interested in long-term dynamics, one of the most thrilling experiences is discovering a legacy dataset stashed away somewhere.

For an ecologist interested in long-term dynamics, one of the most daunting experiences is figuring how to turn that box full of paper into usable data.

The new tool HistMapR, described in ’HistMapR: Rapid digitization of historical land-use maps in R’ by Alistair Auffret and colleagues, makes one part of that task much easier.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Historical maps with coloured areas denoting different land cover or use are a valuable record, but difficult to analyse. This R package automates much of the time-consuming and tedious process of turning paper maps into classified categorical raster maps.

A map is scanned, imported into R, and the software is trained by clicking in different areas of each category. It then automatically classifies pixels based on which colour they are most similar to. The resulting classification is assessed manually. The process can be repeated with slightly different parameters until a good fit is achieved.

The authors found 80-90% agreement between HistMapR classification and manual digitisation (sources of error included clarity of original maps and scan quality). Using HistMapR reduced the time needed for digitising a series of historical land cover maps from two months to two days. Ecologists interested in long-term dynamics should be cheering!

The HistMapR package is available on GitHub and you can find example scripts on Figshare, so you can get right to work.

HistMapR: Rapid digitization of historical land-use maps in R‘ by Auffret et al. is a freely available Applications article (no subscription required).