All of the articles in this month’s issue of Methods in Ecology and Evolution are free for the whole year. You will not need a subscription to access or download any of them throughout 2017.
Our first issue of this year contains three Applications articles and two Open Access articles. These five papers will be freely available permanently.
– CDMetaPOP: Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.
– Rphylopars: An R implementation of PhyloPars, a tool for phylogenetic imputation of missing data and estimation of trait covariance across species (phylogenetic covariance) and within species (phenotypic covariance). Rphylopars provides expanded capabilities over the original PhyloPars interface including a fast linear-time algorithm, thus allowing for extremely large data sets (which were previously computationally infeasible) to be analysed in seconds or minutes rather than hours.
– ggtree: An R package that provides programmable visualisation and annotation of phylogenetic trees. ggtree can read more tree file formats than other software and allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion.
For the first time, it is possible to integrate at the global scale the results obtained with the most widely used methods to evaluate the “health” of ecosystems using lichens. This is the result of a study now published in the journal Methods in Ecology and Evolution, and represents a fundamental step for this indicator to be considered at the global scale and included in the list of indicators of the United Nations.
Lichens have long been successfully used by scientists as ecological indicators – a kind of environment health thermometer. These complex organisms – the yellow or green taints we often see on the surface of tree trunks – are very sensitive to pollution and changes in temperature and humidity. Evaluating how many lichens, of what kind, and their abundance in a certain ecosystem allows scientists to understand the impact that problems like climate change or pollution have on those ecosystems. Continue reading →
Climate change could cause the extinction of one in six species and change the abundance and distribution of those that remain (Urban, 2015). This doesn’t necessarily mean that one in six species in your backyard will go extinct though. Climate change impacts will vary greatly around the globe, with some regions seeing disproportionate effects.
The degree to which climate change will affect species in your region depends on many factors (e.g., land use and species traits), but the amount of climate change that species experience in your region – known as climate change exposure – will certainly be important. For that reason, measuring and mapping climate change exposure is critical for predicting where climate change will have the biggest impacts. Yet, biologists have no agreed upon method to measure exposure and different methods can produce dramatically different results.
A Simple Measure of Exposure and its Limitations
Climate can be defined as a statistical description of weather (e.g., temperature, precipitation) over the course of a long time period, usually 30 years. Most often climate is reduced to the average value of a particular weather variable over a 30-year period of interest. Climate change is then measured as the difference between the averages in two time periods; say the predicted average between 2070-2099 minus the average between 1971-2000.
Projected changes in annual average temperature between 1971-2000 and 2070-2099.
For example, the map to the left shows projected exposure to changes in average annual temperature. This map suggests that species in the arctic will be exposed to the most temperature change while species in the southern hemisphere will experience the least change. However, there are many problems with this interpretation. Continue reading →
A search of almost any topic on Google Scholar promises to return tens of thousands of hits in less than a second. The first step in any research endeavour is to wade through the titanic amounts of articles available to become acquainted with the existing knowledge. For many people it’s one of the most dreadful and tedious parts of the scientific process.
But what if we could streamline/facilitate this step by automatizing parts of it? Automated content analysis (ACA) gives us the opportunity to do just that. ACA – a text-mining method that uses text-parsing and machine learning – is able to classify vast amounts of text into categories of named concepts. It can then quantify the frequency of those concepts and the relationships among them. Continue reading →
It’s not easy to characterise the local environment of species living in mountains because these habitats are highly heterogeneous. At a large scale, we typically assume that temperature varies with altitude, but at a local scale, we understand that exposure to wind or being in the shade has a great influence on climatic conditions. If you go from the south-facing to the north-facing side of a mountain, it can be easily 5°C colder. If we can feel that, so can the organisms that live up there. Plants in particular are submitted to tremendous climatic variations over a year. What we want to know is: how did they adapt to these climatic variations and how localised is their adaptation?
Overcoming the Challenges of Measuring Local Adaptation
We don’t know much about how organisms adapt locally because it’s so difficult to measure the environmental conditions that these plants are facing. Existing weather stations can’t capture micro-habitat conditions because they are few and far between. What we can do instead, is use topographic models of mountains to model their environment. After all, if orientation, slope or shade have an impact on climatic conditions, why couldn’t we use them to model local variations in temperature for example? Continue reading →
I have always loved the Blue Marble image of Earth from the Apollo 17 mission, yet a large part of my science is focused on experimental responses at the scale of meter squared grassland plots or even individual grass plants. While I spent my early career wanting to be able to say something important about regional or global processes, I found myself feeling like generating any experimental insights into processes and ecosystem responses at larger scales would be an impossible fiction.
As a postdoc, I had the opportunity to do a multi-site study across a north-south precipitation gradient in California and jumped at it. Among other questions, I decided to ask about whether plants and insects varied similarly across sites in response to replicated experimental treatments. Yet, the idea of actually sampling – and then processing samples from – more than about four sites for more than a year or two was utterly daunting. Continue reading →
To truly understand how species’ distributions vary through space and time, biogeographers often have to make use of analytical techniques from a wide array of disciplines. As such, these papers cover advances in fields such as evolutionary analysis, biodiversity definitions, species distribution modelling, remote sensing and more. They also reflect the growing understanding that biogeography can include experiments and highlight the increasing number of software packages focused towards biogeography.
This Virtual Issue was compiled by Methods in Ecology and Evolution Associate Editors Pedro Peres-Neto and Will Pearse (both of whom are involved in the conference). All of the articles in this Virtual Issue are free for a limited time and we have a little bit more information about each of the papers included here: Continue reading →
Digital photography has revolutionised the way we view ourselves, each other and our environment. The use of automated cameras (including camera traps) in particular has provided remarkable opportunities for biological research. Although mostly used for recreational purposes, the development of user-friendly, versatile auto-focus digital single lens reflex (DSLR) cameras allows researchers to collect large numbers of high quality images at relatively little cost.
It’s somehow fitting that the centre piece of an ancient midwinter tradition in Europe – that of decorating and worshipping an evergreen tree – is an ancient seed plant, a conifer. In Europe, we tend to think of conifers as “Christmas trees” – evergreen trees with needles and dry cones, restricted to cold and dry environments – but conifers are much more diverse and widespread than that. There are broad-leaved, tropical conifers with fleshy cones and even a parasitic species that is thought to parasitise on members of its own family!
However, while today’s distribution of conifers is global – spanning tropical, temperate and boreal zones – it is fragmented. The conifer fossil record extends well into the Carboniferous and bears witness to a lineage that was once much more abundant, widespread and diverse. So we can tell that today’s diversity and distribution have been shaped by hundreds of millions of years of speciation, extinction and migration. Continue reading →
This month’s issue contains four Applications articles and two Open Access articles, all of which are freely available.
– iNEXT: The R package iNEXT (iNterpolation/EXTrapolation) provides simple functions to compute and plot the seamless rarefaction and extrapolation sampling curves for the three most widely used members of the Hill number family (species richness, Shannon diversity and Simpson diversity).
– camtrapR: A new toolbox for flexible and efficient management of data generated in camera trap-based wildlife studies. The package implements a complete workflow for processing camera trapping data.
– rotl: An R package to search and download data from the Open Tree of Life directly in R. It uses common data structures allowing researchers to take advantage of the rich set of tools and methods that are available in R to manipulate, analyse and visualize phylogenies.
– Fluctuating-temperature chamber: A design for economical, programmable fluctuating-temperature chambers based on a relatively small commercially manufactured constant temperature chamber modified with a customized, user-friendly microcontroller.