Defining macroecology should be easy; it’s just ecology at large spatial scales, right? In reality though, it’s a little more complex than that. No-one agrees on exactly how large the spatial scale should be, and many studies that could be macroecology may also be defined as biogeography, landscape ecology, community ecology etc. Working at large spatial scales can also mean working at large temporal scales, often in deep-time. So there’s a lot of overlap with studies of macroevolution both on living and extinct species too.
This breadth of definitions means the BES Macroecology Special Interest Group (or BES Macro as we usually call it) has members with interests across ecology, evolution and palaeontology. Probably the most common statement at any of our events is “I’m not a macroecologist but…”. So, if you’re interested in broad-scale ecology and evolution, in a living or palaeo context, the SIG is for you, even if you don’t identify as a macroecologist! Continue reading →
In reality, code is often poorly commented (or not commented at all!), hard to reuse for other projects, and difficult to interpret. To add to that, most code isn’t actively maintained, so users are on their own if they try to commandeer it for new purposes. Further, ecologists with little or no programming knowledge are unlikely to benefit from methods that exist only as poorly documented code. In a positive development, some new methods are accessible through software with graphic user interfaces (GUIs) developed by programmers spending significant time and effort. But too often these end up as tools with flashy controls and insufficient instruction manuals. Continue reading →
Happy New Year! We hope that you all had a wonderful Winter Break and that you’re ready to start 2018. We’re beginning the year with a look back at some of our highlights of 2017. Here’s how last year looked at Methods in Ecology and Evolution.
We published some amazing articles in 2017, too many to mention them all here. However, we would like to take a moment to thank all of the Authors, Reviewers and Editors who contributed to the journal last year. Your time and effort make the journal what it is and we are incredibly grateful. THANK YOU for all of your hard work!
Technological Advances at the Interface between Ecology and Statistics
Our first Special Feature of the year came in the April issue of the journal. The idea forTechnological Advances at the Interface between Ecology and Statistics came from the 2015 Eco-Stats Symposium at the University of New South Wales and the feature was guest edited by Associate Editor David Warton. It consists of five articles based on talks from that conference and shows how interdisciplinary collaboration help to solve problems around estimating biodiversity and how it changes over space and time.
How do we know how many fish there are in the ocean? 1000, 1 billion, 1000 billion? We can’t catch them all and count – that’s not practical. Nor can we make observations from Earth-orbiting satellites – light does not penetrate far into the ocean. What we can use is sound.
Sound travels well in water (faster and further than it does in air), so we can use scientific SONAR (echosounders) to produce sound waves and record backscatter from organisms and communities. This provides information concerning their biomass, distribution and behaviour. A recent study used echoes from the mesopelagic zone (200 – 1,000m) to predict global mesopelagic fish biomass to be between 11 and 15 billion tonnes (that’s a lot), suggesting that mesopelagic fish communities could potentially provide global food security.
10 mesopelagic classes are shown for the open-ocean, echo intensity (a proxy for biomass) increases from blue to red. Coastal zones excluded. Longhurst provinces overlaid. Shapefile here. Proud et al. (2017)
An Asian, female Senior Editor under 45? Progressive! I have loved Methods in Ecology and Evolution since it appeared in 2010 and am thrilled to have been selected to join Rob, Bob and Jana to help with the journal’s continued development.
OK, so you want to know who the new Senior Editor on the MEE block is. I’m just another scientist, I guess. On the outside, we look different but on the inside, we’re all the same. (OK, perhaps we are a little different, even on the inside, but that makes life and research interesting, right?)
Here’s my academic life history: I did my Bachelors thesis on the systematics/phylogenetics of an obscure group of marine pulmonate slugs with one of the greatest Icelandic biologists I know, Jon Sigurdsson, at the National University of Singapore. I followed this up with an almost-half-year stint at the Museum of Natural Science in Berlin as a “nobody”, digitizing data. Then I won the academic lottery and headed up to Uppsala to do my masters in conservation biology on tropical pollinator diversity, (un)supervised by two amazing supervisors that never met each other, the late Navjot Sodhi (National University of Singapore) and Thomas Elmqvist, now at Stockholm University. Continue reading →
Evelyn Chrystalla ‘E.C.’ Pielou (February 20, 1924 – July 16, 2016) – a towering figure in ecology – was a key pioneer in the incorporation of statistical rigor into biogeography and ecology. She devised many important statistical hypotheses tests for spatial arrangements and patterns ranging in scale from individual plants in a field through to elevational zonation of vegetation to ranges of groups of species distributed over regional through to continental-scale ranges. Her research has provided the impetus for biogeographical analyses for generations.
She published ten books, including several long after her formal retirement in 1988. Her book Biogeography (1979) is a masterpiece. It covers historical biogeography (including inferences from cladograms, which were just beginning to be a hot topic at that time) and ecological biogeography with keen insight and treats topics like long-distance dispersal (that had largely been the subject of just-so stories) with her characteristic statistical rigor. Her books on mathematical ecology have a strong emphasis on models of spatial pattern and ways to estimate biodiversity, and her methods – including the famous Pielou‘s evenness index – are still widely used.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 →
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 →