Tag Archives: modelling
When dealing with complicated ecological concepts, theoretical models – though they may seem abstract – often help create bridges to fill in our understanding, writes Thomas Haaland (Image Credit: Aga Khan, CC BY-SA 4.0, Image Cropped)
It should not come as a surprise any more that most ecologists don’t spend all that much (work) time outside. Numerous posts on this blog about data management and ecological modelling draw a picture of a modern biologist spending most of their time in front of a computer rather than out in the field. However, the work is still intimately related to the natural world. Gathering the data is simply the first step on the way to scientific understanding, and between organizing data, analyzing data, interpreting results and writing them up, the computer hours just vastly outweigh the outdoor hours. But there is another, more mysterious breed of researchers that has even less to do with nature: theoretical biologists.
Community ecology, as a relatively new discipline, is fraught with challenges. Here, we look at why an hour spent talking about those challenges may make you feel like the PhD student pictured above (Image Credit: Lau Svensson, CC BY 2.0, Image Cropped)
Anyone who has forayed any small distance into academia will probably understand the following quote by Aristotle.
“The more you know, the more you realize you don’t know.”
According to Stewart Lee, participating in further education means embarking on a “quest to enlarge the global storehouse of all human understanding”. This might be true, yet venturing into academia also means that the more answers you learn to challenging scientific questions, the more questions get opened up. It’s the circle of academic life.
When animals like these wolves travel in packs, spotting one individual means we’re more likely to spot another soon after. So how do we come up with a reliable population estimate in situations like these? (Image Credit: Eric Kilby, CC BY-SA 2.0, Image Cropped)
The thought of an ecologist may conjure the image of a scientist spending their time out in the field counting birds, looking for moss, studying mushrooms. Yet whilst field ecologists remain an integral part of modern ecology, the reality is that much of the discipline has come to rely on complex models. These are the processes which allow us to estimate figures like the 1 billion animals that have died in the recent Australian bushfires, or the potential spread of species further polewards as climate change warms our planet.
Bill Sutherland was one of two keynote speakers in last week’s seminar on biodiversity and ecosystem services (Image Credit: Øystein Kielland, NTNU University Museum, CC BY 2.0)
I’ve been on a bit of a policy trip lately. The latest Norwegian Ecological Society conference was heavily policy based, so much so that it inspired me to get in touch and set up a meeting with local freshwater managers in a country in which I do not speak the local language. So when the CBD hosted a one-day seminar on the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (mercifully usually referred to only as IPBES) rolled into town, I was right on board.
Occupancy models for citizen-science data (2018) Altwegg & Nichols, Advances in Modelling Demographic Processes, 10, p. 8-21
Species distributions maps are great. I remember rifling through animal encyclopedias as a kid, checking out the distributions of my favourite animals, just assuming that people knew exactly where to find all these organisms. But the reality is that figuring out exactly where species live is extremely difficult.
It’s made easier, however, by the use of citizen (or community) science. This occurs when volunteers involve themselves in projects in which they observe and report the presence or absence of a species in a given area, which is then used to determine a species’ distribution. This data is obviously incredibly useful to any ecologist, but it comes with some drawbacks. This paper attempts to summarise those drawbacks and outline ways to work around them.
In the latest edition of our ongoing look at how ecology has changed over the last half-century, 5 experts talk technology, modelling, and the study of humans. But we also cover some of the pitfalls of recent leaps forward, including the loss of appreciation for species physiology.
You can also check out parts one, two, and our special on fish ecology.
One of the timeless (get it?) questions in biology is why did we evolve to age? What benefit is there to getting older and deteriorating before we die? (Image Credit: medienluemmel, Pixabay licence, Image Cropped)
Evolution favours aging in populations with assortative mating and in sexully dimorphic populations (2018) Lenart, P. et al., Scientific Reports, 8, https://doi.org/10.1038/s41598-018-34391-x
We as humans are familiar with aging as the slow deterioration of our bodies and minds over time, and we can see this in other animals as well (think of the old family dog with white around its muzzle). The interesting thing is that not every species ages in the way that we do, that is to say that they stay forever “young” until they die. In a biological sense that means that while these organisms can and do die, their risk of death remains the same throughout the course of their lives. This would be akin to your grandparents, in their old age, having the same risk of death as you during the prime of your life. Or, conversely, you being just as likely to die in your sleep as a senior citizen.
The authors of this study note that, while theories for the evolution of aging abound in the scientific literature, they are not broadly applicable and some of them even require the existence of aging for the evolution of aging to even happen. They wanted to find out in what situations aging individuals could outcompete non-aging individuals, and vice-versa.
Image Credit: Sam Perrin, CC BY-SA 2.0, Image Cropped
As a fish ecologist living in Norway, it’s a joy to be able to travel to Melbourne and interact with the people that are driving forward fish science in my home country. So when I found out that the Australian Society of Fish Biology’s annual conference was taking place 3 days after my first flight home since 2016, I knew it was an opportunity I couldn’t pass up.
We’re on the last day of the conference at the moment, and over the next 2 months I’m looking forward to bringing you a number of insights, including interviews with guest speakers Eva Plaganyi and Gretta Pecl and pioneers of intriguing projects like Peter Unmack and Jarod Lyon. I’ll also have a fish edition of The Changing Face of Ecology, and some articles on how the angling community and the fish science community interact in a country with one of the most unique fish assemblages in the world.
Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context (2017) Tikhonov et al, Methods in Ecology and Evolution, DOI: https://doi.org/10.1111/2041-210X.12723
Statistical modelling is a crucial part of ecology. Being able to provide an (admittedly simplified) mathematical description of the relationship between species abundance, range or density and the surrounding environment is a huge help in taking proactive steps to manage an ecosystem, or predicting species numbers in other areas.
Historically models have used environmental variables to explain population or evolutionary developments in species. When modelling a single species, many ecologists have taken into account that the presence of other species (for example competitors or predators) may influence the presence of this single species. This has led to the rise of joint species distribution models (JSDMs), which take into account environmental variables, as well as the interactions between certain species. These models have become increasingly useful, and with environmental change now being the norm in many ecosystems, this week’s authors produced one such model that accounts for changes in species interactions in the face of changing environmental factors.