Predicting how climate change threatens the prey base of Arctic marine predators, Florko et al., 2021 Ecology Letters. https://doi.org/10.1111/ele.13866
Image credit: Kingfisher, CC BY-SA 3.0
We are all (unfortunately) very familiar with the effects of climate change on arctic ecosystems. Horrifying images of polar bears on small blocks of ice and the shrinking polar ice caps are but two of the many results of a warming climate, yet a great deal of the work in the realm has focused on the the charismatic, apex species (like the aforementioned polar bear). These are obviously important things to consider, but it is also necessary to look into the effects of climate change on the lower positions within food webs, as any change to these organisms and processes are likely to cascade upwards to effect the upper trophic levels (like our friend the polar bear).
Hudson Bay in North America is one such area impacted by our warming climate. Due to the changes in temperatures, the energy flowing through ecosystems has shifted away from away from species living in the ice and on the bottom. As a result pelagic (free-swimming) species are favored over benthic species (those living on the bottom of the bay), which alters the rest of the food web itself. Specifically, the fish that feed on pelagic species are increasing, while those that feed on benthic species are decreasing. Today’s authors wanted to understand how these changes in fish numbers are will affect Arctic predators, namely the ringed seal (Pusa hispida).
If we write about our statistical methods behind our ecology work, and none of our readers understand it, have we really communicated at all?
This month I’m getting meta. It’s been about a year and a half since I started writing the Stats Corner for this blog with the goal of demystifying some of the statistical methods that are used by ecologists every day. At the same time, I’ve been writing a book with Deborah Nolan called “Communicating with Data: The Art of Writing for Data Science.” The book was released this spring, so it seemed like a good time to reflect on writing about statistics accessibly.
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.
Community, or citizen, science is a huge, often untapped data source for ecologists. So what are the pitfalls of using it? (Image Credit: Jacob W. Frank, NPS, Public Domain Mark 1.0, Image Cropped)
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.
Image Credit: Tumisu, Pixabay licence, Image Cropped
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.