Tag Archives: species distribution modelling
Integrating dispersal along freshwater systems in species distribution models (2020) Perrin et. al., Diversity & Distributions, https://doi.org/10.1111/ddi.13112
Trying to figure out where a species can comfortably live is one thing, but figuring out which habitats they can actually access is another. I like to think most marsupials would do quite well in South America or Africa, but the fact is that they’re not dispersing across the Atlantic or Pacific anytime soon. However a Species Distribution Model (a statistical model that can be used to predict the likelihood of a species being found somewhere) often requires a more nuanced approach than “big ocean separating these two habitats”.
To integrate a species’ ability to actually access an area into a Species Distributions Model (SDM), we often use the concept of connectivity. Often, this means simply measuring the distance between two populations. But sometimes a species ability to disperse might not reflect something as simple as how far it needs to go. A perfectly good habitat might be only 100 metres away, but cut off by a raging great cliff. Or a road.
In this study, we wanted to see whether we could relate connectivity parameters used in an SDM to the actual ability of the species to disperse.Read more
Image Credit: Pharexia, Ratherous, AKS471883, Source Data from Johns Hopkins University CSSE, The Centers for Disease Control and Prevention, New York Times, CNBC.
As it quickly became clear in late February and early March that COVID-19 was not going away anytime soon, attention turned to trying to figure out when and where the virus would spread. Epidemiologists and virologists have had their work cut out for them, trying to simultaneously reassure and warn people the world over about the dangers, the nature and the potential timeline of the virus.
So it came as somewhat of a surprise to see ecologists try and tip their hat into the ring. Early on in the pandemic, teams of ecologists sprang up, trying to use Species Distribution Models to predict the spread of the virus. And whilst this might sound helpful, many of these studies lacked collaboration with epidemiologists, and their predictions very quickly fell flat. Some studies suggested that areas like Brazil and Central Africa would be largely spared by the virus, which quickly turned out not to be the case. Flaws in the studies were spotted quite quickly by concerned members of both the ecological and epidemiological communities alike, and a few teams got started on responses.