Ecology is all about understanding how biotic and abiotic factors interact within environments. Biotic factors are those that involve living organisms such as prey availability/resource abundance (i.e., the availability of food and resources?), competitor density, or predator density. Abiotic factors, however, are those that involve non-living aspects of the environment, such as rainfall or temperature. Studying how these various factors interact with one another allows researchers to better understand how and why ecological dynamics vary across a changing landscape.
One really cool thing about ecological dynamics is that they can play out across trophic levels, meaning something happening at the level of the resource (such as grass) can then result in changes at a higher trophic level, such as that of the consumer (deer) or predator (wolf). While there has been an enormous amount of work dedicated to understanding how these species interactions affect the species involved, much less is known about how these dynamics play out across a natural landscape. Today’s authors used a well-known model system (see Did You Know?) to study just that.
A green on green conflict is what occurs when forms of renewable energy can have a potentially negative effect on the local environment. We see it in hydropower disrupting freshwater fish populations, or in the case of today’s paper, wind farms causing bird deaths. Marine shorebirds are often killed by wind turbines, yet it’s not totally clear to what extent population numbers are impacted by these deaths.
Additionally, whether wind farms are more dangerous to male or female, old or young birds could have a big impact on whether these bird deaths affect population numbers in the future. Today’s authors wanted to investigate this question, using a population of northern gannets off the coast of Scotland.
Diseases that jump from other animals to humans, or zoonotic diseases (see Did You Know?) have become something that all of us are now very familiar with. COVID-19 is one such disease, and the impact it has had on the world as a whole is all the evidence that anyone could ever need for understanding why it is important to know where these diseases come from. Classically, specific groups of animals have been thought to act as reservoirs for the viruses that cause these diseases. Take rabies, for example. This is the disease that results in rabid animals, but you may not know that bats act as a reservoir for rabies, meaning that the rabies virus survives within bat populations and can be spread by them.
This is known as the “special reservoir hypothesis”, and it posits that there are certain traits associated with these reservoir species and/or their ecology that make them more likely to act as reservoirs for these viruses. In contrast, it could be that all animal species are equally likely to act as a reservoir for zoonotic viruses, and the risk of virus transmission is instead due to how many host species are within a given group of animal hosts. All this means is that you expect to find more diverse groups of animals hosting a more diverse group of viruses. This is known as the “reservoir richness hypothesis”.
In order to better manage zoonotic disease emergence and even predict where it is likely to occur in the future, it is important to understand if there are indeed special reservoirs among animal hosts, or if disease emergence is instead a consequence of host species richness. Today’s authors utilized data on zoonotic viruses and host species to understand this relationship.
Communicating the importance of restoring biodiversity and fighting against climate change is particularly crucial in a world where facts can be so easily distorted. Misinformation and fake news can be easily spread through social media and other online outlets, but the same outlets could also provide effective means of communication for scientific research. However there’s still a lot of work to be done figuring out how to use these new tools, and today’s paper looks at some of the pitfalls involved.
NB: This paper is very well-written, and it’s definitely worth your time to read the whole thing. It’s not open access, but if you get in touch with the authors I’m sure they’ll be more than happy to send out a copy.
The Fine Line of SciComm
We have a pretty solid idea now of the fact that scientific communication needs to be both engaging and factual, yet scientists often forget one of the two. The authors bring up the recent ‘insectageddon‘ paper, a piece of scientific literature which was widely circulated in the media but made claims on a global scale which the data didn’t really support. While it undoubtedly alerted many people worldwide to a serious problem, the dishonest communication employed could potentially damage people’s trust in science.
Humour is a fantastic form of engaging scientific communication, which can (albeit rarely) be used in scientific literature. For a great example, check out the two papers below.
A Final Warning to Planet Earth features the fantastic line “[w]e therefore strongly oppose the agenda accompanying the warning to humanity and will not tolerate any obstacle to our way of life – be it tree-huggers or the trees themselves.”
However these carry with them dangers. We don’t expect scientific papers to be sarcastic, so it’s not a huge surprise when the authors point out that the first of these papers has already been cited as if it is a serious publication.
More worrying is the second example today’s authors present. A satirical paper by Leonard Leibovici made the claim that praying for someone’s recovery 4-10 years AFTER their hospitalisation was effective. The paper is obviously a joke, but it has been cited often by religious groups as proof of the power of prayer.
I chose to review this article because it encapsulates some of the frustrations I wrote about last week. Funny and engaging scientific communication should not be shied away from. Using humour and other more personal forms of communication humanises scientists and can engender more trust in us. It’s why I started a podcast looking at the biology of movie monsters. And there are plenty of scientists out there using humour to great effect.
Yet there are certain aspects of the way scientists communicate information – chief among them scientific articles – that are so rigid and inflexible that any novel approaches to them come with pitfalls attached. I reiterate my hope from last week that we’ll be able to change this going forward.
Dr. Sam Perrin is a freshwater ecologist and climate data analyst who completed his PhD at the Norwegian University of Science and Technology. You can read more about his research and the rest of the Ecology for the Masses writers here, see more of his work at Ecology for the Masses here, or follow him on Twitter here.
A few months ago I was riding high off having handed in my PhD thesis. Having handed in said thesis and submitted all relevant manuscripts, I could relax for a bit, and just enjoy maintaining the blog and doing some defense preparation. I also had been asked to review a paper for a journal, a request I gladly accepted.
Invader-pollinator paradox: Invasive goldenrods benefit from large-size pollinators (2021) Moroń, et al., Diversity and Distributions, https://doi.org/10.1111/ddi.13221
A plant that invades a new part of the world can’t necessarily bring its regular pollinators along with it. So it stands to reason that plants who successfully invade a new area receives pollination from native pollinators. Seems pretty straightforward, right?
Host-parasite relationships are often thought of or depicted in a pairwise structure. That is, one host is attacked by one parasite, without an acknowledgement or consideration of how complex the relationship can be. For example, hosts are often attacked by more than one type of parasite, and the parasites themselves have to compete with one another for resources from the host. Because parasites are costly for a host, the hosts benefit from evolving resistance to the parasites. It follows that the more parasites a host is attacked by, the higher the benefit of evolving resistance, so we’d expect to see more resistance in hosts that are attacked more often. This should then result in differential evolutionary rates among hosts, which would then result in greater evolutionary divergence (see Did You Know?)
To test this idea, the authors of today’s study used a bacterium (Pseudomonas aeruginosa) and five lytic viral parasites (hereafter bacteriophages). These bacteriophages reproduce within host cells until they eventually cause the host to burst, killing the host (think of the chestburster in Alien, but a LOT of them). Because their reproduction results in the death of the host, lytic parasites impose a very strong selection pressure on hosts, making this a perfect host-parasite system to test the above prediction.
There are countless parasites in nature, and many of them tend to have relatively short life-cycles. For example, ticks live for about two years, while may of their hosts (us included) live for much longer. Because there is such a disparity in lifespan, parasites are predicted to have a greater evolutionary potential than their hosts. In other words, parasites should evolve faster than their hosts, which theoretically means that parasites should be more fit on local hosts than they would be on non-local hosts, as they would have had more time to adapt (i.e., local adaption, see Did You Know?).
Despite these predictions, the evidence from experimental studies of parasite local adaptation is mixed at best. Some studies show the adaptation to local hosts we’d expect, but some studies don’t. One reason for the lack of consistent evidence is that parasite dispersal between habitats can limit the ability of parasites to adapt. To help explain that I’ll use a comparison to cooking. If you are cooking a dish and you want to make it spicier you add in more spice. But imagine that when you add in that spice, you are also adding a lot of cream. The dish could be spicy, because you are adding spice, but the cream is diluting the spice and masking any potential heat. That is what parasite dispersal does to local adaptation: parasites within a given habitat (the dish) may have the ability to adapt to their hosts (become spicier), but because parasites from other habitats (the cream) are coming into their habitat and diluting those adaptations it masks any overall adaptation to the host (never gets spicy). Today’s authors therefore wanted to test how parasite dispersal affected local adaptation to hosts.
Quantifying 25 years of disease‐caused declines in Tasmanian devil populations: host density drives spatial pathogen spread (2021) Cunningham et al., Ecology Letters, https://doi.org/10.1111/ele.13703
While the Tasmanian Tiger has made news this last month for all the wrong reasons, there’s still another famous species of Tasmanian mammal which deserves just as much attention (probably more given that we can still save this one from extinction). The Tasmanian devil has seen its populations declined considerably over the last three decades, largely due to the emergence of a transmissible facial tumour, the devil facial tumour disease (DFTD).
The way the devils interact mean that even at low densities, the disease can still be transmitted through a population. The aggressive nature of Tasmanian devil mating (which occurs even when there are few devils around) is a big transmission vector. This unfortunately means that extinction due to DFTD was recently thought to be a likely endpoint.
Today’s authors wanted to test to how strongly the devil density influenced the spread of DFTD, and whether the drop in population that the disease causes means that we’re likely to see the disease’s effects wear off at some point, and Tasmanian devil populations stabilise.
What They Did
Long-term data is an absolute must for a study like this. Luckily, the Tasmanian government has run ‘spotlight surveys’ along 172 road transects for the last 25 years. These involve driving slowly along a 10 kilometre stretch of road and recording mammal presence using a handheld spotlight. This was combined with further surveys designed to obtain density at smaller scales to come up with a predictive estimate of devil density in Tasmania from 1985 to 2035.
The team also used occurrence data for DFTD to figure out how quickly it initially spread through Tasmania, and modelled the spread into a new region against the density of the devils in that region.
Did You Know: Devil Reintroduction
The Tasmanian devils are an Australian icon, and a lot of money has been put into figuring out how to save their species. Suggestions have been made to reintroduce DFTD-free population back onto mainland Australia, where their presence may even help reduce the effect of cats and foxes. However it is also possible that the introduction of a new predator could instead put added pressure on mainland species already threatened by invasive predators. Studies into this are ongoing, and you can check out more on them at the articles linked below.
Tasmanian devil density may have played a large role in the initial spread of the disease, explaining why it spread so quickly through certain parts of Tasmania. This isn’t hugely surprising, though the precision with which the authors modelled its spread will be absolutely crucial for effective conservation.
What is really interesting is that the Tasmanian devil population back before the disease struck were probably much lower than initially thought. If this sounds depressing, the other big takeaway is that based on the predictions here, the decline in devil numbers should ease off soon, meaning the disease is unlikely to result in the extinction of Tasmania’s most iconic endemic species.
Normally authors will mention interesting future research which could build on the research they’ve carried out. Standard practice. Here, my ‘problem’ is that the authors mention some research so incredibly tantalising I’m angry at them for bringing it up. What will be important in the future is looking at devil genotypes. The genetic makeup of some devils will make them more resistant to the disease, and identifying and moving these individuals to areas where the disease is rampant could help fight DFTD. Having said that, it could also help produce more aggressive strains of the disease. GIVE ME ANSWERS.
This is a good news story, which often feel quite scant in the world of ecology. But it doesn’t mean the devil is out of the woods yet. Actually the woods themselves are a massive problem, seeing as Australia’s rates of deforestation are among the worst in the world. We need to constantly monitor the population to figure out where local extinctions are likely.
This study is also a fantastic example of how important long-term monitoring is for ecologists. Studies like the one used here are hard to fund (more on that here), but their value to ecologists in allowing us to figure out what drives population fluctuations is enormous.
Sam Perrin is a freshwater ecologist currently completing his PhD at the Norwegian University of Science and Technology who has spent way too much time looking at photos of Tasmanian mammals over the last 2 weeks. You can read more about his research and the rest of the Ecology for the Masses writers here, see more of his work at Ecology for the Masses here, or follow him on Twitter here.
Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection (2021)Petersen et al., Ecological Solutions and Evidence, https://doi.org/10.1002/2688-8319.12048
With the rise of the internet, GPS’ and smartphones, the amount of openly available species occurrence data has reached previously unfathomable numbers. This increase is mostly due to the engagement of the citizen scientist – regular people getting out there in nature and taking part in data collection and research. From people taking photos of flowers in their backyard to organised salamander spotting safaris, citizen scientists have opened up data that previously would have cost massive amounts to produce.
The Global Biodiversity Information Facility (GBIF) is the largest hub of such data, collating data ranging from amateur observation to museum specimens to professional surveys. It is well-known, however, that this kind of openly available data comes with a myriad of caveats: some species groups are reported much more than others (I am looking at you, bird-watchers), and “roadside bias” (see Did You Know?) haunts the records. But how are the records distributed among different land-cover types on a country-scale, does it differ between groups of conservation concern, and does it depend on who the reporters are?