The Lazy Bird Gets the Worm
A fine-scale analysis reveals microgeographic hotspots maximizing infection rate between a parasite and its fish host (2021) Mathieu-Bégné et al., Functional Ecology, https://doi.org/10.1111/1365-2435.13967
Image credit: Viridiflavus via Wikimedia Commons, CC BY-SA 3.0
Interactions between hosts and parasites can be broken down into two broad stages: the encounter filter and the compatibility filter. The encounter filter determines whether a parasite actually comes in contact with a host, through either a spatial or temporal overlap. After the encounter filter comes the compatibility filter, the stage at which a parasite either successfully infects a host and takes the resources needed, or is successfully repelled by the host. Though the encounter filter must come before the compatibility filter, most studies tend to focus on the compatibility filter. Yet for a parasite to successfully encounter a host, many obstacles must first be overcome.
Parasites tend to be very small, and hosts tend to be rare. Furthermore, many hosts move around the environment and/or are only available to a parasite at specific times of the year. Finally, in many cases the environment that a single host can occupy is huge. With all of these difficulties facing parasites, it is not surprising that they have evolved many different strategies to effectively find hosts.
However, some species don’t appear to display these strategies. For them to succeed, it is possible that they distribute themselves in a non-random (see Did You Know?) fashion in the environment, clumping together to form “hot-spots” of infection. Other studies have investigated this “hot-spot” phenomenon before, but tended to focus on larger spatial scales, anywhere from hundreds to thousands of meters. Today’s authors wanted to understand if investigations at much smaller spatial scales (i.e., ~10 meters or less) could provide further insight into the spatial aggregation of parasites.
Did You Know: Non-Random
The phrase “non-random” describes itself, right? Not. Random. But when your data is massive, it can be hard to figure out whether it’s random, or whether there’s a pattern present. Specifically, scientists use a variety of statistical models to test if a given variable/organism/phenomenon is distributed in a statistically non-random way. Different models can be used for different situations or tests, but in the end they all allow the modelers to compare their data to an actual random distribution. The models will then spit out a result, which will tell the modeler if the data is significantly different from this random distribution or not, which then provides more information about the system in question.
What They Did
For their host and parasite species, the authors studied a fish host (Leuciscus spp.) and a crustacean parasite (Tracheliastes polycolpus). These parasites do not utilize host chemical cues to find a suitable host, nor do they modify the host’s behavior. What’s more, they are not strong swimmers, making it difficult to search for a host.
The authors first tested if the probability of infection was distributed in a non-random fashion in the environment, which would provide evidence for infection hot-spots. They did this by placing cages into a river in varying habitat types, namely areas with either a slow or rapid current. Two fish were placed into each cage, and after 45 days the authors collected the fish and quantified parasitism.
Then, they tested if these infection hot-spots resulted in clusters of infected hosts. To do this, the authors collected fish from the same river used in the above experiment. They conducted two sampling events, once before the cage experiment and once after the cage experiment. After collecting fish they quantified parasitism and then released them back to the river.
Finally, the authors tested if these infection hot-spots were associated with specific environmental variables. They quantified the depth, current velocity, and substrate composition at every site the cages in the first experiment were placed and at every site that fish were collected for the sampling study.
What They Found
For the cage experiment, the authors found that parasites were aggregated among cages in a non-random fashion, meaning that there were indeed infection hot-spots. Similarly, the authors also found evidence for spatial aggregation of infected hosts in the sampling study. Not only did infected hosts occur in the same areas in both years of the study, but these areas were similar to the areas where caged fish in the first experiment were infected, providing further evidence for hot-spots of infection. Finally, the authors found that higher parasite loads were found in areas with either low stream velocity and medium-sized substrate (the soil and rocks making up the bottom of the river), high stream velocity and small substrate, or deep water.
I honestly don’t have any issues with this study. The authors paired a semi-natural cage experiment with a survey of parasitism in the natural habitat, which allowed them to understand if the unnatural constriction of host fish movement by the cages would affect the results. However, the results between the two experiments were very consistent, meaning that the cages did not change the natural patterns of aggregated infection. The only thing I could say is that this study should be repeated in other areas and with other hosts and parasites, but that relates more to the question that this study sought to answer than the study itself.
I am a huge fan of studies that combine experimental and observational data to test an idea, and this study is a perfect example of that. Aggregation of infection patterns tends to be studied at large scales, yet today’s authors showed that infection can actually be aggregated at microgeographic scales, which has implications for how host-parasite interactions can play out at larger scales. Further, this study taught me that not only do some parasites not actively seek their hosts, but they can also be quite successful using a sit-and-wait strategy!
Dr. Adam Hasik is an evolutionary ecologist interested in the ecological and evolutionary dynamics of host-parasite interactions who wished that his food would also come to him. You can read more about his research and his work for Ecology for the Masses here, see his personal website here, or follow him on Twitter here.