Connections Matter: How Patterns of Habitat Connectivity Affect Population Dynamics

This blog post is provided by Paulina A. Arancibia and Peter J. Morin and tells the #StoryBehindThePaper for the paper “Network topology and patch connectivity affect dynamics in experimental and model metapopulations”, which was recently published in the Journal of Animal Ecology.

Global change has increased the rate at which habitats are fragmented, increasing the creation of spatially discontinuous populations linked by migration (metapopulations). The location of a favorable habitat patch within the surrounding unsuitable habitat matrix can influence the quality of its resources. However, its connections to other patches within the network can dictate how “accessible” or “isolated” a patch can be simply by the number of its direct or indirect neighbors. These patterns of connectivity reflect the topology of those networks. This “architecture” of a network describes the spatial arrangement of its patches, dictates the level of movement in and out of them, and describes the distribution of the proportion of patches with different levels of connectivity in the network.

Paramecium tetraurelia. Credit: Peter Morin

Given the recent advances in the fields of ecological design and restoration, ecologists now have the ability to create, modify, or protect particular habitats. Understanding how these very abstract notions can be applied to spatial networks therefore becomes relevant for the conservation and management of fragmented habitats. However, the large scale of natural patchy or fragmented systems makes their study very expensive and often impractical.

To circumvent these constraints, we created our own experimentally tractable micro-landscapes that differed in network topology. We used different network layouts to compare two types of network configurations representing the two ends of the biologically plausible spectrum of spatial networks: random and scale free. On one end, a random network represents a system where the connections between patches are random, leading to a structure where most patches have a similar/average number of connections and only a few are either sparsely or highly connected. On the other end of the topological continuum, a scale free network represents a system where most patches are very isolated (with few connections) and only a handful are very highly connected. We used populations of the aquatic ciliate Paramecium tetraurelia in our experimental landscapes, which we constructed from two 24-well plates for each replicate. Each well (filled with bacterized protist media) corresponded to one isolated habitat patch that we connected to a subset of others using capillary tubes through which protists could swim freely and move to occupy other patches.

Two 24-well plates connected using capillary tubes (following a random network) to allow the dispersal of P. tetraurelia. Credit: Paulina Arancibia.

Three times a week, protists in each patch were counted with the aid of a microscope. The large amount of time required to process the number of replicates necessitated splitting the experiment into two temporally non-overlapping blocks. Abundance and occupation patterns were followed for roughly 16 protist generations (3 weeks). Even at this “small” scale, experiments replicating more than two network configurations for each of the random and scale-free topologies were not feasible, so we also developed a simple patch occupancy model to simulate dynamics over at least 200 different network layouts per topology and compared those results to our experimental data.

After many hours spent counting protists at the microscope, our data showed that the pattern of connectivity of the spatial network of habitats can affect both occupancy (presence/absence) and abundance in these metapopulations. In scenarios where colonization and extinction rates are relatively low, randomly connected metapopulations performed better as determined by higher abundance in patches as well as a higher proportion of occupied patches. In contrast, in scenarios where extinction and/or colonization rates were higher, the effects of topology were indistinguishable and both networks performed similarly. The logistics involved in both lab-based experiments and in field studies greatly limit the size and scope of empirical research. However, experiments such as this can provide a reality-check for the extensive literature based on mathematical models that so far has addressed the issue of the role of network structure in metapopulation dynamics.

Occupancy in experimental (top row) and simulated (bottom row) metapopulations arranged as random (light blue) and scale free (orange) networks. Left and right columns correspond to different colonization/extinction scenarios. Credit: the authors.

Paulina A. Arancibia (@Pauli_arancibia)

Paulina is a Chilean community ecologist. She earned her PhD at Rutgers University (USA) and she is currently a postdoctoral researcher at University of Jyväskylä (Finland).

Her research interests lie in the intersection between experiments and theory. She is particularly interested in the effects of spatial configurations in metapopulation and metacommunity dynamics.

Peter J. Morin

Peter is an American community ecologist. He is mainly interested in using experiments with model systems to test predictions of community theory.

Disease Ecology: The Lion’s Share

For the 2017 Elton Prize, the Editors selected one winning paper and two highly-commended papers. Last month we featured a blog post about prize winner Natalie Clay, and now we are proud to feature a post by highly-commended author Nick Fountain-Jones. Nick is a postdoc with the Department of Veterinary Population Medicine at the University of Minnesota. Read on to hear the #StoryBehindThePaper

Understanding disease transmission is a major challenge in disease ecology. This is particularly true for viruses that infect social groups of wildlife. Contact between individuals and groups are frequent but not all contacts lead to transmission events and understanding what types of contact matters is difficult. Further muddying the waters, even within the same disease, the types of contact that lead to transmission can vary by genetic variants or ‘subtypes’ of that disease. These subtype-specific differences in transmission not only have implications for managing disease in wildlife but also have fundamental consequences for how that disease evolves. But how to detect differences in transmission between virus subtypes in wild populations? This was the problem that I grappled with when I started my postdoc with Meggan Craft at the University of Minnesota.

Lions - Meggan Craft

Photo: Meggan Craft

Under the stewardship of Craig Packer, the University of Minnesota is home to the famous Serengeti Lion Project (SLP) that has unique long-term data from thousands of lions that provided an ideal system to start to explore what contacts matter for different subtypes of feline immunodeficiency virus (FIV). As with HIV, FIV is genetically diverse and the Serengeti lion population is a ‘melting pot’ of FIV. Three different subtypes are found across the population with all lions infected by at least one subtype with some infected by all three. Even though most FIV infected lions live normal lives, previous research led by Jennifer Troyer revealed that lions infected by FIV subtype C are potentially more likely to die young compared to those infected by B. She also speculated that the contacts that led to transmission also varied, but did not have to tools to test this hypothesis. Armed with community phylogenetic tools I had learned during my PhD at the University of Tasmania, started to quantify subtype differences between the more common subtypes B and C using FIV molecular data together with a wealth of information from 16 prides from the SLP database. I found contacts between pride-mates were important for the transmission of both subtypes, but surprisingly within a pride, whilst mother-cub relationships were universally important for transmission, dads played an important role in the transmission of subtype C to their cubs. The mechanism for this is unclear at this stage, yet as father-offspring relationships are less often considered to impact transmission this was an important finding.

Upscaling this to examine if these subtype differences could shape between-pride transmission dynamics remained a challenge. Whilst working on this problem, I was awarded funding from the infectious disease evolution across scales (IDEAS) program for a research exchange with the University of Glasgow with Roman Biek. A solution came during my stay in Glasgow when I met Roman’s postdoc Maude Jacquot who works on disease genetic networks where two individuals that share genetically similar viruses are ‘connected’. In the same way, Maude created networks for the Serengeti lions so that lion prides that had a similar FIV genetic profile were ‘connected’. I then compared networks generated for each subtype and used more community ecology statistical approaches to overlay different types of networks, such as male immigration where two prides are connected if a male immigrated from one pride to another, to see which one was the best fit. Again males were important for the transmission of FIV subtype C with the male immigration network correlating best with the subtype C network as prides far away from each other on the landscape had similar subtype C genetic profiles. Male lions do most of the dispersing and it turns out they take their FIV subtype C with them. In contrast, subtype B showed a totally different pattern with prides neighbouring each other sharing similar subtype B genetic profiles. Females are the guardians of pride territory and defend it from others so this may be evidence for the importance of aggressive interactions between females in the transmission of FIV subtype B. This new approach to untangling transmission between groups was a product of a collaboration made possible via research exchange, which highlights how important exchanges between labs are. More generally, this paper also demonstrates how integrating network, phylogenetic and community ecology tools can be applied to understand disease dynamics.

FIV - Nick Fountain-Jones

Lion prides that had a similar Feline Immunodeficiency Virus (FIV) genetic profile were ‘connected’  (Image: Nick Fountain-Jones)

This is just the beginning of understanding on how genetic variation within a disease can have consequences for transmission. Next steps include sampling more of the FIV genome (this study was based on a small part of the FIV genome) from a larger number of individuals to gain more fine scale insight into how FIV spreads in lions. Furthermore, successful transmission of an FIV subtype from lion to lion may also be dependent on the presence (or absence) of other subtypes or other diseases, so exploring this avenue is a logical next step.

More Info:

Fountain‐Jones, N.M. et al. (2017) Linking social and spatial networks to viral community phylogenetics reveals subtype‐specific transmission dynamics in African lions. Journal of Animal Ecology, 86: 1469–1482.