Biomass Reallocation between Juveniles and Adults Mediates Food Web Stability by Distributing Energy Away from Strong Interactions

<div><p>Ecological theory has uncovered dynamical differences between food web modules (i.e. low species food web configurations) with only species-level links and food web modules that include within-species links (e.g. non-feeding links between mature and immature individuals) and has argued that these differences ought to cause food web theory that includes within-species links to contrast with classical food web theory. It is unclear, however, if life-history will affect the observed connection between interaction strength and stability in species-level theory. We show that when the predator in a species-level food chain is split into juvenile and adult stages using a simple nested approach, stage-structure can mute potentially strong interactions through the transfer of biomass within a species. Within-species biomass transfer distributes energy away from strong interactions promoting increased system stability consistent with classical food web theory.</p></div

The predator, consumer, and resource isoclines of the food chain module.

<p>The location of the intersections of resource (R’), consumer (C’), and predator (P’) isoclines on reduced nullsurface of a food chain module, which is a projection of all zero isoclines onto the <i>C-R</i> plane, is directly related to the stability of the food chain. The C-R interaction is unstable when C’ lies to the left of the resource isocline hump and is stable when to the right of the hump (a). Similarly, when P’ is pushed from high to low C densities, the potential to drive P-C oscillations (depicted in this figure as an outwardly spiraling arrow) increases (b). All parameter combinations are represented in one of four basic cases: stable food chain (c), cyclic food chain where the cycle is driven primarily by a strong consumer-resource interaction (d), cyclic food chain where the cycle is driven primarily by a strong predator-consumer interaction (e) and, complex dynamics where the cycles are an interaction of strong consumer-resource and strong predator-consumer interactions (f).</p

Values for predator and consumer mortality rates altered to change the relative interaction strengths to represent the four possible dynamic starting conditions.

<p>Values for predator and consumer mortality rates altered to change the relative interaction strengths to represent the four possible dynamic starting conditions.</p

Maximum stability of the LHIGP module.

<p>Patterns of the maximum point of stability reached across nested food-web modules over a range of juvenile predator consumption constants for Case 1, which is representative of the qualitative patterns for all four cases (Parameter values: <i>r</i> = 1.5, K = 1, a_CR = 3, a_PC = 1, a_PR = 0–1, b_CR = 1.5, b_PC = 1, b_<i>PR</i> <i>= 1</i>, d_C = 0.5, d_P = 0.15).</p

Eigenvalue stability in the LHIGP module.

<p>Patterns of stability for Cases 1–4 (a-d) starting from a food chain module and moving towards an exploitative competition module as <i>m</i> & <i>s</i> decrease from 1 to 0 (Parameter values: r = 1.5, K = 1, a_CR = 3, a_PC = 1, a_PR = 0.2, b_CR = 1.5, b_PC = 1, b_<i>PR</i> <i>= 1</i>, d_C = 0.3 (b and d), 0.5 (a and c), d_P = 0.1 (c and d), 0.15(a and b)).</p

Food Webs in the Human Body: Linking Ecological Theory to Viral Dynamics

<div><p>The dynamics of in-host infections are central to predicting the progression of natural infections and the effectiveness of drugs or vaccines, however, they are not well understood. Here, we apply food web theory to in-host disease networks of the human body that are structured similarly to food web models that treat both predation and competition simultaneously. We show that in-host trade-offs, an under-studied aspect of disease ecology, are fundamental to understanding the outcomes of competing viral strains under differential immune responses. Further, and importantly, our analysis shows that the outcome of competition between virulent and non-virulent strains can be highly contingent on the abiotic conditions prevailing in the human body. These results suggest the alarming idea that even subtle behavioral changes that alter the human body (e.g. weight gain, smoking) may switch the environmental conditions in a manner that suddenly allows a virulent strain to dominate and replace less virulent strains. These ecological results therefore cast new light on the control of disease in the human body, and highlight the importance of longitudinal empirical studies across host variation gradients, as well as, of studies focused on delineating life history trade-offs within hosts.</p> </div

Effects of in-host trade-offs on dynamical outcomes.

<p>Viral loads of the virulent strain (strain 1; blue), and of the less virulent strain (strain 2; red). <b>A: Example bifurcation plot.</b> Replication-defense trade-off. As the cost of higher replication increases, transient coexistence becomes possible (area near the edge of shaded region). If this cost is raised even higher (less investment in immune defenses; blue link becomes stronger), then coexistence or dominance of the less virulent strain are possible. Mapped to each dynamical outcome is the corresponding module with its respective interaction strengths and relative densities. <b>Replication and decay trade-off in viral dynamics model: </b><b><i>Chronic and transient infections.</i></b> HIV is plotted in <b>B</b> and Influenza A in <b>C.</b> The dashed lines indicate where both strains have identical decay rates (<i>u_1 =u₂</i>), which is where most conventional in-host models fall. However, without strain-specific measurements of viral decay rates we do not know where the system actually lies along these plots.</p

Variation between hosts as environmental gradients.

<p>The different weighted arrows represent the overall interaction strengths, and the various sized circles indicate relative densities of: shared resource, <i>R</i>, competitor species <i>i</i>, <i>C_i</i>, top predator, <i>P</i>, antibodies, <i>Abs</i>, macrophages, <i>MAC</i>, cytotoxic T cells, <i>CTL</i> , uninfected cells, <i>X</i>, and infected cells by strain or species <i>i</i>, <i>Y_i</i>. Similar to studies of abiotic environmental gradients studied in ecology (<b>A</b>), finding patterns of dynamic behaviours and outcomes across host heterogeneities (<b>B</b>) could greatly improve our understanding of variability in disease burden and what factors affect virulence and persistence.</p

Immune deficiency and vaccination in HPV.

<p>Viral load of HPV-16 (blue), viral load of non-vaccine HPV type (red). <b>A: Immunocompetent CTL response.</b> (<b>i</b>) Smoking impairs the humoral response which shifts the system away from the bifurcation, resulting in higher viral load and thus higher disease burden. (<b>ii</b>) Hypothetical scenario: Smoking changes the strain dominance structure, by weakening the strength of the natural trade-off, so that the more virulent strain is dominant in the smoker. Epidemiologically, some strains would be more prevalent in smokers than in non-smokers, and their viral loads would be significantly higher. <b>B: HIV-positive hosts with HPV infection.</b> Over the entire trade-off axis the viral loads are higher (than in A) due to the depletion of the CTL population by HIV. (<b>i</b>) Similar to A.i, the effect of the simultaneous suppression of the humoral system is augmented by the in-host trade-off. (<b>ii</b>) Example time series at u₁ = 0.83 and u₁ = 1.2. Another dynamical consequence of CTL depletion by HIV is that HPV types coexist longer inside the host (differences in time till exclusion of one type). Compare transients of dotted (immunocompetent) vs. solid (immunocompromised) curves. <b>Vaccination. C:</b> If stochasticity near zero is considered, then vaccination in the immune-mediated apparent competition module leads to the clearance of both strains, i.e. cross-protection (here the curves represent infected cells of the HPV types). <b>D:</b> In contrast, the diamond module, with shared resources, behaves differently. By increasing the strength of the trade-off, the vaccine changes the conditions to favor the less virulent strain.</p

Common community modules in both free-living and in-host systems.

<p>P = predator (e.g. carnivore), C = competitor (e.g. herbivore) and R = resource (e.g. plant species). Modules: (i) Single-chain (ii) Apparent competition (iii) Resource competition (iv) Diamond (v) Intraguild predation, (vi) Modules are sub-webs of a larger web of all interacting host cells and coinfecting parasites.</p