Detection of periodic peaks in Karenia brevis concentration consistent with the time-delay logistic equation

The logistic equation models single-species population growth with a sigmoid curve that begins as exponential and ends with an asymptotic approach to a final population determined by natural system carrying capacity. But the population of a natural system often does not stabilize as it approaches carrying capacity. Instead, it exhibits periodic change, sometimes with very large amplitudes. The time-delay modification of the logistic equation accounts for this behavior by connecting the present rate of population growth to conditions at an earlier time. The periodic change in population with time can progress from a monotonic approach to the carrying capacity; to oscillation around the carrying capacity; to limit-cycle periodic change; and, finally, to chaotic change. The presence of multiple species and inadequate sampling frequency and spatial coverage hinder the application of the time-delay logistic equation to real-world populations. Blooms of Karenia brevis along the southwest Florida Gulf Coast, however, provide a unique opportunity in that blooms are nearly monospecific and are sampled frequently over a wide geographic region; they are good candidates for testing the time-delay logistic equation. We show that these blooms exhibit peaks in concentration with periods in the range of 40-100 days, consistent with that predicted by the time-delay logistic equation. Cell concentrations in the valleys between the peaks are at least 2-3 orders of magnitude lower than peak values, offering predictable windows of opportunity for potential mitigation efforts.

Rethinking the Prevalence and Relevance of Chaos in Ecology

Chaos was proposed in the 1970s as an alternative explanation for apparently noisy fluctuations in population size. Although readily demonstrated in models, the search for chaos in nature proved challenging and led many to conclude that chaos is either rare or nigh impossible to detect. However, in the intervening half-century, it has become clear that ecosystems are replete with the enabling conditions for chaos. Chaos has been repeatedly demonstrated under laboratory conditions and has been found in field data using updated detection methods. Together, these developments indicate that the apparent rarity of chaos was an artifact of data limitations and overreliance on low-dimensional population models. We invite readers to reevaluate the relevance of chaos in ecology, and we suggest that chaos is not as rare or undetectable as previously believed.

Within-host priority effects and epidemic timing determine outbreak severity in co-infected populations

Co-infections of hosts by multiple pathogen species are ubiquitous, but predicting their impact on disease remains challenging. Interactions between co-infecting pathogens within hosts can alter pathogen transmission, with the impact on transmission typically dependent on the relative arrival order of pathogens within hosts (within-host priority effects). However, it is unclear how these within-host priority effects influence multi-pathogen epidemics, particularly when the arrival order of pathogens at the host-population scale varies. Here, we combined models and experiments with zooplankton and their naturally co-occurring fungal and bacterial pathogens to examine how within-host priority effects influence multi-pathogen epidemics. Epidemiological models parametrized with within-host priority effects measured at the single-host scale predicted that advancing the start date of bacterial epidemics relative to fungal epidemics would decrease the mean bacterial prevalence in a multi-pathogen setting, while models without within-host priority effects predicted the opposite effect. We tested these predictions with experimental multi-pathogen epidemics. Empirical dynamics matched predictions from the model including within-host priority effects, providing evidence that within-host priority effects influenced epidemic dynamics. Overall, within-host priority effects may be a key element of predicting multi-pathogen epidemic dynamics in the future, particularly as shifting disease phenology alters the order of infection within hosts.

Warming at the population level: Effects on age structure, density, and generation cycles

The impact of climate change on strongly age-structured populations is poorly understood, despite the central role of temperature in determining developmental rates in ectotherms. Here we examine the effect of warming and its interactions with resource availability on the population dynamics of the pyralid moth Plodia interpunctella, populations of which normally show generation cycles, a consequence of strong and asymmetric age-related competition. Warming by 3°C above the standard culture temperature led to substantial changes in population density, age structure, and population dynamics. Adult populations were some 50% larger in warmed populations, probably because the reduced fecundity associated with warming leads to reduced larval competition, allowing more larvae to develop to adulthood. Warming also interacted with resource availability to alter population dynamics, with the generation cycles typical of this species breaking down in the 30° populations when standard laboratory diet was provided but not when a reduced nutrient poor diet was used. Warming by 6° led to either rapid extinction or the persistence of populations at low densities for the duration of the experiment. We conclude that even moderate warming can have considerable effects on population structure and dynamics, potentially leading to complete changes in dynamics in some cases. These results are particularly relevant given the large number of economically important species that exhibit generation cycling, in many cases arising from similar mechanisms to those operating in P. interpunctella.

Complex Daphnia interactions with parasites and competitors

Species interactions can strongly influence the size and dynamics of epidemics in populations of focal hosts. The "dilution effect" provides a particularly interesting type of interaction from a biological standpoint. Diluters - other host species which resist infection but remove environmentally-distributed propagules of parasites (spores) - should reduce disease prevalence in focal hosts. However, diluters and focal hosts may compete for shared resources. This combination of positive (dilution) and negative (competition) effects could greatly complicate, even undermine, the benefits of dilution and diluter species from the perspective of the focal host. Motivated by an example from the plankton (i.e., zooplankton hosts, a fungal parasite, and algal resources), we study a model of dilution and competition. Our model reveals a suite of five results: • A diluter that is a superior competitor wipes out the host, regardless of parasitism. Although expected, this outcome is an ever-present danger in strategies that might use diluters to control disease. • If the diluter is an inferior competitor, it can reduce disease prevalence, despite the competition, as parameterized in our model. However, competition may also reduce density of susceptible hosts to levels below that seen in focal host-parasite systems alone. • As they decrease disease prevalence, diluters destabilize dynamics of the focal host and their resources. Thus, diluters undermine the stabilizing effects of disease. • The four species combination can generate very complex dynamics, including period-doubling bifurcations and torus (Neimark-Sacker) bifurcations. • At lower resource carrying capacity, the diluter’s dilution of spores is 'helpful' to the focal host, i.e., dilution can elevate host density by reducing disease. But, as the resource carrying capacity increases further, the equilibrium density of the diluter increases while the density of the focal host decreases, despite competition. Namely, the negative effects of competition start to outweigh the positive effects of dilution from the perspective of equilibrium density of the focal host.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series.

Modeling toxic stress by atrazine in a marine consumer-resource system

The present study combines short-term experiments with food chain modeling to explore the long-term effects of the herbicide atrazine on consumer-resource dynamics in a marine intertidal ecosystem. Short-term (28 d) lab experiments indicated that the intrinsic rate of increase (r) and carrying capacity (K) of the marine diatom Seminavis robusta decreased with increasing atrazine exposure. This decrease did not show the concave shape expected from the lifetime productivity for nonexposed diatoms and from single-species toxicity data in the literature but instead was described best by a linear model. These experimentally observed atrazine-induced decreases of r and K were used to parameterize a Rosenzweig-MacArthur model representing a simple food chain including the tested diatom and its grazer, the harpacticoid copepod Delavalia palustris var. palustris. Stable oscillation zoo-phytobenthos systems were produced at diatom exposures of 0, 100, and 150 µg/L atrazine. An atrazine concentration of 150 µg/L contributed to a 15% increase of the oscillation periods of both diatoms and copepods as well as a 52% reduction of oscillation amplitudes compared with the control situation. Although the amplitudes of copepods increased only 7% at 150 µg/L atrazine, the maximum and minimum copepod densities at that concentration were reduced 61 and 63%, respectively. The effects of atrazine on periodicity and amplitudes were robust to 20% changes in the food-chain model parameters that represented allometric relationships. The simulations in the present study suggest food chain-mediated indirect effects on zoobenthos populations, indicating a reduced diatom and copepod availability throughout the year.

Predator-prey molecular ecosystems

Biological organisms use intricate networks of chemical reactions to control molecular processes and spatiotemporal organization. In turn, these living systems are embedded in self-organized structures of larger scales, for example, ecosystems. Synthetic in vitro efforts have reproduced the architectures and behaviors of simple cellular circuits. However, because all these systems share the same dynamic foundations, a generalized molecular programming strategy should also support complex collective behaviors, as seen, for example, in animal populations. We report here the bottom-up assembly of chemical systems that reproduce in vitro the specific dynamics of ecological communities. We experimentally observed unprecedented molecular behaviors, including predator-prey oscillations, competition-induced chaos, and symbiotic synchronization. These synthetic systems are tailored through a novel, compact, and versatile design strategy, leveraging the programmability of DNA interactions under the precise control of enzymatic catalysis. Such self-organizing assemblies will foster a better appreciation of the molecular origins of biological complexity and may also serve to orchestrate complex collective operations of molecular agents in technological applications.

SPECTRAL ANALYSIS UNMASKS SYNCHRONOUS AND COMPENSATORY DYNAMICS IN PLANKTON COMMUNITIES

Community biomass is often less variable than the biomasses of populations within the community, yet attempts to implicate compensatory dynamics between populations as a cause of this relationship often fail. In part, this may be due to the lack of appropriate metrics for variability, but there is also great potential for large-scale processes such as seasonality or longer-term environmental change to obscure important dynamics at other temporal scales. In this study, we apply a scale-resolving method to long-term plankton data, to identify the specific temporal scales at which community-level variability is influenced by synchrony or compensatory dynamics at the population level. We show that variability at both the population and community level is influenced strongly by a few distinct temporal scales: in phytoplankton, ciliate, rotifer, and crustacean communities, synchronous dynamics are predominant at most temporal scales. However, in phytoplankton and crustacean communities, compensatory dynamics occur at a sub-annual scale (and at the annual scale in crustaceans) leading to substantial reductions in community-level variability. Aggregate measures of population and community variability do not detect compensatory dynamics in these communities; thus, resolving their scale dependence unmasks dynamics that are important for community stability in this system. The methods and results presented herein will ultimately lead to a better understanding of how stability is achieved in communities.

The Evolution of Oscillatory Behavior in Age‐Structured Species

A major challenge in ecology is to explain why so many species show oscillatory population dynamics and why the oscillations commonly occur with particular periods. The background environment, through noise or seasonality, is one possible driver of these oscillations, as are the components of the trophic web with which the species interacts. However, the oscillation may also be intrinsic, generated by density-dependent effects on the life history. Models of structured single-species systems indicate that a much broader range of oscillatory behavior than that seen in nature is theoretically possible. We test the hypothesis that it is selection that acts to constrain the range of periods. We analyze a nonlinear single-species matrix model with density dependence affecting reproduction and with trade-offs between reproduction and survival. We show that the evolutionarily stable state is oscillatory and has a period roughly twice the time to maturation, in line with observed patterns of periodicity. The robustness of this result to variations in trade-off function and density dependence is tested.