Misapplied management makes matters worse: Spatially explicit control leverages biotic interactions to slow invasion

A wide range of approaches has been used to manage the spread of invasive species, yet invaders continue to be a challenge to control. In some cases, management actions have no effect or may even inadvertently benefit the targeted invader. Here, we use the mid-20th century management of the Red Imported Fire Ant, Solenopsis invicta, in the US as a motivating case study to explore the conditions under which such wasted management effort may occur. Introduced in approximately 1940, the fire ant spread widely through the southeast US and became a problematic pest. Historically, fire ants were managed with broad-spectrum pesticides; unfortunately, these efforts were largely unsuccessful. One hypothesis suggests that, by also killing native ants, mass pesticide application reduced competitive burdens thereby enabling fire ants to invade more quickly than they would in the absence of management. We use a mechanistic competition model to demonstrate the landscape-level effects of such management. We explicitly model the extent and location of pesticide applications, showing that the same pesticide application can have a positive, neutral, or negative effect on the progress of an invasion, depending on where it is applied on the landscape with respect to the invasion front. When designing management, the target species is often considered alone; however, this work suggests that leveraging existing biotic interactions, specifically competition with native species, can increase the efficacy of management. Our model not only highlights the potential unintended consequences of ignoring biotic interactions, but also provides a framework for developing spatially explicit management strategies that take advantage of these biotic interactions to work smarter, not harder.

How do seasonal temperature variations influence interplay between toxic and non-toxic cyanobacterial blooms? Evidence from modeling and experimental data

Summer cyanobacterial blooms exhibit a dynamic interplay between toxic and non-toxic genotypes, significantly influencing the cyanotoxin levels within a lake. The challenge lies in accurately predicting these toxin concentrations due to the significant temporal fluctuations in the proportions of toxic and non-toxic genotypes. Typically, the toxic genotypes dominate during the early and late summer periods, while the non-toxic variants prevail in mid-summer. To dissect this phenomenon, we propose a model that accounts for the competitive interaction between toxic and non-toxic genotypes, as well as seasonal temperature variations. Our numerical simulations suggest that the optimal temperature of the toxic genotypes is lower than that of the optimal temperatures of the non-toxic counterparts. This difference of optimal temperature may potentially contribute to explain the dominance of toxic genotypes at the early and late summer periods, situation often observed in the field. Experimental data from the laboratory align qualitatively with our simulation results, enabling a better understanding of complex interplays between toxic and non-toxic cyanobacteria.

Asymptotic properties of the Lotka-Volterra competition and mutualism model under stochastic perturbations

Stochastically perturbed models, where the white noise type stochastic perturbations are proportional to the current system state, the most realistically describe real-life biosystems. However, such models essentially have no equilibrium states apart from one at the origin. This feature makes analysis of such models extremely difficult. Probably, the best result that can be found for such models is finding of accurate estimations of a region in the model phase space that serves as an attractor for model trajectories. In this paper, we consider a classical stochastically perturbed Lotka-Volterra model of competing or symbiotic populations, where the white noise type perturbations are proportional to the current system state. Using the direct Lyapunov method in a combination with a recently developed technique, we establish global asymptotic properties of this model. In order to do this, we, firstly, construct a Lyapunov function that is applicable to the both competing (and globally stable) and symbiotic deterministic Lotka-Volterra models. Then, applying this Lyapunov function to the stochastically perturbed model, we show that solutions with positive initial conditions converge to a certain compact region in the model phase space and oscillate around this region thereafter. The direct Lyapunov method allows to find estimates for this region. We also show that if the magnitude of the noise exceeds a certain critical level, then some or all species extinct via process of the stochastic stabilization ('stabilization by noise'). The approach applied in this paper allows to obtain necessary conditions for the extinction. Sufficient conditions for the extinction (that for this model occurs via the process that is known as the 'stochastic stabilization', or the 'stabilization by noise') are found applying the Khasminskii-type Lyapunov functions.

In vitro competition between two transmissible cancers and potential implications for their host, the Tasmanian devil

Since the emergence of a transmissible cancer, devil facial tumour disease (DFT1), in the 1980s, wild Tasmanian devil populations have been in decline. In 2016, a second, independently evolved transmissible cancer (DFT2) was discovered raising concerns for survival of the host species. Here, we applied experimental and modelling frameworks to examine competition dynamics between the two transmissible cancers in vitro. Using representative cell lines for DFT1 and DFT2, we have found that in monoculture, DFT2 grows twice as fast as DFT1 but reaches lower maximum cell densities. Using co-cultures, we demonstrate that DFT2 outcompetes DFT1: the number of DFT1 cells decreasing over time, never reaching exponential growth. This phenomenon could not be replicated when cells were grown separated by a semi-permeable membrane, consistent with exertion of mechanical stress on DFT1 cells by DFT2. A logistic model and a Lotka-Volterra competition model were used to interrogate monoculture and co-culture growth curves, respectively, suggesting DFT2 is a better competitor than DFT1, but also showing that competition outcomes might depend on the initial number of cells, at least in the laboratory. We provide theories how the in vitro results could be translated to observations in the wild and propose that these results may indicate that although DFT2 is currently in a smaller geographic area than DFT1, it could have the potential to outcompete DFT1. Furthermore, we provide a framework for improving the parameterization of epidemiological models applied to these cancer lineages, which will inform future disease management.

Modeling Input Factors in Second Language Acquisition of the English Article Construction

Based on the Competition Model, the current study investigated how cue availability and cue reliability as two important input factors influenced second language (L2) learners' cue learning of the English article construction. Written corpus data of university-level Chinese-L1 learners of English were sampled for a comparison of English majors and non-English majors who demonstrated two levels of L2 competence in English article usage. The path model analysis in structural equation modeling was utilized to investigate the relationship between the input factors and L2 usage (frequency and accuracy of article cue production). The findings contribute novel and scarce empirical evidence that confirms a central claim of the Competition Model, i.e., the changing importance of cue availability and cue reliability in the frequency and accuracy of production. Cue availability was found to determine L2 production frequency regardless of level of L2 competence. Cue reliability was the input factor that differentiated competence levels. When learners stayed at a relatively lower L2 proficiency, cue reliability played an important role in influencing L2 frequency of usage rather than accuracy of usage. When learners developed increased exposure to and stronger competence in the target language, cue reliability played a significant role in determining learners' success of cue learning. The study is methodologically innovative and expands the empirical applicability of the Competition Model to the domain of second language production and construction learning.

Bayesian Estimation of the ntPET Model in Single-Scan Competition PET Studies

This paper proposes an innovative method, named b-ntPET, for solving a competition model in PET. The model is built upon the state-of-the-art method called lp-ntPET. It consists in identifying the parameters of the PET kinetic model relative to a reference region that rule the steady state exchanges, together with the identification of four additional parameters defining a displacement curve caused by an endogenous neurotransmitter discharge, or by a competing injected drug targeting the same receptors as the PET tracer. The resolution process of lp-ntPET is however suboptimal due to the use of discretized basis functions, and is very sensitive to noise, limiting its sensitivity and accuracy. Contrary to the original method, our proposed resolution approach first estimates the probability distribution of the unknown parameters using Markov-Chain Monte-Carlo sampling, distributions from which the estimates are then inferred. In addition, and for increased robustness, the noise level is jointly estimated with the parameters of the model. Finally, the resolution is formulated in a Bayesian framework, allowing the introduction of prior knowledge on the parameters to guide the estimation process toward realistic solutions. The performance of our method was first assessed and compared head-to-head with the reference method lp-ntPET using well-controlled realistic simulated data. The results showed that the b-ntPET method is substantially more robust to noise and much more sensitive and accurate than lp-ntPET. We then applied the model to experimental animal data acquired in pharmacological challenge studies and human data with endogenous releases induced by transcranial direct current stimulation. In the drug challenge experiment on cats using [¹⁸F]MPPF, a serotoninergic 1A antagonist radioligand, b-ntPET measured a dose response associated with the amount of the challenged injected concurrent 5-HT1A agonist, where lp-ntPET failed. In human [¹¹C]raclopride experiment, contrary to lp-ntPET, b-ntPET successfully detected significant endogenous dopamine releases induced by the stimulation. In conclusion, our results showed that the proposed method b-ntPET has similar performance to lp-ntPET for detecting displacements, but with higher resistance to noise and better robustness to various experimental contexts. These improvements lead to the possibility of detecting and characterizing dynamic drug occupancy from a single PET scan more efficiently.

Competition model for international comparisons of livestock

Interbull has been responsible for comparing dairy bulls across countries since the mid-1980s. The current methodology is called MACE (multiple across country evaluations) which has been in use since 1995. Now that genomic data are being utilized in many countries, this has led to two serious problems. The first is that of preselection of young bulls such that the young animals are no longer a random sample of progeny from a sire by dam mating pair. Secondly, some countries are becoming less willing to share genomic data with Interbull. Both issues raise concern over the future of Interbull and international comparisons. This paper suggests a competition model as a potential replacement for MACE. The competition model makes pairwise comparisons between all pairs of bulls within a country and combines these differences across countries through bulls that are used in more than one country. Pedigree information is ignored as are all genomic data because bulls are treated as fixed. The model produces one international ranking of bulls averaging out any genotype by environment interactions which may exist. The competition model is illustrated by a small example. The limitations and advantages of the competition model are discussed.

Predicting microbial growth in a mixed culture from growth curve data

We present a model-based approach for prediction of microbial growth in a mixed culture and relative fitness using data solely from growth curve experiments, which are easier to perform than competition experiments. Our approach combines growth and competition models and utilizes the total densities of mixed cultures. We implemented our approach in an open-source software package, validated it using experiments with bacteria, and demonstrated its application for estimation of relative fitness. Our approach establishes that growth in a mixed culture can be predicted using growth and competition models. It provides a way to infer relative strain or species frequencies even when competition experiments are not feasible, and to determine how differences in growth affect differences in fitness.

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Competition between cyanobacteria and green algae at low versus elevated CO2: who will win, and why?

Traditionally, it has often been hypothesized that cyanobacteria are superior competitors at low CO2 and high pH in comparison with eukaryotic algae, owing to their effective CO2-concentrating mechanism (CCM). However, recent work indicates that green algae can also have a sophisticated CCM tuned to low CO2 levels. Conversely, cyanobacteria with the high-flux bicarbonate uptake system BicA appear well adapted to high inorganic carbon concentrations. To investigate these ideas we studied competition between three species of green algae and a bicA strain of the harmful cyanobacterium Microcystis aeruginosa at low (100 ppm) and high (2000 ppm) CO2. Two of the green algae were competitively superior to the cyanobacterium at low CO2, whereas the cyanobacterium increased its competitive ability with respect to the green algae at high CO2. The experiments were supported by a resource competition model linking the population dynamics of the phytoplankton species with dynamic changes in carbon speciation, pH and light. Our results show (i) that competition between phytoplankton species at different CO2 levels can be predicted from species traits in monoculture, (ii) that green algae can be strong competitors under CO2-depleted conditions, and (iii) that bloom-forming cyanobacteria with high-flux bicarbonate uptake systems will benefit from elevated CO2 concentrations.

Habitat-mediated carry-over effects lead to context-dependent outcomes of species interactions

When individuals disperse, their performance in newly colonized habitats can be influenced by the conditions they experienced in the past, leading to environmental carry-over effects. While carry-over effects are ubiquitous in animal and plant systems, their impact on species interactions and coexistence are largely ignored in traditional coexistence theory. Here we used a combination of modelling and experiments with two competing species to examine when and how such environmental carry-over effects influence community dynamics and competitive exclusions. We found that variation in the natal habitat quality of colonizing individuals created carry-over effects which altered competitive coefficients, fecundity and mortality rates, and extinction probabilities of both species. As a consequence, the dynamics of competitive exclusion within and across habitat types was contingent on the natal habitat of colonizing individuals, indicating that spatial carry-over effects can fundamentally alter the dynamics and outcome of interspecific competition. Interestingly, carry-over effects persistently influenced dynamics in systems with interspecific competition for the entire duration of the experiment while carry-over effects were transient and only influenced initial dynamics in single-species populations. Thus carry-over effects can be enhanced by species interactions, suggesting that their long-term effects may often not be accurately predicted by single-species studies. Given that carry-over effects are ubiquitous in heterogeneous landscapes, our results provide a novel mechanism that could help explain variation in the structure of natural communities.

Reversal in competitive dominance of a toxic versus non-toxic cyanobacterium in response to rising CO2

Climate change scenarios predict a doubling of the atmospheric CO(2) concentration by the end of this century. Yet, how rising CO(2) will affect the species composition of aquatic microbial communities is still largely an open question. In this study, we develop a resource competition model to investigate competition for dissolved inorganic carbon in dense algal blooms. The model predicts how dynamic changes in carbon chemistry, pH and light conditions during bloom development feed back on competing phytoplankton species. We test the model predictions in chemostat experiments with monocultures and mixtures of a toxic and non-toxic strain of the freshwater cyanobacterium Microcystis aeruginosa. The toxic strain was able to reduce dissolved CO(2) to lower concentrations than the non-toxic strain, and became dominant in competition at low CO(2) levels. Conversely, the non-toxic strain could grow at lower light levels, and became dominant in competition at high CO(2) levels but low light availability. The model captured the observed reversal in competitive dominance, and was quantitatively in good agreement with the results of the competition experiments. To assess whether microcystins might have a role in this reversal of competitive dominance, we performed further competition experiments with the wild-type strain M. aeruginosa PCC 7806 and its mcyB mutant impaired in microcystin production. The microcystin-producing wild type had a strong selective advantage at low CO(2) levels but not at high CO(2) levels. Our results thus demonstrate both in theory and experiment that rising CO(2) levels can alter the community composition and toxicity of harmful algal blooms.