Variation in Avian Predation Pressure as a Driver for the Diversification of Periodical Cicada Broods.

AbstractPeriodical cicadas live 13 or 17 years underground as nymphs, then emerge in synchrony as adults to reproduce. Developmentally synchronized populations called broods rarely coexist, with one dominant brood locally excluding those that emerge in off years. Twelve modern 17-year cicada broods are believed to have descended from only three ancestral broods following the last glaciation. The mechanisms by which these daughter broods overcame exclusion by the ancestral brood to synchronously emerge in a different year, however, are elusive. Here, we demonstrate that temporal variation in the population density of generalist predators can allow intermittent opportunities for new broods to invade, even though a single brood remains dominant most of the time. We show that this mechanism is consistent, in terms of the type and frequency of brood replacements, with the distribution of periodical cicada broods throughout North America today. Although we investigate one particularly charismatic case study, the mechanisms involved (competitive exclusion, Allee effects, trait variation, predation, and temporal variability) are ubiquitous and could contribute to patterns of species diversity in a range of systems.

Modeling and prediction of phase shifts in noisy two-cycle oscillations.

Understanding and predicting ecological dynamics in the presence of noise remains a substantial and important challenge. This is particularly true in light of the poor quality of much ecological data and the imprecision of many ecological models. As a first approach to this problem, we focus here on a simple system expressed as a discrete time model with 2-cycle behavior, reflecting alternating high and low population sizes. Such dynamics naturally arise in ecological systems with overcompensatory density dependence. We ask how the amount of detail included in the population estimates affects the ability to forecast the likelihood of changes in the phase of oscillation, meaning whether high populations occur in odd or in even years. We adjust the level of detail by converting continuous population levels to simple, coarse-grained descriptions using two-state and four-state models. We also consider a cubic noisy over-compensatory model with three parameters. The focus on phase changes is what distinguishes the question we are asking and the methods we use from more standard time series approaches. Obviously, adding observation states improves the ability to forecast phase shifts. In particular, the four-state model and cubic model outperform the two-state model because they include a transition state, through which the dynamics typically pass during a phase change. Nonetheless, at high noise levels the improvement in forecast skill is relatively modest. Additionally, the frequency of phase changes depends strongly on the noise level, and is much less affected by the parameter determining amplitude in the population model, so phase shift frequencies could possibly be used to infer noise levels.

The clinical role of glycobiology on ovarian cancer progression.

Diverse carbohydrate (glycan) structures are located on lipids and proteins that cover the surface of human cells known as the glycocalyx. Research over many decades have illustrated that the glycan structures located in the glycocalyx change dramatically with cancer contributing to the early development and progression of tumors. New therapeutic and diagnostic applications for cancers based on targeting glycan changes are now in development and in early stage clinical trials. There is an abundance of research for ovarian cancer indicating that certain glycoproteins and glycolipids play major roles in the progression, recurrence, and chemoresistance of this disease. This review is focused on discussion of these biomarkers and how translational medicine for ovarian cancer can be further defined focusing on targeting glycans, glycoproteins, and glycan-mediated interactions.

Perfect Compensation Is Sufficient to Explain Insect Outbreaks Previously Attributed to Overcompensation : (A Comment on Stieha et al., "The Effects of Plant Compensatory Regrowth and Induced Resistance on Herbivore Population Dynamics").

AbstractIn "The Effects of Plant Compensatory Regrowth and Induced Resistance on Herbivore Population Dynamics," which appeared in The American Naturalist in 2016, Stieha et al. argued that overcompensatory regrowth of plant tissues lost to herbivory ("overcompensation") promotes cyclic herbivore outbreaks. In contrast, they concluded that partial regrowth ("tolerance") stabilizes herbivore dynamics, preventing outbreaks. These conclusions were based on a comparison between two plant-herbivore models that differed in two properties: (1) whether biomass could ever be higher after herbivory and regrowth than before herbivory (i.e., is overcompensatory regrowth possible?) and (2) how much herbivory the plants could withstand before only being able to partially compensate for losses (for overcompensating plants, there was a threshold herbivory level above which this occurred, whereas tolerant plants always showed partial compensation). While Stieha et al. supposed that difference 1 was responsible for the increased propensity for outbreaks in their overcompensation model, we show here that, in fact, difference 2 is responsible. Thus, we conclude that Stieha et al.'s results about "overcompensating" plants apply more broadly: the risk of herbivore outbreaks is elevated whenever plants with low-enough herbivore loads can perfectly compensate or overcompensate for losses to herbivory.

How public can public goods be? Environmental context shapes the evolutionary ecology of partially private goods.

The production of costly public goods (as distinct from metabolic byproducts) has largely been understood through the realization that spatial structure can minimize losses to non-producing "cheaters" by allowing for the positive assortment of producers. In well-mixed systems, where positive assortment is not possible, the stable production of public goods has been proposed to depend on lineages that become indispensable as the sole producers of those goods while their neighbors lose production capacity through genome streamlining (the Black Queen Hypothesis). Here, we develop consumer-resource models motivated by nitrogen-fixing, siderophore-producing bacteria that consider the role of colimitation in shaping eco-evolutionary dynamics. Our models demonstrate that in well-mixed environments, single "public goods" can only be ecologically and evolutionarily stable if they are partially privatized (i.e., if producers reserve a portion of the product pool for private use). Colimitation introduces the possibility of subsidy: strains producing a fully public good can exclude non-producing strains so long as the producing strain derives sufficient benefit from the production of a second partially private good. We derive a lower bound for the degree of privatization necessary for production to be advantageous, which depends on external resource concentrations. Highly privatized, low-investment goods, in environments where the good is limiting, are especially likely to be stably produced. Coexistence emerges more rarely in our mechanistic model of the external environment than in past phenomenological approaches. Broadly, we show that the viability of production depends critically on the environmental context (i.e., external resource concentrations), with production of shared resources favored in environments where a partially-privatized resource is scarce.

Noise-induced versus intrinsic oscillation in ecological systems.

Studies of oscillatory populations have a long history in ecology. A first-principles understanding of these dynamics can provide insights into causes of population regulation and help with selecting detailed predictive models. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing oscillations. We employ statistical physics concepts, including measures of spatial synchrony, that incorporate patterns at all scales and are novel to ecology, to show that spatial patterns can, under broad and well-defined circumstances, elucidate drivers of population dynamics. We find that when neighbours are coupled (e.g. by dispersal), noisy intrinsic oscillations become distinguishable from noise-induced oscillations at a transition point related to synchronisation that is distinct from the deterministic bifurcation point. We derive this transition point and show that it diverges from the deterministic bifurcation point as stochasticity increases. The concept of universality suggests that the results are robust and widely applicable.

Better baboon break-ups: collective decision theory of complex social network fissions.

Many social groups are made up of complex social networks in which each individual associates with a distinct subset of its groupmates. If social groups become larger over time, competition often leads to a permanent group fission. During such fissions, complex social networks present a collective decision problem and a multidimensional optimization problem: it is advantageous for each individual to remain with their closest allies after a fission, but impossible for every individual to do so. Here, we develop computational algorithms designed to simulate group fissions in a network-theoretic framework. We focus on three fission algorithms (democracy, community and despotism) that fall on a spectrum from a democratic to a dictatorial collective decision. We parameterize our social networks with data from wild baboons (Papio cynocephalus) and compare our simulated fissions with actual baboon fission events. We find that the democracy and community algorithms (egalitarian decisions where each individual influences the outcome) better maintain social networks during simulated fissions than despotic decisions (driven primarily by a single individual). We also find that egalitarian decisions are better at predicting the observed individual-level outcomes of observed fissions, although the observed fissions often disturbed their social networks more than the simulated egalitarian fissions.

Microbiome influence on host community dynamics: Conceptual integration of microbiome feedback with classical host-microbe theory.

Microbiomes have profound effects on host fitness, yet we struggle to understand the implications for host ecology. Microbiome influence on host ecology has been investigated using two independent frameworks. Classical ecological theory powerfully represents mechanistic interactions predicting environmental dependence of microbiome effects on host ecology, but these models are notoriously difficult to evaluate empirically. Alternatively, host-microbiome feedback theory represents impacts of microbiome dynamics on host fitness as simple net effects that are easily amenable to experimental evaluation. The feedback framework enabled rapid progress in understanding microbiomes' impacts on plant ecology, and can also be applied to animal hosts. We conceptually integrate these two frameworks by deriving expressions for net feedback in terms of mechanistic model parameters. This generates a precise mapping between net feedback theory and classic population modelling, thereby merging mechanistic understanding with experimental tractability, a necessary step for building a predictive understanding of microbiome influence on host ecology.

Effects of stochasticity on the length and behaviour of ecological transients.

There is a growing recognition that ecological systems can spend extended periods of time far away from an asymptotic state, and that ecological understanding will therefore require a deeper appreciation for how long ecological transients arise. Recent work has defined classes of deterministic mechanisms that can lead to long transients. Given the ubiquity of stochasticity in ecological systems, a similar systematic treatment of transients that includes the influence of stochasticity is important. Stochasticity can of course promote the appearance of transient dynamics by preventing systems from settling permanently near their asymptotic state, but stochasticity also interacts with deterministic features to create qualitatively new dynamics. As such, stochasticity may shorten, extend or fundamentally change a system's transient dynamics. Here, we describe a general framework that is developing for understanding the range of possible outcomes when random processes impact the dynamics of ecological systems over realistic time scales. We emphasize that we can understand the ways in which stochasticity can either extend or reduce the lifetime of transients by studying the interactions between the stochastic and deterministic processes present, and we summarize both the current state of knowledge and avenues for future advances.

When and Where We Can Expect to See Early Warning Signals in Multispecies Systems Approaching Tipping Points: Insights from Theory.

AbstractEarly warning signals (EWSs) have the potential to predict tipping points where catastrophic changes occur in ecological systems. However, EWSs are plagued by false negatives, leading to undetected catastrophes. One reason may be because EWSs do not occur equally for all species in a system, so whether and how strongly EWSs are detected depends on which species is being observed. Here, we illustrate how the strength of EWSs is determined by each species' relationship to properties of the noise, the system's response to that noise, and the occurrence of critical slowing down (the dynamical phenomenon that gives rise to EWSs). Using these relationships, we present general rules for maximizing EWS detection in ecological communities. We find that for two-species competitive and mutualistic systems, one should generally monitor the species experiencing smaller intraspecific effects to maximize EWS performance, while in consumer-resource systems, one should monitor the species imposing the smaller interspecific effects. These guidelines appear to hold for at least some larger communities as well. We close by extending the theoretical basis for our rules to systems with any number of species and more complex forms of noise. Our findings provide important guidance on how to monitor systems for EWSs to maximize detection of tipping points.

Sharp boundary formation and invasion between spatially adjacent periodical cicada broods.

Periodical cicadas, Magicicada spp., are a useful model system for understanding the population processes that influence range boundaries. Unlike most insects, these species typically exist at very high densities (occasionally >1000/ m2) and have unusually long life-spans (13 or 17 years). They spend most of their lives underground feeding on plant roots. After the underground period, adults emerge from the ground to mate and oviposit over a period of just a few days. Collections of populations that are developmentally synchronized across large areas are known as "broods". There are usually sharp boundaries between spatially adjacent broods and regions of brood overlap are generally small. The exact mechanism behind this developmental synchronization and the sharp boundary between broods remain unknown: previous studies have focused on the impacts of predator-driven Allee-effects, competition among nymphs, and their impacts on the persistence of off-synchronized emergence events. Here, we present a nonlinear Leslie-type matrix model to additionally consider cicada movement between spatially separated broods, and examine its role in maintaining brood boundaries and within-brood developmental synchrony that is seen in nature. We successfully identify ranges of competition and dispersal that lead to stable coexistence of broods that differ between spatial patches.

Management implications of long transients in ecological systems.

The underlying biological processes that govern many ecological systems can create very long periods of transient dynamics. It is often difficult or impossible to distinguish this transient behaviour from similar dynamics that would persist indefinitely. In some cases, a shift from the transient to the long-term, stable dynamics may occur in the absence of any exogenous forces. Recognizing the possibility that the state of an ecosystem may be less stable than it appears is crucial to the long-term success of management strategies in systems with long transient periods. Here we demonstrate the importance of considering the potential of transient system behaviour for management actions across a range of ecosystem organizational scales and natural system types. Developing mechanistic models that capture essential system dynamics will be crucial for promoting system resilience and avoiding system collapses.

Density dependent Resource Budget Model for alternate bearing.

Alternate bearing, seen in many types of plants, is the variable yield with a strongly biennial pattern. In this paper, we introduce a new model for alternate bearing behavior. Similar to the well-known Resource Budget Model, our model is based on the balance between photosynthesis or other limiting resource accumulation and reproduction processes. We consider two novel features with our model, 1) the existence of a finite capacity in the tree's resource reservoir and 2) the possibility of having low (but non-zero) yield when the tree's resource level is low. We achieve the former using a density dependent resource accumulation function, and the latter by removing the concept of the well-defined threshold used in the Resource Budget Model. At the level of an individual tree, our model has a stable two-cycle solution, which is suitable to model plants in which the alternate bearing behavior is pronounced. We incorporate environmental stochasticity by adding two uncorrelated noise terms to the parameters of the model associated with the nutrient accumulation and reproduction processes. Furthermore, we examine the model's behavior on a system of two coupled trees with direct coupling. Unlike the coupled Resource Budget Model, for which the only stable solution is the out-of-phase solution, our model with direct coupling has stable in-phase period-2 solutions. This suggests that our model might serve to explain spatial synchrony on a larger scale.

Dynamical Ising model of spatially coupled ecological oscillators.

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of 'universal' properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system's dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.

Novel role for the Golgi membrane protein TMEM165 in control of migration and invasion for breast carcinoma.

The TMEM165 gene encodes for a multiple pass membrane protein localized in the Golgi that has been linked to congenital disorders of glycosylation. The TMEM165 protein is a putative ion transporter that regulates H+/Ca++/Mn++ homeostasis and pH in the Golgi. Previously, we identified TMEM165 as a potential biomarker for breast carcinoma in a glycoproteomic study using late stage invasive ductal carcinoma tissues with patient- matched adjacent normal tissues. The TMEM165 protein was not detected in non-malignant matched breast tissues and was detected in invasive ductal breast carcinoma tissues by mass spectrometry. Our hypothesis is that the TMEM165 protein confers a growth advantage to breast cancer. In this preliminary study we have investigated the expression of TMEM165 in earlier stage invasive ductal carcinoma and ductal carcinoma in situ cases. We created a CRISPR/Cas9 knockout of TMEM165 in the human invasive breast cancer cell line MDAMB231. Our results indicate that removal of TMEM165 in these cells results in a significant reduction of cell migration, tumor growth, and tumor vascularization in vivo. Furthermore, we find that TMEM165 expression alters the glycosylation of breast cancer cells and these changes promote the invasion and growth of breast cancer by altering the expression levels of key glycoproteins involved in regulation of the epithelial to mesenchymal transition such as E-cadherin. These studies illustrate new potential functions for this Golgi membrane protein in the control of breast cancer growth and invasion.

Analysis of GPI-anchored proteins involved in germline stem cell proliferation in the Caenorhabditis elegans germline stem cell niche.

Stem cells divide and undergo self-renewal depending on the signals received from the stem cell niche. This phenomenon is indispensable to maintain tissues and organs in individuals. However, not all the molecular factors and mechanisms of self-renewal are known. In our previous study, we reported that glycosylphosphatidylinositol (GPI)-anchored proteins (GPI-APs) synthesized in the distal tip cells (DTCs; the stem cell niche) are essential for germline stem cell proliferation in Caenorhabditis elegans. Here, we characterized the GPI-APs required for proliferation. We selected and verified the candidate GPI-APs synthesized in DTCs by RNA interference screening and found that F57F4.3 (GFI-1), F57F4.4 and F54E2.1 are necessary for germline proliferation. These proteins are likely involved in the same pathway for proliferation and activated by the transcription factor PQM-1. We further provided evidence suggesting that these GPI-APs act through fatty acid remodelling of the GPI anchor, which is essential for association with lipid rafts. These findings demonstrated that GPI-APs, particularly F57F4.3/4 and F54E2.1, synthesized in the germline stem cell niche are located in lipid rafts and involved in promoting germline stem cell proliferation in C. elegans. The findings may thus shed light on the mechanisms by which GPI-APs regulate stem cell self-renewal.

Pathogens and Mutualists as Joint Drivers of Host Species Coexistence and Turnover: Implications for Plant Competition and Succession.

The potential for either pathogens or mutualists to alter the outcome of interactions between host species has been clearly demonstrated experimentally, but our understanding of their joint influence remains limited. Individually, pathogens and mutualists can each stabilize (via negative feedback) or destabilize (via positive feedback) host-host interactions. When pathogens and mutualists are both present, the potential for simultaneous positive and negative feedbacks can generate a wide range of possible effects on host species coexistence and turnover. Extending existing theoretical frameworks, we explore the range of dynamics generated by simultaneous interactions with pathogens and mutualists and identify the conditions for pathogen or mutualist mediation of host coexistence. We then explore the potential role of microbial mutualists and pathogens in plant species turnover during succession. We show how a combination of positive and negative plant-microbe feedbacks can generate a coexistence state that is part of a set of alternative stable states. This result implies that the outcomes of coexistence from classical plant-soil feedback experiments may be susceptible to disturbances and that empirical investigations of microbially mediated coexistence would benefit from consideration of interactive effects of feedbacks generated from different distinct components of the plant microbiome.