The Disconnect Between Short- and Long-Term Population Projections for Plant Reintroductions

The reintroduction of rare species in natural preserves is a commonly used restoration strategy to prevent species extinction. An essential first step in planning successful reintroductions is identifying which life stages (e.g., seeds or large adults) should be used to establish these new populations. Following this initial establishment phase, it is necessary to determine the level of survival, growth, and recruitment needed to maintain population persistence over time and identify management actions that will achieve these goals. In this 5-year study, we projected the short- and long-term population growth rates of a critically endangered long-lived shrub, Delissea waianaeensis. Using this model system, we show that reintroductions established with mature individuals have the lowest probability of quasi-population extinction (10 individuals) and the highest increase in population abundance. However, our results also demonstrate that short-term increases in population abundances are overly optimistic of long-term outcomes. Using long-term stochastic model simulations, we identified the level of natural seedling regeneration needed to maintain a positive population growth rate over time. These findings are relevant for planning future reintroduction efforts for long-lived species and illustrate the need to forecast short- and long-term population responses when evaluating restoration success.

Automating microsatellite screening and primer design from multi-individual libraries using Micro-Primers

Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species’ genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies.

Dynamics of a new stage-structured population model with transient and nontransient impulsive effects in a polluted environment

In this paper, we consider a new stage-structured population model with transient and nontransient impulsive effects in a polluted environment. By using the theories of impulsive differential equations, we obtain the globally asymptotically stable condition of a population-extinction solution; we also present the permanent condition for the investigated system. The results indicate that the nontransient and transient impulsive harvesting rate play important roles in system permanence. Finally, numerical analyses are carried out to illustrate the results. Our results provide effective methods for biological resource management in a polluted environment.

Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction

The incidence of somatic copy number alterations (SCNAs) per base pair of the genome is orders of magnitudes larger than that of point mutations. This makes SCNAs phenotypically effective. One mitotic event stands out in its potential to significantly change a cell's SCNA burden -- a chromosome missegregation. We have presented a general deterministic framework for modeling whole chromosome missegregations and use it to evaluate the possibility of missegregation-induced population extinction (MIE). The model predicts critical curves that separate viable from non-viable populations as a function of their turnover- and mis-segregation rates. Missegregation- and turnover rates estimated for nine cancer types are then compared to these predictions for various biological assumptions. The assumption of heterogeneous missegregation rates within a tumor was sufficient to explain the observed data. By contrast, when assuming constant mis-segregation rates, several cancers were located in regions predicted as unviable. Intra-tumor heterogeneity, including heterogeneity in mis-segregation rates, increases as tumors progress. Our predictions suggest that this intra-tumor heterogeneity hinders the chance of success of therapies aimed at MIE.

Pathophysiological Responses of Pinna nobilis Individuals Enlightens the Etiology of Mass Mortality Situation in the Mediterranean Populations

Due to the rapid decrease of Pinna nobilis populations during the previous decades, this bivalve species, endemic in the Mediterranean Sea, is characterized as ‘critically endangered’. In addition to human pressures, various pathogen infections have resulted in extended reduction, even population extinction. While Haplosporidium pinnae is characterized as one of the major causative agents, mass mortalities have also been attributed to Mycobacterium sp. and Vibrio spp. Due to limited knowledge concerning the physiological response of infected P. nobilis specimens against various pathogens, this study’s aim was to investigate to pathophysiological response of P. nobilis individuals, originating from mortality events in the Thermaikos Gulf and Lesvos and Limnos islands (Greece), and their correlation to different potential pathogens detected in the diseased animals. In isolated tissues, several cellular stress indicators of the heat shock and immune response, apoptosis and autophagy, were examined. Despite the complexity and limitations in the study of P. nobilis mortality events, the present investigation demonstrates the cumulative negative effect of co-infection additionally with H. pinnae in comparison to the non-presence of haplosporidian parasite. In addition, impacts of global climate change affecting physiological performance and immune responses result in more vulnerable populations in infectious diseases, a phenomenon which may intensify in the future.

Tipping Cascades in a Multi-patch System with Noise and Spatial Coupling

Forecasting tipping points in spatially extended systems is a key area of interest to ecologists. A slowly declining spatially distributed population is an important example of an ecological system that could exhibit a cascade of tipping points. Here, we develop a spatial two-patch model with environmental stochasticity that is slowly forced through population collapse, in the presence of changing environmental conditions. We begin with a basic spatial model, then introduce a fast–slow version of the model using geometric singular perturbation theory, followed by the inclusion of stochasticity. Using the spectral density of the fluctuating subpopulation in each patch, we derive analytic expressions for candidate indicators of population extinction and evaluate their performance through a simulation study. We find that coupling and spatial heterogeneity decrease the magnitude of the proposed indicators in coupled populations relative to isolated populations. Moreover, the degree of coupling dictates the trends in summary statistics. We conclude that this theory may be applied to other contexts, including the control of invasive species.

Phenotypic Heterogeneity Facilitates Survival While Hindering the Evolution of Drug Resistance

ABSTRACTRising rates of resistance to antimicrobial drugs threatens the effective treatment of infections across the globe. Recently, it has been shown that drug resistance can emerge from non-genetic mechanisms, such as fluctuations in gene expression, as well as from genetic mutations. However, it is unclear how non-genetic drug resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment, but that it hinders the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. The presence of non-genetic drug resistance is found to increase the first-appearance, establishment, and fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. These findings advance our fundamental understanding of the evolution of drug resistance and may guide novel treatment strategies for patients with drug-resistant infections.SIGNIFICANCEAntimicrobial (drug) resistance is predicted to kill as many as 10 million people per year and cost over 100 trillion USD in cumulative lost production globally by 2050. To mitigate these socio-economic costs, we need to fundamentally understand the drug resistance process. We investigate the effect that different forms of drug resistance have on the evolution of drug resistance using mathematical modeling and computer simulations. We find that the presence of non-genetically drug-resistant cells (whose resistance is temporary and not encoded in a genetic mutation) allows the population to survive drug treatment, while at the same time slowing down the evolution of permanent genetic drug resistance. These findings have important implications for advancing evolutionary theory and for developing effective “resistance-proof” treatments.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.