Sheltered from the storm? Population viability analysis of a rare endemic under periodic catastrophe regimes

Rare species are important targets for biodiversity conservation efforts because rarity often equates to small populations and increased endangerment. Rare species are prone to stochastic extinction events and may be particularly susceptible to catastrophes. Therefore, understanding how rare species respond to disturbances is critical for evaluating extinction risk and guiding conservation managers. Population viability analyses (PVAs) are essential for assessing rare species' status yet they seldom consider catastrophic events. Accordingly, we present a PVA of a rare tropical epiphyte, Lepanthes caritensis (Orchidaceae), under simulated disturbance regimes to evaluate its demographics and extinction risk. We aimed to test how demographic models incorporating catastrophes affect population viability estimates. Our goal was to better guide management of these orchids and other rare plants. Results revealed L. caritensis numbers have declined recently, but projected growth rates indicated that most subpopulations should increase in size if undisturbed. Still, projection models show that moderate catastrophes reduce growth rates, increase stochasticity in subpopulation sizes, and elevate extinction risk. Severe catastrophes had a more pronounced effect in simulations; growth rates fell below replacement level, there was greater variation in projected population sizes, and extinction risk was significantly higher. PVAs incorporating periodic catastrophes indicate that rare species may have greater extinction probabilities than standard models suggest. Thus, precautionary conservation measures should be taken in disturbance prone settings and we encourage careful monitoring after environmental catastrophes. Future rare plant PVAs should incorporate catastrophes and aim to determine if rescue and reintroduction efforts are necessary after disturbances to insure long-term population viability.     

植物种群生存力分析研究进展

对十多年来国外植物PVA的研究进行了综合评述;具体分析了影响植物种群生存力的各种随机性因子及确定性因子;总结了植物PVA研究的方法步骤及采用的模拟模型;探讨了植物PVA的难点,PVA对管理措施的评价效果;并提出对今后植物PVA的研究展望,认为PVA是研究濒危植物种群灭绝及评价管理或保护措施的有力工具;发展描述复杂种间关系的多种种的PVA模型以及包含多个影响因素的PVA应用模型是未来植物PVA的研究方向。     

Population viability analyses in plants: challenges and opportunities

This review of 95 plant population viability analyses (PVAs) reveals that most studies consider one species, only a few populations and are based on data collected for less than five years. Only five studies referred to themselves as PVAs. Plants offer numerous challenges, such as seed banks and periodic recruitment, but these can be answered with suitable data collection and modeling. New approaches, such as metapopulation models, inclusion of disturbance cycles, and integration of genetics and demography, are producing more realistic PVAs. Although exact solutions are fraught with limitations, plant PVAs can be useful in comparing management regimes, populations and microhabitats, and in using these results to guide conservation and management.     

Genetic diversity,admixture, and hatchery influence in Brook Trout (Salvelinus fontinalis) throughout western New York State

Although Brook Trout are distributed across most of eastern North America, population numbers have declined in many regions due to habitat loss, climate change, and competition with non‐native species. In New York State, Brook Trout habitat has been substantially reduced, with many areas showing complete extirpation of Brook Trout populations, predominantly in the western portion of the state. Small, fragmented populations are at risk of genetic diversity loss, inbreeding depression, and reduced fitness, leading to a greater potential for local extirpation. Genetic monitoring is a practical tool that can facilitate further conservation‐decision making regarding small populations. In this study, we used 12 microsatellite loci to examine 3,436 sampled Brook Trout, representing 75 sites from the Allegheny, Erie/Niagara, Genesee, Oswego, Lake Ontario, and Susquehanna drainage basins throughout western New York State. Three Brook Trout hatchery strains were also genetically characterized to evaluate the degree of hatchery introgression between wild populations and hatchery strains stocked in the region. Overall, estimates of genetic diversity varied widely: Allelic richness ranged from 2.23 to 7.485, and expected heterozygosity ranged from 0.402 to 0.766. As observed for Brook Trout in other regions, we found a high degree of genetic differentiation among populations, with all comparisons except one showing significant F_ST values. Hatchery introgression was found to be minimal, with estimates ranging from 1.96% to 3.10% of wild individuals exhibiting membership proportions to a hatchery strain cluster exceeding 10% (q ≥ 0.10). Results from this investigation can be used to prioritize management efforts for Brook Trout in western New York State and act as a baseline to monitor future population trends.     

Integrating genetic data and population viability analyses for the identification of harbour seal (Phoca vitulina) populations and management units

Identification of populations and management units is an essential step in the study of natural systems. Still, there is limited consensus regarding how to define populations and management units, and whether genetic methods allow for inference at the relevant spatial and temporal scale. Here, we present a novel approach, integrating genetic, life history and demographic data to identify populations and management units in southern Scandinavian harbour seals. First, 15 microsatellite markers and model‐ and distance‐based genetic clustering methods were used to determine the population genetic structure in harbour seals. Second, we used harbour seal demographic and life history data to conduct population viability analyses (PVAs) in the vortex simulation model in order to determine whether the inferred genetic units could be classified as management units according to Lowe and Allendorf's (Molecular Ecology, 19, 2010, 3038) ‘population viability criterion’ for demographic independence. The genetic analyses revealed fine‐scale population structuring in southern Scandinavian harbour seals and pointed to the existence of several genetic units. The PVAs indicated that the census population size of each of these genetic units was sufficiently large for long‐term population viability, and hence that the units could be classified as demographically independent management units. Our study suggests that population genetic inference can offer the same degree of temporal and spatial resolution as ‘nongenetic’ methods and that the combined use of genetic data and PVAs constitutes a promising approach for delineating populations and management units.     

Reconstructing Local Population Dynamics in Noisy Metapopulations—The Role of Random Catastrophes and Allee Effects

Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity—demographic, environmental, and random catastrophes— affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity—positive and negative environmental fluctuations—caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are applicable to metapopulation or time series data and are relevant for predicting extinction in conservation applications or the management of invasive species.     

Evolutionary suicide and evolution of dispersal in structured metapopulations

 We study the evolution of dispersal in a structured metapopulation model. The metapopulation consists of a large (infinite) number of local populations living in patches of habitable environment. Dispersal between patches is modelled by a disperser pool and individuals in transit between patches are exposed to a risk of mortality. Occasionally, local catastrophes eradicate a local population: all individuals in the affected patch die, yet the patch remains habitable. We prove that, in the absence of catastrophes, the strategy not to migrate is evolutionarily stable. Under a given set of environmental conditions, a metapopulation may be viable and yet selection may favor dispersal rates that drive the metapopulation to extinction. This phenomenon is known as evolutionary suicide. We show that in our model evolutionary suicide can occur for catastrophe rates that increase with decreasing local population size. Evolutionary suicide can also happen for constant catastrophe rates, if local growth within patches shows an Allee effect. We study the evolutionary bifurcation towards evolutionary suicide and show that a discontinuous transition to extinction is a necessary condition for evolutionary suicide to occur. In other words, if population size smoothly approaches zero at a boundary of viability in parameter space, this boundary is evolutionarily repelling and no suicide can occur. Received: 10 November 2000 / Revised version: 13 February 2002 / Published online: 17 July 2002     

Early human dispersals into the Iberian Peninsula: a comment on Martínez et al.(2010) and Garcia et al. (2011)

Garcia et al. (2011) recently discussed early human dispersals into the Iberian Peninsula, describing several putative lithic artifacts (Martínez et al., 2010) recovered from layer 7 of the Vallpara díssection (Madurell-Malapeira et al., 2010) in Terrassa (Vallès-Penedès Basin, Catalonia, Spain). According to the authors' opinion, such evidence (1) fills a gap in the chronology of early human occupation in Iberia, (2) indicates that these populations had primary and early access to carcasses, and (3) confirms that early human populations were equipped with advanced cultural traits enabling them to survive in unfavourable climatic conditions. We argue below that the record of human activity at Vallparadís (Martínez et al., 2010;Garcia et al., 2011) is doubtful and even that if confirmed, a chronological gap would remain (contra Garcia et al., 2011). Additional remarks on assertions by these authors on the Vallparadís geology, taphonomy and paleonvironment are also provided.     

Kinesins lead aging microtubules to catastrophe

The ability of growing microtubules to undergo catastrophes--abrupt switches from growth to shortening--is one of the key aspects of microtubule dynamics important for shaping cellular microtubule arrays. Gardner et al. show that catastrophes occur at a microtubule age-dependent rate and that depolymerizing kinesins can affect this process in fundamentally different ways.     

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

Background

Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.

Results

We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.

Conclusions

Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.     

Ready on arrival: Standing variation at a chromosomal inversion contributes to rapid adaptation in an invasive marine crab

In this issue of Molecular Ecology, Tepolt et al. (2021) illustrate how the genetic architecture of adaptation and life history influence invasive success. A marvel of many invasive species is that they are incredibly successful despite evolutionary expectations that they will have low adaptive potential and suffer inbreeding depression due to initially small founding population sizes. Determining the combinations of ecoevolutionary factors that permit this apparent “genetic paradox of invasions” is an ongoing endeavour of invasive species research. Tepolt et al. (2021) study the European green crab in its invasive range on the North American west coast. Following a single introduction into California, this crab quickly spread across a wide latitude gradient, despite low diversity in the original founding population. Adaptation of this crab to clinal variation in temperature appeared largely driven by an inferred chromosomal inversion. This inversion exists as a balanced polymorphism in the European home range of green crabs and is associated with thermal tolerance. Tepolt et al. (2021) therefore demonstrate that adaptive evolution post introduction need not be impeded by bottlenecks if variation at key parts of the genome is available and can be maintained in introduced populations. Moreover, Tepolt et al. (2021) show how chromosomal inversions acting as large-effect loci might facilitate rapid responses to selection in introduced populations.     

Wolverines and declining snowpack: response to comments

The critiques by DeVink et al. (2010) and McKelvey et al. (2010) are flawed for several reasons. We show here that, contrary to what DeVink et al. claim, the influence of annual pelt price on wolverine harvest returns is essentially negligible. DeVink et al. also suggest that our results show the influence of snowpack on trapper success, rather than on actual wolverine population dynamics. This is unlikely, since most of the snowpack terms in our models are at 1- or 2-year time lags, whereas the impact of snow conditions on trapper success can only manifest in the current year. Both DeVink et al. and McKelvey et al. claim that wolverine populations across Canada are actually increasing, but provide no quantitative data to support this claim. Both sets of authors present alternative explanations for the declines in harvest returns, but none of those explanations are mutually exclusive with our own, and none can explain the significance of time-lagged snowpack on annual harvest returns. McKelvey et al.’s claim that our results represent a spurious correlation, as well as other points that they raise, suggests either a superficial understanding or deliberate misrepresentation of our methods and can simply reflect their underlying philosophical biases.     

The Potential for Enhancing the Power of Genetic Association Studies in African Americans through the Reuse of Existing Genotype Data

We consider the feasibility of reusing existing control data obtained in genetic association studies in order to reduce costs for new studies. We discuss controlling for the population differences between cases and controls that are implicit in studies utilizing external control data. We give theoretical calculations of the statistical power of a test due to Bourgain et al (Am J Human Genet 2003), applied to the problem of dealing with case-control differences in genetic ancestry related to population isolation or population admixture. Theoretical results show that there may exist bounds for the non-centrality parameter for a test of association that places limits on study power even if sample sizes can grow arbitrarily large. We apply this method to data from a multi-center, geographically-diverse, genome-wide association study of breast cancer in African-American women. Our analysis of these data shows that admixture proportions differ by center with the average fraction of European admixture ranging from approximately 20% for participants from study sites in the Eastern United States to 25% for participants from West Coast sites. However, these differences in average admixture fraction between sites are largely counterbalanced by considerable diversity in individual admixture proportion within each study site. Our results suggest that statistical correction for admixture differences is feasible for future studies of African-Americans, utilizing the existing controls from the African-American Breast Cancer study, even if case ascertainment for the future studies is not balanced over the same centers or regions that supplied the controls for the current study.     

Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. .     

Analyses of physiological evolutionary response

Selection studies are useful if they can provide us with insights into the patterns and processes of evolution in populations under controlled conditions. In this context it is particularly valuable to be able to analyze the limitations of and constraints on evolutionary responses to allow predictions concerning evolutionary change. The concept of a selection pathway is presented as a means of visualizing this predictive process and the constraints that help define the population's response to selection. As pointed out by Gould and Lewontin, history and chance are confounding forces that can mask or distort the adaptive response. Students of the evolutionary responses of organisms are very interested in the effects of these confounding forces, since they play a critical role not only in the laboratory but also in natural selection in the field. In this article, we describe some methods that are a bit different from those used in most studies for examining data from laboratory selection studies. These analytical methods are intended to provide insights into the physiological mechanisms by which evolutionary responses to the environment proceed. Interestingly, selection studies often exhibit disparate responses in replicate populations. We offer methods for analyzing these disparate responses in replicate populations to better understand this very important source of variability in the evolutionary response. We review the techniques of Travisano et al. and show that these approaches can be used to investigate the relative roles of adaptation, history, and chance in the evolutionary responses of populations of Drosophila melanogaster to selection for enhanced desiccation resistance. We anticipate that a wider application of these techniques will provide valuable insights into the organismal, genetic, and molecular nature of the constraints, as well as the factors that serve to enhance or, conversely, to mask the effects of chance. Such studies should help to provide a more detailed understanding of the processes producing evolutionary change in populations.     

Developing optimal prediction models for cancer classification using gene expression data

Microarrays can provide genome-wide expression patterns for various cancers, especially for tumor sub-types that may exhibit substantially different patient prognosis. Using such gene expression data, several approaches have been proposed to classify tumor sub-types accurately. These classification methods are not robust, and often dependent on a particular training sample for modelling, which raises issues in utilizing these methods to administer proper treatment for a future patient. We propose to construct an optimal, robust prediction model for classifying cancer sub-types using gene expression data. Our model is constructed in a step-wise fashion implementing cross-validated quadratic discriminant analysis. At each step, all identified models are validated by an independent sample of patients to develop a robust model for future data. We apply the proposed methods to two microarray data sets of cancer: the acute leukemia data by Golub et al. and the colon cancer data by Alon et al. We have found that the dimensionality of our optimal prediction models is relatively small for these cases and that our prediction models with one or two gene factors outperforms or has competing performance, especially for independent samples, to other methods based on 50 or more predictive gene factors. The methodology is implemented and developed by the procedures in R and Splus. The source code can be obtained at http://hesweb1.med.virginia.edu/bioinformatics.     

In silico study of transcriptome genetic variation in outbred populations

Dissecting the genetic architecture of regulatory elements on a genome-wide basis is now technically feasible. The potential medical and genetical implications of this kind of experiment being very large, it is paramount to assess the reliability and repeatability of the results. This is especially relevant in outbred populations, such as humans, where the genetic architecture is necessarily more complex than in crosses between inbred lines. Here we simulated a chromosome-wide SNP association study using real human microarray data. Our model predicted, as observed, a highly significant clustering of quantitative trait loci (QTL) for gene expression. Importantly, the estimates of QTL positions were often unstable, and a decrease in the number of individuals of 16% resulted in a loss of power of approximately 30% and a large shift in the position estimate in approximately 30-40% of the remaining significant QTL. We also found that the analysis of two repeated measures of the same mRNA can also result in two QTL that are located far apart. The intrinsic difficulties of analyzing outbred populations should not be underestimated. We anticipate that (many) conflicting results may be collected in the future if whole-genome association studies for mRNA levels are carried out in outbred populations.     

Species life-history strategies affect population responses to temperature and land-cover changes

Human-induced environmental changes have a direct impact on species populations, with some species experiencing declines while others display population growth. Understanding why and how species populations respond differently to environmental changes is fundamental to mitigate and predict future biodiversity changes. Theoretically, species life-history strategies are key determinants shaping the response of populations to environmental impacts. Despite this, the association between species life histories and the response of populations to environmental changes has not been tested. In this study, we analysed the effects of recent land-cover and temperature changes on rates of population change of 1,072 populations recorded in the Living Planet Database. We selected populations with at least 5 yearly consecutive records (after imputation of missing population estimates) between 1992 and 2016, and for which we achieved high population imputation accuracy (in the cases where missing values had to be imputed). These populations were distributed across 553 different locations and included 461 terrestrial amniote vertebrate species (273 birds, 137 mammals, and 51 reptiles) with different life-history strategies. We showed that populations of fast-lived species inhabiting areas that have experienced recent expansion of cropland or bare soil present positive populations trends on average, whereas slow-lived species display negative population trends. Although these findings support previous hypotheses that fast-lived species are better adapted to recover their populations after an environmental perturbation, the sensitivity analysis revealed that model outcomes are strongly influenced by the addition or exclusion of populations with extreme rates of change. Therefore, the results should be interpreted with caution. With climate and land-use changes likely to increase in the future, establishing clear links between species characteristics and responses to these threats is fundamental for designing and conducting conservation actions. The results of this study can aid in evaluating population sensitivity, assessing the likely conservation status of species with poor data coverage, and predicting future scenarios of biodiversity change.