Population dynamics of three songbird species in a nestbox population in Central Europe show effects of density,climate and competitive interactions

Unravelling the contributions of density‐dependent and density‐independent factors in determining species population dynamics is a challenge, especially if the two factors interact. One approach is to apply stochastic population models to long‐term data, yet few studies have included interactions between density‐dependent and density‐independent factors, or explored more than one type of stochastic population model. However, both are important because model choice critically affects inference on population dynamics and stability. Here, we used a multiple models approach and applied log‐linear and non‐linear stochastic population models to time series (spanning 29 years) on the population growth rates of Blue Tits Cyanistes caeruleus, Great Tits Parus major and Pied Flycatchers Ficedula hypoleuca breeding in two nestbox populations in southern Germany. We focused on the roles of climate conditions and intra‐ and interspecific competition in determining population growth rates. Density dependence was evident in all populations. For Blue Tits in one population and for Great Tits in both populations, addition of a density‐independent factor improved model fit. At one location, Blue Tit population growth rate increased following warmer winters, whereas Great Tit population growth rates decreased following warmer springs. Importantly, Great Tit population growth rate also decreased following years of high Blue Tit abundance, but not vice versa. This finding is consistent with asymmetric interspecific competition and implies that competition could carry over to influence population dynamics. At the other location, Great Tit population growth rate decreased following years of high Pied Flycatcher abundance but only when Great Tit population numbers were low, illustrating that the roles of density‐dependent and density‐independent factors are not necessarily mutually exclusive. The dynamics of this Great Tit population, in contrast to the other populations, were unstable and chaotic, raising the question of whether interactions between density‐dependent and density‐independent factors play a role in determining the (in) stability of the dynamics of species populations.     

Failure of in vitro growth inhibition by cysteine to differentiate between Australian Gaeumannomyces graminis isolates pathogenic and non-pathogenic to oats

Radial growth of oat and non oat-attacking Australian isolates of Gaeumannomyces graminis was greatly inhibited by increasing concentration of DL-cysteine in basal medium agar, and growth was completely inhibited by cysteine concentrations of 3 μM. As a group, isolates of G. graminis var. tritici (both oat and non oat-attacking forms) were more inhibited than isolates of G.graminis var.avenae at 1 μM cysteine, but differences did not occur at other concentrations. Isolates of a lobed-hyphodiate fungus similar to G. graminis var. graminis were more tolerant of cysteine than other isolates. The findings indicate that in vitro inhibition of Australian G. graminis isolates by cysteine is not useful for differentiation between oat and non oat-attacking types, and is unlikely to be fundamentally related to the ability of isolates to attack oats.     

INTER‐ AND INTRASPECIFIC RELATIONSHIPS BETWEEN NUCLEAR DNA CONTENT AND CELL SIZE IN SELECTED MEMBERS OF THE CENTRIC DIATOM GENUS THALASSIOSIRA (BACILLARIOPHYCEAE)1

The enormous species diversity of diatoms correlates with the remarkable range of cell sizes in this group. Nuclear DNA content relates fundamentally to cell volume in other eukaryotic cells. The relationship of cell volume to G1 DNA content was determined among selected members of the genus Thalassiosira, one of the most species‐rich and well‐studied centric diatom genera. Both minimum and maximum species‐specific cell volume correlated positively with G1 DNA content. Phylogeny based on 5.8 S and ITS rDNA sequences indicated that multiple changes in G1 DNA content and cell volume occurred in Thalassiosira evolution, leading to a 1,000‐fold range in both parameters in the group. Within the Thalassiosira weissflogii (Grunow) G. A. Fryxell et Grunow species complex, G1 DNA content varied 3‐fold: differences related to geographic origin and time since isolation; doubling and tripling of G1 DNA content occurred since isolation in certain T. weissflogii isolates; and subcultures of T. weissflogii CCMP 1336 diverged in DNA content by 50% within 7 years of separation. Actin, β‐tubulin, and Spo11/TopVIA genes were selected for quantitative PCR estimation of haploid genome size in subclones of selected T. weissflogii isolates because they occur only once in the T. pseudonana Hasle et Heimdal genome. Comparison of haploid genome size estimates with G1 DNA content suggested that the most recent T. weissflogii isolate was diploid, whereas other T. weissflogii isolates appeared to be polyploid and/or aneuploid. Aberrant meiotic and mitotic cell divisions were observed, which might relate to polyploidization. The structural flexibility of diatom genomes has important implications for their evolutionary diversification and stability during laboratory maintenance.     

Null model analyses of temporal patterns of bird assemblages and their foraging guilds revealed the predominance of positive and random associations

Patterns of species associations have been commonly used to infer interactions among species. If species positively co‐occur, they may form predominantly neutral assemblages, and such patterns suggest a relatively weak role for compensatory dynamics. The main objective of this study was to test this prediction on temporal samples of bird assemblages (n = 19, 10–57 years) by the presence/absence and quantitative null models on assemblage and guild levels. These null model outcomes were further analyzed to evaluate the effects of various data set characteristics on the outcomes of the null models. The analysis of two binary null models in combination with three association indices revealed 20% with significant aggregations, 61% with random associations, and only 19% with significant segregations (n = 95 simulations). The results of the quantitative null model simulations detected more none‐random associations: 61% aggregations, 6% random associations, and 33% segregations (n = 114 simulations). Similarly, quantitative analyses on guild levels showed 58% aggregations, 20% segregations, and 22% random associations (n = 450 simulations). Bayesian GLMs detected that the outcomes of the binary and quantitative null models applied to the assemblage analyses were significantly related to census plot size, whereas the outcomes of the quantitative analyses were also related to the mean population densities of species in the data matrices. In guild‐level analyses, only 9% of the GLMs showed a significant influence of matrix properties (plot size, matrix size, species richness, and mean species population densities) on the null model outcomes. The results did not show the prevalence of negative associations that would have supported compensatory dynamics. Instead, we assume that a similar response of the majority of species to climate‐driven and stochastic factors may be responsible for the revealed predominance of positive associations.     

Simulating the effects of climate change on the distribution of an invasive plant,using a high resolution,local scale,mechanistic approach: challenges and insights

The growing economic and ecological damage associated with biological invasions, which will likely be exacerbated by climate change, necessitates improved projections of invasive spread. Generally, potential changes in species distribution are investigated using climate envelope models; however, the reliability of such models has been questioned and they are not suitable for use at local scales. At this scale, mechanistic models are more appropriate. This paper discusses some key requirements for mechanistic models and utilises a newly developed model (PSS[gt]) that incorporates the influence of habitat type and related features (e.g., roads and rivers), as well as demographic processes and propagule dispersal dynamics, to model climate induced changes in the distribution of an invasive plant (Gunnera tinctoria) at a local scale. A new methodology is introduced, dynamic baseline benchmarking, which distinguishes climate‐induced alterations in species distributions from other potential drivers of change. Using this approach, it was concluded that climate change, based on IPCC and C4i projections, has the potential to increase the spread‐rate and intensity of G. tinctoria invasions. Increases in the number of individuals were primarily due to intensification of invasion in areas already invaded or in areas projected to be invaded in the dynamic baseline scenario. Temperature had the largest influence on changes in plant distributions. Water availability also had a large influence and introduced the most uncertainty in the projections. Additionally, due to the difficulties of parameterising models such as this, the process has been streamlined by utilising methods for estimating unknown variables and selecting only essential parameters.     

Competing G protein‐coupled receptor kinases balance G protein and β‐arrestin signaling

Seven‐transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through β‐arrestins, whose recruitment to the activated receptor is regulated by G protein‐coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal‐regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT_1AR) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)‐based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well‐established function in the desensitization of G‐protein activation, GRK2 exerts a strong negative effect on β‐arrestin‐dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2‐dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT_1AR, and HEK293 cells expressing other 7TMRs.     

Predictive power of food web models based on body size decreases with trophic complexity

Food web models parameterised using body size show promise to predict trophic interaction strengths (IS) and abundance dynamics. However, this remains to be rigorously tested in food webs beyond simple trophic modules, where indirect and intraguild interactions could be important and driven by traits other than body size. We systematically varied predator body size, guild composition and richness in microcosm insect webs and compared experimental outcomes with predictions of IS from models with allometrically scaled parameters. Body size was a strong predictor of IS in simple modules (r² = 0.92), but with increasing complexity the predictive power decreased, with model IS being consistently overestimated. We quantify the strength of observed trophic interaction modifications, partition this into density‐mediated vs. behaviour‐mediated indirect effects and show that model shortcomings in predicting IS is related to the size of behaviour‐mediated effects. Our findings encourage development of dynamical food web models explicitly including and exploring indirect mechanisms.     

Arrhenius and Gleason revisited: new hybrid models resolve an old controversy

Aim Studies have typically employed species–area relationships (SARs) from sample areas to fit either the power relationship or the logarithmic (exponential) relationship. However, the plots from empirical data often fall between these models. This article proposes two complementary and hybrid models as solutions to the controversy regarding which model best fits sample‐area SARs. Methods The two models are and , where S_A is number of species in an area, A, where z, b, c₁ and c₂ are predetermined parameters found by calculation, and where d and n are parameters to be fitted. The number of parameters is reduced from six to two by fixing the model at either end of the scale window of the data set, a step that is justified by the condition that the error or the bias, or both, in the first and the last data points is negligible. The new hybrid models as well as the power model and the logarithmic model are fitted to 10 data sets. Results The two proposed models fit well not only to Arrhenius’ and Gleason’s data sets, but also to the other six data sets. They also provide a good fit to data sets that follow a sigmoid (or triphasic) shape in log–log space and to data sets that do not fall between the power model and the logarithmic model. The log‐transformation of the dependent variable, S, does not affect the curve fit appreciably, although it enhances the performance of the new models somewhat. Main conclusions Sample‐area SARs have previously been shown to be convex upward, convex downward (concave), sigmoid and inverted sigmoid in log–log space. The new hybrid models describe successfully data sets with all these curve shapes, and should therefore produce good fits also to what are termed triphasic SARs.     

Detection of Biomarkers of Pathogenic Naegleria fowleri Through Mass Spectrometry and Proteomics

Emerging methods based on mass spectrometry (MS) can be used in the rapid identification of microorganisms. Thus far, these practical and rapidly evolving methods have mainly been applied to characterize prokaryotes. We applied matrix‐assisted laser‐desorption‐ionization‐time‐of‐flight mass spectrometry MALDI‐TOF MS in the analysis of whole cells of 18 N. fowleri isolates belonging to three genotypes. Fourteen originated from the cerebrospinal fluid or brain tissue of primary amoebic meningoencephalitis patients and four originated from water samples of hot springs, rivers, lakes or municipal water supplies. Whole Naegleria trophozoites grown in axenic cultures were washed and mixed with MALDI matrix. Mass spectra were acquired with a 4700 TOF‐TOF instrument. MALDI‐TOF MS yielded consistent patterns for all isolates examined. Using a combination of novel data processing methods for visual peak comparison, statistical analysis and proteomics database searching we were able to detect several biomarkers that can differentiate all species and isolates studied, along with common biomarkers for all N. fowleri isolates. Naegleria fowleri could be easily separated from other species within the genus Naegleria. A number of peaks detected were tentatively identified. MALDI‐TOF MS fingerprinting is a rapid, reproducible, high‐throughput alternative method for identifying Naegleria isolates. This method has potential for studying eukaryotic agents.     

Prenatal data impacts common bottlenose dolphin (Tursiops truncatus) growth parameters estimated by length‐at‐age curves

Compilation of marine mammal demographic data is central to management efforts. However, marine mammal length‐at‐age growth curves demonstrate limitations. Physiological growth parameters of terrestrial mammals are typically estimated using curvilinear models fit to size‐at‐age data along a time series from conception to senescence. The difficulty of collecting and aging prenatal cetaceans is addressed here, and growth parameters of common bottlenose dolphins (Tursiops truncatus) along coastal Texas were estimated using length‐at‐age information from a broader scope of age classes, including late‐term fetuses. A Gompertz growth curve fit to pre‐ and postnatal data underestimated size parameters, but demonstrated similar growth rate constants (k) to an exclusively postnatal model. However, when growth parameters were broken out, the absolute growth rate (G) and rate of growth decay (g) decreased (0.44 from 0.27 and 0.55 from 0.39, respectively), which underscores the importance of reporting k in its expanded form (G/g). Although the Gompertz fits most age classes well, it cannot explain growth in all age classes. We argue that a novel sigmoidal model would be more useful for inference.     

Sterols of the Green‐Pigmented,Freshwater Raphidophyte,Gonyostomum semen,from Scandinavian Lakes

Sterols are a class of membrane‐reinforcing, ringed lipids which have a long history of examination in algae as a means of deriving chemotaxonomic relationships and as potential lipidic biomarkers. The Raphidophyceae represent a class of harmful, bloom‐forming, marine and freshwater algae. To date, there have been four published examinations of their sterol composition, focusing primarily on brown‐pigmented, marine species within the genera, Chattonella, Fibrocapsa, and Heterosigma. Lacking in these examinations has been the species Gonyostomum semen Ehrenb., which is a green‐pigmented, freshwater raphidophyte with a worldwide distribution. The goal of this study was to examine the sterol composition of this nuisance alga, determine the potential of using its sterol profile as a biomarker, and finally to determine if there is any intraspecific variability between isolates. We have examined 21 isolates of G. semen from a number of Scandinavian lakes, and all were found to produce two major sterols, 24‐ethylcholesta‐5,22E‐dien‐3β‐ol and 24‐ethylcholest‐5‐en‐3β‐ol, and 24‐methylcholest‐5‐en‐3β‐ol as a minor sterol; the presence of 24‐ethylcholesta‐5,22E‐dien‐3β‐ol differentiates G. semen from brown‐pigmented, marine raphidophytes which generally lack it. The results of this study indicate that isolates of G. semen from geographically separate lakes across Finland and Scandinavia have the same sterol biosynthetic pathway, and that there is no evolutionary divergence between the isolates with regard to sterol composition. The sterols of G. semen are not considered to be useful biomarkers for this particular organism because they are commonly found in other algae and plants.     

Dynamic gene expression regulation model for growth and penicillin production in Penicillium chrysogenum

As is often the case for microbial product formation, the penicillin production rate of Penicillium chrysogenum has been observed to be a function of the growth rate of the organism. The relation between the biomass specific rate of penicillin formation (q_p) and growth rate (µ) has been measured under steady state conditions in carbon limited chemostats resulting in a steady state q_p(µ) relation. Direct application of such a relation to predict the rate of product formation during dynamic conditions, as they occur, for example, in fed‐batch experiments, leads to errors in the prediction, because q_p is not an instantaneous function of the growth rate but rather lags behind because of adaptational and regulatory processes. In this paper a dynamic gene regulation model is presented, in which the specific rate of penicillin production is assumed to be a linear function of the amount of a rate‐limiting enzyme in the penicillin production pathway. Enzyme activity assays were performed and strongly indicated that isopenicillin‐N synthase (IPNS) was the main rate‐limiting enzyme for penicillin‐G biosynthesis in our strain. The developed gene regulation model predicts the expression of this rate limiting enzyme based on glucose repression, fast decay of the mRNA encoding for the enzyme as well as the decay of the enzyme itself. The gene regulation model was combined with a stoichiometric model and appeared to accurately describe the biomass and penicillin concentrations for both chemostat steady‐state as well as the dynamics during chemostat start‐up and fed‐batch cultivation. Biotechnol. Bioeng. 2010;106: 608–618. © 2010 Wiley Periodicals, Inc.     

A field test of a model for the stability of androdioecy in the freshwater shrimp,Eulimnadia texana

The evolution of hermaphroditism from dioecy is a poorly studied transition. Androdioecy (the coexistence of males and hermaphrodites) has been suggested as an intermediate step in this evolutionary transition or could be a stable reproductive mode. Freshwater crustaceans in the genus Eulimnadia have reproduced via androdioecy for 24+ million years and thus are excellent organisms to test models of the stability of androdioecy. Two related models that allow for the stable maintenance of males and hermaphrodites rely on the counterbalancing of three life history parameters. We tested these models in the field over three field seasons and compared the results to previous laboratory estimates of these three parameters. Male and hermaphroditic ratios within years were not well predicted using either the simpler original model or a version of this model updated to account for differences between hermaphroditic types (‘monogenic’ and ‘amphigenic’ hermaphrodites). Using parameter estimates of the previous year to predict the next year's sex ratios revealed a much better fit to the original relative to the updated version of the model. Therefore, counter to expectations, accounting for differences between the two hermaphroditic types did not improve the fit of these models. At the moment, we lack strong evidence that the long‐term maintenance of androdioecy in these crustaceans is the result of a balancing of life history parameters; other factors, such as metapopulation dynamics or evolutionary constraints, may better explain the 24+ million year maintenance of androdioecy in clam shrimp.     

Clinical relevance and functional implications for human leucocyte antigen‐g expression in non‐small‐cell lung cancer

HLA‐G has been documented both in establishment of anti‐tumour immune responses and in tumour evasion. To investigate the clinical relevance of HLA‐G in non‐small‐cell lung cancer (NSCLC), expression status and potential significance of HLA‐G in NSCLC were analysed. In this study, HLA‐G expression in 101 NSCLC primary lesions and plasma soluble HLA‐G (sHLA‐G) from 91 patients were analysed with immunohistochemistry and ELISA, respectively. Correlations between HLA‐G status and various clinical parameters including survival time were evaluated. Meanwhile, functional analysis of transfected cell surface HLA‐G expression and plasma sHLA‐G form NSCLC patients on natural killer (NK) cell cytolysis were performed. Data revealed that HLA‐G was expressed in 41.6% (42/101) NSCLC primary lesions, while undetectable in adjacent normal lung tissues. HLA‐G expression in NSCLC lesions was strongly correlated to disease stages (P= 0.002). Plasma sHLA‐G from NSCLC patients was markedly higher than that in normal controls (P= 0.004), which was significantly associated with the disease stages (I versus IV, P= 0.025; II versus IV, P= 0.029). Patient plasma sHLA‐G level (≥median, 32.0 U/ml) had a significantly shorter survival time (P= 0.044); however, no similar significance was observed for the lesion HLA‐G expression. In vitro data showed that both cell surface HLA‐G and patient plasma sHLA‐G could dramatically decrease the NK cell cytolysis. Our findings indicated that both lesion HLA‐G expression and plasma sHLA‐G in NSCLC is related to the disease stage and can exert immunosuppression to the NK cell cytolysis, indicating that HLA‐G could be a potential therapeutic target. Moreover, plasma sHLA‐G in NSCLC patients could be used as a prognosis factor for NSCLC.     

Choosing the right sigmoid growth function using the unified‐models approach

Growth of the young is an important part of the life history in birds. However, modelling methods have paid little attention to the choice of regression model used to describe its pattern. The aim of this study was to evaluate whether a single sigmoid model with an upper asymptote could describe avian growth adequately. We compared unified versions of five growth models of the Richards family (the four‐parameter U‐Richards and the three‐parameter U‐logistic, U‐Gompertz, U‐Bertalanffy and U4‐models) for three traits (body mass, tarsus‐length and wing‐length) for 50 passerine species, including species with varied morphologies and life histories. The U‐family models exhibit a unified set of parameters for all models. The four‐parameter U‐Richards model proved a good choice for fitting growth curves to various traits – its extra d‐parameter allows for a flexible placement of the inflection point. Which of the three‐parameter U‐models was the best performing varied greatly between species and between traits, as each three‐parameter model had a different fixed relative inflection value (fraction of the upper asymptote), implying a different growth pattern. Fixing the asymptotes to averages for adult trait value generally shifted the model preference towards one with lower relative inflection values. Our results illustrate an overlooked difficulty in the analysis of organismal growth, namely, that a single traditional three‐parameter model does not suit all growth data. This is mostly due to differences in inflection placement. Moreover, some biometric traits require more attention when estimating growth rates and other growth‐curve characteristics. We recommend fitting either several three‐parameter models from the U‐family, where the parameters are comparable between models, or only the U‐Richards model.     

Folding thermodynamics of β‐hairpins studied by replica‐exchange molecular dynamics simulations

We study the differences in folding stability of β‐hairpin peptides, including GB1 hairpin and a point mutant GB1 K10G, as well as tryptophan zippers (TrpZips): TrpZip1, TrpZip2, TrpZip3‐1, and TrpZip4. By performing replica‐exchange molecular dynamics simulations with Amber03* force field (a modified version of Amber ff03) in explicit solvent, we observe ab initio folding of all the peptides except TrpZip3‐1, which is experimentally known to be the least stable among the peptides studied here. By calculating the free energies of unfolding of the peptides at room temperature and folding midpoint temperatures for thermal unfolding of peptides, we find that TrpZip4 and GB1 K10G peptides are the most stable β‐hairpins followed by TrpZip1, GB1, and TrpZip2 in the given order. Hence, the proposed K10G mutation of GB1 peptide results in enhanced stability compared to wild‐type GB1. An important goal of our study is to test whether simulations with Amber 03* model can reproduce experimentally predicted folding stability differences between these peptides. While the stabilities of GB1 and TrpZip1 yield close agreement with experiment, TrpZip2 is found to be less stable than predicted by experiment. However, as heterogenous folding of TrpZip2 may yield divergent thermodynamic parameters by different spectroscopic methods, mismatching of results with previous experimental values are not conclusive of model shortcomings. For most of the cases, molecular simulations with Amber03* can successfully reproduce experimentally known differences between the mutated peptides, further highlighting the predictive capabilities of current state‐of‐the‐art all‐atom protein force fields. Proteins 2015; 83:1307–1315. © 2015 Wiley Periodicals, Inc.     

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.     

G4 motifs affect origin positioning and efficiency in two vertebrate replicators

DNA replication ensures the accurate duplication of the genome at each cell cycle. It begins at specific sites called replication origins. Genome‐wide studies in vertebrates have recently identified a consensus G‐rich motif potentially able to form G‐quadruplexes (G4) in most replication origins. However, there is no experimental evidence to demonstrate that G4 are actually required for replication initiation. We show here, with two model origins, that G4 motifs are required for replication initiation. Two G4 motifs cooperate in one of our model origins. The other contains only one critical G4, and its orientation determines the precise position of the replication start site. Point mutations affecting the stability of this G4 in vitro also impair origin function. Finally, this G4 is not sufficient for origin activity and must cooperate with a 200‐bp cis‐regulatory element. In conclusion, our study strongly supports the predicted essential role of G4 in replication initiation.     

Hydrogenosomes of Laboratory‐Induced Metronidazole‐Resistant Trichomonas vaginalis Lines are Downsized While Those from Clinically Metronidazole‐Resistant Isolates Are Not

ABSTRACT. Trichomonas vaginalis is the most common sexually transmitted protozoan in the world and its resistance to metronidazole is increasing. The purpose of this study was to demonstrate that clinical metronidazole resistance in T. vaginalis does not occur via the same mechanism as laboratory‐induced metronidazole resistance—that is, via hydrogenosome down sizing. Ultrathin sections of this parasite were examined using transmission electron microscopy and the size and area of the cell and hydrogenosomes were compared between drug‐resistant laboratory lines and clinically resistant isolates. Clinical metronidazole‐resistant T. vaginalis had similar‐sized hydrogenosomes as a metronidazole‐sensitive isolate. Inducing metronidazole resistance in both of these isolates caused down sizing of hydrogenosomes. Inducing toyocamycin resistance did not cause any ultrastructural changes to the cell or to the hydrogenosome. No correlation between hydrogenosome number and the drug‐resistant status of T. vaginalis isolates and lines was observed. This report demonstrates that clinical metronidazole resistance is not associated with down‐sized hydrogenosomes, thus indicating that an alternative resistance mechanism is used by T. vaginalis.     

Using dynamic N‐mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation

Dynamic N‐mixture models have been recently developed to estimate demographic parameters of unmarked individuals while accounting for imperfect detection. We propose an application of the Dail and Madsen ( 2011 : Biometrics, 67 , 577–587) dynamic N‐mixture model in a manipulative experiment using a before‐after control‐impact design (BACI). Specifically, we tested the hypothesis of cavity limitation of a cavity specialist species, the northern flying squirrel, using nest box supplementation on half of 56 trapping sites. Our main purpose was to evaluate the impact of an increase in cavity availability on flying squirrel population dynamics in deciduous stands in northwestern Québec with the dynamic N‐mixture model. We compared abundance estimates from this recent approach with those from classic capture–mark–recapture models and generalized linear models. We compared apparent survival estimates with those from Cormack–Jolly–Seber (CJS) models. Average recruitment rate was 6 individuals per site after 4 years. Nevertheless, we found no effect of cavity supplementation on apparent survival and recruitment rates of flying squirrels. Contrary to our expectations, initial abundance was not affected by conifer basal area (food availability) and was negatively affected by snag basal area (cavity availability). Northern flying squirrel population dynamics are not influenced by cavity availability at our deciduous sites. Consequently, we suggest that this species should not be considered an indicator of old forest attributes in our study area, especially in view of apparent wide population fluctuations across years. Abundance estimates from N‐mixture models were similar to those from capture–mark–recapture models, although the latter had greater precision. Generalized linear mixed models produced lower abundance estimates, but revealed the same relationship between abundance and snag basal area. Apparent survival estimates from N‐mixture models were higher and less precise than those from CJS models. However, N‐mixture models can be particularly useful to evaluate management effects on animal populations, especially for species that are difficult to detect in situations where individuals cannot be uniquely identified. They also allow investigating the effects of covariates at the site level, when low recapture rates would require restricting classic CMR analyses to a subset of sites with the most captures.