Partitioned Gene-Tree Analyses and Gene-Based Topology Testing Help Resolve Incongruence in a Phylogenomic Study of Host-Specialist Bees (Apidae: Eucerinae)

Incongruence among phylogenetic results has become a common occurrence in analyses of genome-scale data sets. Incongruence originates from uncertainty in underlying evolutionary processes (e.g., incomplete lineage sorting) and from difficulties in determining the best analytical approaches for each situation. To overcome these difficulties, more studies are needed that identify incongruences and demonstrate practical ways to confidently resolve them. Here, we present results of a phylogenomic study based on the analysis 197 taxa and 2,526 ultraconserved element (UCE) loci. We investigate evolutionary relationships of Eucerinae, a diverse subfamily of apid bees (relatives of honey bees and bumble bees) with >1,200 species. We sampled representatives of all tribes within the group and >80% of genera, including two mysterious South American genera, Chilimalopsis and Teratognatha. Initial analysis of the UCE data revealed two conflicting hypotheses for relationships among tribes. To resolve the incongruence, we tested concatenation and species tree approaches and used a variety of additional strategies including locus filtering, partitioned gene-trees searches, and gene-based topological tests. We show that within-locus partitioning improves gene tree and subsequent species-tree estimation, and that this approach, confidently resolves the incongruence observed in our data set. After exploring our proposed analytical strategy on eucerine bees, we validated its efficacy to resolve hard phylogenetic problems by implementing it on a published UCE data set of Adephaga (Insecta: Coleoptera). Our results provide a robust phylogenetic hypothesis for Eucerinae and demonstrate a practical strategy for resolving incongruence in other phylogenomic data sets.     

An improved phylogeny of the Andean tit-tyrants (Aves, Tyrannidae): more characters trump sophisticated analyses

The phylogeny of the flycatcher genus Anairetes was previously inferred using short fragments of mitochondrial DNA and parsimony and distance-based methods. The resulting topology spurred taxonomic revision and influenced understanding of Andean biogeography. More than a decade later, we revisit the phylogeny of Anairetes tit-tyrants using more mtDNA characters, seven unlinked loci (three mitochondrial genes, six nuclear loci), more closely related outgroup taxa, partitioned Bayesian analyses, and two coalescent species-tree approaches (Bayesian estimation of species trees, BEST; Bayesian evolutionary analysis by sampling trees, (*)BEAST). Of these improvements in data and analyses, the fourfold increase in mtDNA characters was both necessary and sufficient to incur a major shift in the topology and near-complete resolution. The species-tree analyses, while theoretically preferable to concatenation or single gene approaches, yielded topologies that were compatible with mtDNA but with weaker statistical resolution at nodes. The previous results that had led to taxonomic and biogeographic reappraisal were refuted, and the current results support the resurrection of the genus Uromyias as the sister clade to Anairetes. The sister relationship between these two genera corresponds to an ecological dichotomy between a depauperate humid cloud forest clade and a diverse dry-tolerant clade that has diversified along the latitudinal axis of the Andes. The species-tree results and the concatenation results each reaffirm the primacy of mtDNA to provide phylogenetic signal for avian phylogenies at the species and subspecies level. This is due in part to the abundance of informative characters in mtDNA, and in part to its lower effective population size that causes it to more faithfully track the species tree.     

Complexity of the simplest species tree problem

The multispecies coalescent model provides a natural framework for species tree estimation accounting for gene-tree conflicts. Although a number of species tree methods under the multispecies coalescent have been suggested and evaluated using simulation, their statistical properties remain poorly understood. Here, we use mathematical analysis aided by computer simulation to examine the identifiability, consistency, and efficiency of different species tree methods in the case of three species and three sequences under the molecular clock. We consider four major species-tree methods including concatenation, two-step, independent-sites maximum likelihood, and maximum likelihood. We develop approximations that predict that the probit transform of the species tree estimation error decreases linearly with the square root of the number of loci. Even in this simplest case, major differences exist among the methods. Full-likelihood methods are considerably more efficient than summary methods such as concatenation and two-step. They also provide estimates of important parameters such as species divergence times and ancestral population sizes,whereas these parameters are not identifiable by summary methods. Our results highlight the need to improve the statistical efficiency of summary methods and the computational efficiency of full likelihood methods of species tree estimation.     

Phylogeny estimation of the radiation of western North American chipmunks (Tamias) in the face of introgression using reproductive protein genes

The causes and consequences of rapid radiations are major unresolved issues in evolutionary biology. This is in part because phylogeny estimation is confounded by processes such as stochastic lineage sorting and hybridization. Because these processes are expected to be heterogeneous across the genome, comparison among marker classes may provide a means of disentangling these elements. Here we use introns from nuclear-encoded reproductive protein genes expected to be resistant to introgression to estimate the phylogeny of the western chipmunks (Tamias: subgenus: Neotamias), a rapid radiation that has experienced introgressive hybridization of mitochondrial DNA (mtDNA). We analyze the nuclear loci using coalescent-based species-tree estimation methods and concatenation to estimate a species tree and we use parametric bootstraps and coalescent simulations to differentiate between phylogenetic error, coalescent stochasticity and introgressive hybridization. Results indicate that the mtDNA gene tree reflects several introgression events that have occurred between taxa of varying levels of divergence and at different time points in the tree. T. panamintinus and T. speciosus appear to be fixed for ancient mitochondrial introgressions from T. minimus. A southern Rocky Mountains clade appears well sorted (i.e., species are largely monophyletic) at multiple nuclear loci, while five of six taxa are nonmonophyletic based on cytochrome b. Our simulations reject phylogenetic error and coalescent stochasticity as causes. The results represent an advance in our understanding of the processes at work during the radiation of Tamias and suggest that sampling reproductive-protein genes may be a viable strategy for phylogeny estimation of rapid radiations in which reproductive isolation is incomplete. However, a genome-scale survey that can statistically compare heterogeneity of genealogical process at many more loci will be necessary to test this conclusion.     

Using hierarchical models to compare the sensitivity of metabarcoding and qPCR for eDNA detection

Environmental DNA (eDNA) sampling—the detection of intra- or extra-cellular DNA in environmental samples—is a rapid and sensitive survey method for detecting aquatic species. Single-species detection methods (typically based on PCR or LAMP) have been shown to be more sensitive for detecting target species than multi-species detection methods, such as metabarcoding. However, previous studies have generally only compared these two eDNA detection approaches for a single target species and have used different methodological and statistical approaches. Here we present a comparison of single- and multi-species eDNA detection methods, drawing on two published case studies (one fish, one amphibian) and two new extensive datasets on a freshwater mammal (the platypus). To ensure consistent conclusions regarding the sensitivity of each eDNA method, we use the same hierarchical site occupancy-detection model for each dataset, incorporating uncertainty at the site, water sample, and technical replicate level. Overall, qPCR achieved higher detection probabilities than metabarcoding across species and datasets. However, differences in sensitivity between detection methods varied depending on methodological decisions concerning what constitutes a true positive detection (i.e., qPCR and metabarcoding thresholds). The decision as to which eDNA detection method to use should always be influenced by the study aims, but our results suggest that single-species detection methods based on qPCR may be preferable when the aim is to achieve a high detection probability for target species.     

Addressing Temporal Variability in Bird Calling with Design and Estimation: A Northern Bobwhite Example

Imprecise or biased density estimates can lead to inadequate conservation action, overexploitation of game species, or lost recreational opportunities. Common approaches to estimating density of avian populations often either ignore the probability that an individual is present within the sampling area but is not available to be sampled (e.g., not vocalizing), or do not consider covariates that could influence availability. Additionally, management decisions made at the management unit scale are often informed by inadequate monitoring practices, such as limited sampling intensity. In such cases, management agencies calculate density by applying correction factors (e.g., detection probabilities estimated using empirical data from a different study system) to count data, rather than estimating a detection function directly using statistical models. We conducted a simulation study using northern bobwhite (Colinus virginianus; bobwhite) as a model species to quantify the consequences of mis-specifying avian point count models on bias and precision of density estimates. We compared bias and precision of estimates from a fully specified distance-sampling model that estimates availability and detection to 4 different mis-specified approaches, including 2 approaches to calculating density using correction factors. Using correction factors to calculate density produced estimates with low bias but relatively lower precision compared to the fully specified model (CV of density estimates at 35 sites over 5 years: fully specified = 10%, correction factors = 25% and 30%). Although the mean precision and bias of the fully specified model improved with more data (70 sites over 5 years, CV = 9%; 35 sites over 10 years, CV = 9%), precision of correction factors did not (70 sites over 5 years, CV = 22% and 27%; 35 sites over 10 years, CV = 24% and 29%). The fully specified model captured the underlying temporal variation in detection and availability. Increasing sampling duration from 5 to 10 years improved modeled estimates of growth rate, even for mis-specified models, but not derived growth rates using pre-determined detection functions. We demonstrated that conducting point counts 3 times/year at a feasible number of sites can produce relatively unbiased estimates of bobwhite density. Pre-determined detection functions can be fortuitously unbiased for certain years, but they are not a reliable method for determining density or identifying trends in density over time. © 2020 The Wildlife Society.     

The Erwin equation of biodiversity: From little steps to quantum leaps in the discovery of tropical insect diversity

Almost 40 years ago, Terry L. Erwin published a seemingly audacious proposition: There may be as many as 30 million species of insects in the world. Here, we translate Erwin's verbal argument into a diversity-ratio model—the Erwin Equation of Biodiversity—and discuss how it has inspired other biodiversity estimates. We categorize, describe the assumptions for, and summarize the most commonly used methods for calculating estimates of global biodiversity. Subsequent diversity-ratio extrapolations have incorporated parameters representing empirical insect specialization ratios, and how insect specialization changes at different spatial scales. Other approaches include macroecological diversity models and diversity curves. For many insect groups with poorly known taxonomies, diversity estimates are based on the opinions of taxonomic experts. We illustrate our current understanding of insect diversity by focusing on the six most speciose insect orders worldwide. For each order, we compiled estimates of the (a) maximum estimated number of species, (b) minimum estimated number of species, and (c) number of currently described species. By integrating these approaches and considering new information, we believe an estimate of 5.5 million species of insects in the world is much too low. New molecular methodologies (e.g., metabarcoding and NGS studies) are revealing daunting numbers of cryptic and previously undescribed species, at the same time increasing our precision but also uncertainty about present estimates. Not until technologies advance and sampling become more comprehensive, especially of tropical biotas, will we be able to make robust estimates of the total number of insect species on Earth.     

Increased taxon sampling greatly reduces phylogenetic error

Several authors have argued recently that extensive taxon sampling has a positive and important effect on the accuracy of phylogenetic estimates. However, other authors have argued that there is little benefit of extensive taxon sampling, and so phylogenetic problems can or should be reduced to a few exemplar taxa as a means of reducing the computational complexity of the phylogenetic analysis. In this paper we examined five aspects of study design that may have led to these different perspectives. First, we considered the measurement of phylogenetic error across a wide range of taxon sample sizes, and conclude that the expected error based on randomly selecting trees (which varies by taxon sample size) must be considered in evaluating error in studies of the effects of taxon sampling. Second, we addressed the scope of the phylogenetic problems defined by different samples of taxa, and argue that phylogenetic scope needs to be considered in evaluating the importance of taxon-sampling strategies. Third, we examined the claim that fast and simple tree searches are as effective as more thorough searches at finding near-optimal trees that minimize error. We show that a more complete search of tree space reduces phylogenetic error, especially as the taxon sample size increases. Fourth, we examined the effects of simple versus complex simulation models on taxonomic sampling studies. Although benefits of taxon sampling are apparent for all models, data generated under more complex models of evolution produce higher overall levels of error and show greater positive effects of increased taxon sampling. Fifth, we asked if different phylogenetic optimality criteria show different effects of taxon sampling. Although we found strong differences in effectiveness of different optimality criteria as a function of taxon sample size, increased taxon sampling improved the results from all the common optimality criteria. Nonetheless, the method that showed the lowest overall performance (minimum evolution) also showed the least improvement from increased taxon sampling. Taking each of these results into account re-enforces the conclusion that increased sampling of taxa is one of the most important ways to increase overall phylogenetic accuracy.     

Hierarchical Bayesian integrated model for estimating migratory bird harvest in Canada

The Canadian Wildlife Service (CWS) requires reliable estimates of the harvest of migratory game birds, including waterfowl, to effectively manage populations of these hunted species. The National Harvest Survey is an annual survey of hunters who purchase Canada's mandatory migratory game bird hunting permit, integrating information from a survey of hunting activity with information from a separate survey of species composition in the harvest. We used these survey data to estimate the number of birds harvested for each species and hunting activity metrics (e.g., number of active hunters, days spent hunting). The analytical methods used to generate these estimates have not changed since the survey was first designed in the early 1970s. We describe a new hierarchical Bayesian integrated model, which replaces the series of ratio estimators that comprised the old model. We are using this new model to generate estimates for migratory bird harvests as of the 2019–2020 hunting season, and to generate updated estimates for all earlier years. The hierarchical Bayesian model uses over-dispersed Poisson distributions to model mean hunter activity and harvest (zero-inflated Poisson and zero-truncated Poisson, respectively). It also includes multinomial distributions to model some key components (e.g., variation in harvest across periods of the hunting season, the species composition of the harvest within each of those periods, the age and sex composition in the harvests of a given species). We estimated the parameters of the Poisson and the multinomial distributions for each year as random effects using first-difference time-series. This time-series component allows the model to share information across years and reduces the sensitivity of the estimates to annual sampling noise. The new model estimates are generally very similar to those from the old model, particularly for the species that occur most commonly in the harvest, so the results do not suggest any major changes to harvest management decisions and regulations. Estimates for all species from the new model are more precise and less susceptible to annual sampling error, particularly for species that occur less commonly in the harvest (e.g., sea ducks, other species of conservation concern). This new model, with its hierarchical Bayesian framework, will also facilitate future improvements and elaborations, allowing the incorporation of prior information from the rich literature and knowledge in game bird management and biology.     

Combining Distance Matrices on Identical Taxon Sets for Multi-Gene Analysis with Singular Value Decomposition

We present a simple and effective method for combining distance matrices from multiple genes on identical taxon sets to obtain a single representative distance matrix from which to derive a combined-gene phylogenetic tree. The method applies singular value decomposition (SVD) to extract the greatest common signal present in the distances obtained from each gene. The first right eigenvector of the SVD, which corresponds to a weighted average of the distance matrices of all genes, can thus be used to derive a representative tree from multiple genes. We apply our method to three well known data sets and estimate the uncertainty using bootstrap methods. Our results show that this method works well for these three data sets and that the uncertainty in these estimates is small. A simulation study is conducted to compare the performance of our method with several other distance based approaches (namely SDM, SDM* and ACS97), and we find the performances of all these approaches are comparable in the consensus setting. The computational complexity of our method is similar to that of SDM. Besides constructing a representative tree from multiple genes, we also demonstrate how the subsequent eigenvalues and eigenvectors may be used to identify if there are conflicting signals in the data and which genes might be influential or outliers for the estimated combined-gene tree.     

Using measurement error models to account for georeferencing error in species distribution models

Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data‐level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data‐level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence‐only data as well as to include other sources of uncertainty in species distribution models.     

Inferring host specificity and network formation through agent-based models: tick–mammal interactions in Borneo

Patterns of host–parasite association are poorly understood in tropical forests. While we typically observe only snapshots of the diverse assemblages and interactions under variable conditions, there is a desire to make inferences about prevalence and host-specificity patterns. We studied the interaction of ticks with non-volant small mammals in forests of Borneo. We inferred the probability of species interactions from individual-level data in a multi-level Bayesian model that incorporated environmental covariates and advanced estimates for rarely observed species through model averaging. We estimated the likelihood of observing particular interaction frequencies under field conditions and a scenario of exhaustive sampling and examined the consequences for inferring host specificity. We recorded a total of 13 different tick species belonging to the five genera Amblyomma, Dermacentor, Haemaphysalis, Ixodes, and Rhipicephalus from a total of 37 different host species (Rodentia, Scandentia, Carnivora, Soricidae) on 237 out of 1,444 host individuals. Infestation probabilities revealed most variation across host species but less variation across tick species with three common rat and two tree shrew species being most heavily infested. Host species identity explained ca. 75 % of the variation in infestation probability and another 8–10 % was explained by local host abundance. Host traits and site-specific attributes had little explanatory power. Host specificity was estimated to be similarly low for all tick species, which were all likely to infest 34–37 host species if exhaustively sampled. By taking into consideration the hierarchical organization of individual interactions that may take place under variable conditions and that shape host–parasite networks, we can discern uncertainty and sampling bias from true interaction frequencies, whereas network attributes derived from observed values may lead to highly misleading results. Multi-level approaches may help to move this field towards inferential approaches for understanding mechanisms that shape the strength and dynamics in ecological networks.     

Leaf phenology in relation to canopy closure in southern Appalachian trees

Leaf phenology varies markedly across tree species of temperate deciduous forests. Early leafing in spring may increase light capture and carbon gain prior to canopy closure, allowing saplings to survive in understory sites deeply shaded in midsummer. We quantified sapling leaf phenology for 18 tree species and seasonal variation in understory light availability at three sites along a ridge-slope-cove landform gradient in the Great Smoky Mountains National Park. Early leafing species (e.g., Aesculus flava, Carpinus caroliniana) broke bud an average of 24 d before late leafers (e.g., Magnolia fraseri, Nyssa sylvatica). Canopy closure occurred 14-18 d earlier and summer understory light was on average 63-74% lower on intermediate and mesic sites than on the xeric site. Early leafing species intercepted 45-80% of their growing season photon flux before canopy closure vs. 8-15% for late leafers. However, earlier leafing increased exposure to freezing temperatures by 5.5% per week near the mean time of bud break. Early leafing is strongly correlated with midsummer shade, risk of freezing temperatures, and distribution on mesic sites across a "main spectrum" of 15 deciduous species. Differences in leaf phenology and resultant impacts on spring carbon gain may help determine tree shade tolerance and distribution in southern Appalachian forests.     

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re‐assign non identified species in each site to any of the other species found in the remaining sites. After each re‐assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re‐assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus‐resolution, family‐resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.     

Ecological Thresholds in the Savanna Landscape: Developing a Protocol for Monitoring the Change in Composition and Utilisation of Large Trees

Background

Acquiring greater understanding of the factors causing changes in vegetation structure - particularly with the potential to cause regime shifts - is important in adaptively managed conservation areas. Large trees (≥5 m in height) play an important ecosystem function, and are associated with a stable ecological state in the African savanna. There is concern that large tree densities are declining in a number of protected areas, including the Kruger National Park, South Africa. In this paper the results of a field study designed to monitor change in a savanna system are presented and discussed.

Methodology/Principal Findings

Developing the first phase of a monitoring protocol to measure the change in tree species composition, density and size distribution, whilst also identifying factors driving change. A central issue is the discrete spatial distribution of large trees in the landscape, making point sampling approaches relatively ineffective. Accordingly, fourteen 10 m wide transects were aligned perpendicular to large rivers (3.0–6.6 km in length) and eight transects were located at fixed-point photographic locations (1.0–1.6 km in length). Using accumulation curves, we established that the majority of tree species were sampled within 3 km. Furthermore, the key ecological drivers (e.g. fire, herbivory, drought and disease) which influence large tree use and impact were also recorded within 3 km.

Conclusions/Significance

The technique presented provides an effective method for monitoring changes in large tree abundance, size distribution and use by the main ecological drivers across the savanna landscape. However, the monitoring of rare tree species would require individual marking approaches due to their low densities and specific habitat requirements. Repeat sampling intervals would vary depending on the factor of concern and proposed management mitigation. Once a monitoring protocol has been identified and evaluated, the next stage is to integrate that protocol into a decision-making system, which highlights potential leading indicators of change. Frequent monitoring would be required to establish the rate and direction of change. This approach may be useful in generating monitoring protocols for other dynamic systems.     

Evaluating δ15N–body size relationships across taxonomic levels using hierarchical models

Ecologists routinely set out to estimate the trophic position of individuals, populations, and species composing food webs, and nitrogen stable isotopes (δ¹⁵N) are a widely used proxy for trophic position. Although δ¹⁵N values are often sampled at the level of individuals, estimates and confidence intervals are frequently sought for aggregations of individuals. If individual δ¹⁵N values are correlated as an artifact of sampling design (e.g., clustering of samples in space or time) or due to intrinsic groupings (e.g., life history stages, social groups, taxonomy), such estimates may be biased and exhibit overly optimistic confidence intervals. However, these issues can be accommodated using hierarchical modeling methods. Here, we demonstrate how hierarchical models offer an additional quantitative tool for investigating δ¹⁵N variability and we explicitly evaluate how δ¹⁵N varies with body size at successively higher levels of taxonomic aggregation in a diverse fish assemblage. The models take advantage of all available data, better account for uncertainty in parameters estimates, may improve inferences on coefficients corresponding to groups with small to moderate sample sizes, and partition variation across model levels, which provides convenient summaries of the ‘importance’ of each level in terms of unexplained heterogeneity in the data. These methods can easily be applied to diet-based studies of trophic position. Although hierarchical models are well-understood and established tools, their benefits have yet to be fully reaped by stable isotope and food web ecologists. We suggest that hierarchical models can provide a robust framework for conceptualizing and statistically modeling trophic position at multiple levels of aggregation.     

Fungal Diversity of Norway Spruce Litter: Effects of Site Conditions and Premature Leaf Fall Caused By Bark Beetle Outbreak

Fungi play an important role in leaf litter decomposition due to their ability to break down the lignocellulose matrix, which other organisms are unable to digest. However, little is known regarding the factors affecting components of fungal diversity. Here, we quantified richness of internal fungi in relation to litter nutrient and phenolic concentrations, sampling season (spring or fall), and premature leaf shedding due to low precipitation and infestation of bark beetles (mainly Ips typographus and Ips duplicatus). The study was conducted in 37-year-old Norway spruce [Picea abies (L.) Karst.] stands, with three plots each in mixed forest (MF) and coniferous forest (CF) site conditions in south-central Poland. Fifty-four species of sporulating fungi were identified in 2,330 freshly fallen needles sampled during 2003-2005, including 45 species in MF and 31 in CF. The significantly higher number of species in MF was likely related to moister conditions at that site. Among isolated fungi, 22% (12 species) were identified as endophytes of Norway spruce in prior studies. During spring of 2005, we found less than half the number of isolates and fungal species at each forest site as compared to fall for the two prior years. This pattern was observed in typical soil fungi (e.g., Penicillium daleae, Penicillium purpurogenum) and endophytes/epiphytes (e.g., Aureobasidium pullulans, Alternaria alternata, Cladosporium spp., and Lophodermium piceae). Premature shedding of needles was the most likely cause of this decline because it shortened the time period for fungi to infect green needles while on the tree. For all sites and sampling periods, richness of internal fungi was strongly and positively related to the age of freshly fallen litter (assessed using needle Ca concentration as a needle age tracer) and was also negatively related to litter phenolic concentration. Richness of internal fungi in freshly fallen litter may be adversely affected by low soil moisture status, natural inhibitors slowing fungal colonization (e.g., phenolics) and biotic (e.g., insect infestation) and abiotic (e.g., drought) factors that shorten leaf life span.     

Diversity Dynamics in Nymphalidae Butterflies: Effect of Phylogenetic Uncertainty on Diversification Rate Shift Estimates

The species rich butterfly family Nymphalidae has been used to study evolutionary interactions between plants and insects. Theories of insect-hostplant dynamics predict accelerated diversification due to key innovations. In evolutionary biology, analysis of maximum credibility trees in the software MEDUSA (modelling evolutionary diversity using stepwise AIC) is a popular method for estimation of shifts in diversification rates. We investigated whether phylogenetic uncertainty can produce different results by extending the method across a random sample of trees from the posterior distribution of a Bayesian run. Using the MultiMEDUSA approach, we found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees produced diversification rates ranging from high values to almost zero for the same clade, and both significant rate increase and decrease in some clades. Only four out of 18 significant shifts found on the maximum clade credibility tree were consistent across most of the sampled trees. Among these, we found accelerated diversification for Ithomiini butterflies. We used the binary speciation and extinction model (BiSSE) and found that a hostplant shift to Solanaceae is correlated with increased net diversification rates in Ithomiini, congruent with the diffuse cospeciation hypothesis. Our results show that taking phylogenetic uncertainty into account when estimating net diversification rate shifts is of great importance, as very different results can be obtained when using the maximum clade credibility tree and other trees from the posterior distribution.     

Multilocus phylogeny and systematics of Iberian endemic Squalius (Actinopterygii,Leuciscidae)

Inferring the evolutionary history of a group of species can be challenging given the many factors involved. In recent years, the increased availability of sequences of multiple genes per species has spurred the development of new methodologies to analyse multilocus data sets. Two approaches that analyse such data are concatenated supermatrix and coalescent-based species-tree analyses. In this study, we used both of these methods to infer the phylogenetic relationships of Iberian species of the genus Squalius from one mitochondrial and six nuclear genes. We found mitonuclear discordance in the phylogenetic relationships of the group. According to the mitochondrial gene analysis, all species were recovered as monophyletic except S. pyrenaicus; besides, in the concatenated supermatrix analysis of the nuclear markers, this species resolved as polyphyletic with three divergent evolutionary lineages. The coalescent-based nuclear species-tree analysis rendered a well-resolved phylogeny compared with the supermatrix analysis, which was unable to discern between S. carolitertii, S. castellanus and one of the evolutionary lineages of S. pyrenaicus. This result is likely due to the better integration of population uncertainty in the coalescent approach. Furthermore, Bayesian multilocus species delimitation analyses based on a BPP approach strongly supported the distinct nuclear lineages as different species. Nevertheless, the supermatrix analysis was able to obtain well-supported relationships in the divergent lineages with low numbers of individuals. Our study highlights the usefulness of different analytical methodologies to obtain a more complete picture of the evolutionary history of taxa, especially when discordant patterns among genes are found.     

Detecting and quantifying introgression in hybridized populations: simplifying assumptions yield overconfidence and uncertainty

A growing threat to the conservation of many native species worldwide is genetic introgression from non‐native species. Although improved molecular genetic techniques are increasing the availability of species‐diagnostic markers for many species, efficient field sampling design and reliable data interpretation require accurate estimates of uncertainty associated with the detection of non‐native alleles and the quantification of introgression in native populations. Using fish populations as examples, we developed a simulation model of an age‐structured population that tracks the introduction and inheritance of non‐native alleles across generations by simulating stochastic mating and survival of individual fish and the resulting transmission of diagnostic markers. To simulate detection and quantification of introgression, we sampled varying combinations of n fish and m diagnostic markers to detect and quantify introgression from thousands of virtual, independent fish populations for a wide range of hybridization scenarios. Using the results of simulated sampling, we quantified the extent to which common simplifying assumptions regarding population structure and inheritance mechanisms can lead to the following: (i) overconfidence in our ability to detect non‐native alleles and (ii) unrealistically narrow confidence intervals for estimates of the proportion of non‐native alleles present. Under many circumstances, commonly used simplifying assumptions underestimate the probability of failing to detect ongoing introgression and the uncertainty associated with estimates of introgression by orders of magnitude. Such overconfidence in our ability to detect and quantify introgression can affect critical conservation and management decisions regarding native species undergoing or at risk of introgression from non‐native species.