The importance of tree size and fecundity for wind dispersal of big-leaf mahogany

Seed dispersal by wind is a critical yet poorly understood process in tropical forest trees. How tree size and fecundity affect this process at the population level remains largely unknown because of insufficient replication across adults. We measured seed dispersal by the endangered neotropical timber species big-leaf mahogany (Swietenia macrophylla King, Meliaceae) in the Brazilian Amazon at 25 relatively isolated trees using multiple 1-m wide belt transects extended 100 m downwind. Tree diameter and fecundity correlated positively with increased seed shadow extent; but in combination large, high fecundity trees contributed disproportionately to longer-distance dispersal events (>60 m). Among three empirical models fitted to seed density vs. distance in one dimension, the Student-t (2Dt) generally fit best (compared to the negative exponential and inverse power). When seedfall downwind was modelled in two dimensions using a normalised sample, it peaked furthest downwind (c. 25 m) for large, high-fecundity trees; with the inverse Gaussian and Weibull functions providing comparable fits that were slightly better than the lognormal. Although most seeds fell within 30 m of parent trees, relatively few juveniles were found within this distance, resulting in juvenile-to-seed ratios peaking at c. 35-45 m. Using the 2Dt model fits to predict seed densities downwind, coupled with known fecundity data for 2000-2009, we evaluated potential Swietenia regeneration near adults (≤30 m dispersal) and beyond 30 m. Mean seed arrival into canopy gaps >30 m downwind was more than 3× greater for large, high fecundity trees than small, high-fecundity trees. Tree seed production did not necessarily scale up proportionately with diameter, and was not consistent across years, and this resulting intraspecific variation can have important consequences for local patterns of dispersal in forests. Our results have important implications for management and conservation of big-leaf mahogany populations, and may apply to other threatened wind-dispersed Meliaceae trees.     

Age versus size determination of radial variation in wood specific gravity: lessons from eccentrics

Radial increases in wood specific gravity have been shown to characterize early successional trees from tropical forests. Here, we develop and apply a novel method to test whether radial increases are determined by tree age or tree size. The method compares the slopes of specific gravity changes across a short radius and a long radius of trees with eccentric trunks. If radial changes are determined by size, then the slope of the change should be the same on both radii. If radial changes are determined by age, then the slope should be greater on the short radius. For 30 trees from 12 species with eccentricity of at least 4%, the ratio of the slopes of the linear regressions of specific gravity on radial distance (short radius slope/long radius slope) was regressed on the ratio of radii lengths (long radius/short radius). The regression was highly significant, and the faster increase in specific gravity on the short radius was sufficient to compensate for the difference in radius lengths, so the specific gravity of wood along the short radius was equal to the specific gravity on the long radius at any given proportional distance on the radius. Therefore, trees that are producing xylem faster on one radius than another produce wood of comparable specific gravity on both radii at the same time, so radial increases in specific gravity are dependent on tree age, not tree size.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

The devil in the details: interactions between the branch-length prior and likelihood model affect node support and branch lengths in the phylogeny of the Psoraceae

In popular use of Bayesian phylogenetics, a default branch-length prior is almost universally applied without knowing how a different prior would have affected the outcome. We performed Bayesian and maximum likelihood (ML) inference of phylogeny based on empirical nucleotide sequence data from a family of lichenized ascomycetes, the Psoraceae, the morphological delimitation of which has been controversial. We specifically assessed the influence of the combination of Bayesian branch-length prior and likelihood model on the properties of the Markov chain Monte Carlo tree sample, including node support, branch lengths, and taxon stability. Data included two regions of the mitochondrial ribosomal RNA gene, the internal transcribed spacer region of the nuclear ribosomal RNA gene, and the protein-coding largest subunit of RNA polymerase II. Data partitioning was performed using Bayes' factors, whereas the best-fitting model of each partition was selected using the Bayesian information criterion (BIC). Given the data and model, short Bayesian branch-length priors generate higher numbers of strongly supported nodes as well as short and topologically similar trees sampled from parts of tree space that are largely unexplored by the ML bootstrap. Long branch-length priors generate fewer strongly supported nodes and longer and more dissimilar trees that are sampled mostly from inside the range of tree space sampled by the ML bootstrap. Priors near the ML distribution of branch lengths generate the best marginal likelihood and the highest frequency of "rogue" (unstable) taxa. The branch-length prior was shown to interact with the likelihood model. Trees inferred under complex partitioned models are more affected by the stretching effect of the branch-length prior. Fewer nodes are strongly supported under a complex model given the same branch-length prior. Irrespective of model, internal branches make up a larger proportion of total tree length under the shortest branch-length priors compared with longer priors. Relative effects on branch lengths caused by the branch-length prior can be problematic to downstream phylogenetic comparative methods making use of the branch lengths. Furthermore, given the same branch-length prior, trees are on average more dissimilar under a simple unpartitioned model compared with a more complex partitioned models. The distribution of ML branch lengths was shown to better fit a gamma or Pareto distribution than an exponential one. Model adequacy tests indicate that the best-fitting model selected by the BIC is insufficient for describing data patterns in 5 of 8 partitions. More general substitution models are required to explain the data in three of these partitions, one of which also requires nonstationarity. The two mitochondrial ribosomal RNA gene partitions need heterotachous models. We found no significant correlations between, on the one hand, the amount of ambiguous data or the smallest branch-length distance to another taxon and, on the other hand, the topological stability of individual taxa. Integrating over several exponentially distributed means under the best-fitting model, node support for the family Psoraceae, including Psora, Protoblastenia, and the Micarea sylvicola group, is approximately 0.96. Support for the genus Psora is distinctly lower, but we found no evidence to contradict the current classification.     

A metric for phylogenetic trees based on matching

Comparing two or more phylogenetic trees is a fundamental task in computational biology. The simplest outcome of such a comparison is a pairwise measure of similarity, dissimilarity, or distance. A large number of such measures have been proposed, but so far all suffer from problems varying from computational cost to lack of robustness; many can be shown to behave unexpectedly under certain plausible inputs. For instance, the widely used Robinson-Foulds distance is poorly distributed and thus affords little discrimination, while also lacking robustness in the face of very small changes--reattaching a single leaf elsewhere in a tree of any size can instantly maximize the distance. In this paper, we introduce a new pairwise distance measure, based on matching, for phylogenetic trees. We prove that our measure induces a metric on the space of trees, show how to compute it in low polynomial time, verify through statistical testing that it is robust, and finally note that it does not exhibit unexpected behavior under the same inputs that cause problems with other measures. We also illustrate its usefulness in clustering trees, demonstrating significant improvements in the quality of hierarchical clustering as compared to the same collections of trees clustered using the Robinson-Foulds distance.     

Ficus seed shadows in a Bornean rain forest

Due to their copious seed production and numerous dispersers, rain forest fig trees have been assumed to produce extensive and dense seed shadows. To test this idea, patterns of seed dispersal of two species of large hemiepiphytic fig tree were measured in a Bornean rain forest. The sample included four Ficus stupenda and three F. subtecta trees with crop sizes ranging from 2,000 to 40,000 figs (400,000 to 13,000,000 seeds). Seed rain out to a distance of 60 m from each study tree was quantified using arrays of seed traps deployed in the understory. These trees showed a strongly leptokurtic pattern of dispersal, as expected, but all individuals had measurable seed rain at 60 m, ranging from 0.2 to 5.0 seeds/m². A regression of In-transformed seed rain density against distance gave a significant fit to all seven trees' dispersal patterns, indicating that the data could be fitted to the negative exponential distribution most commonly fitted to seed shadows. However, for six of seven trees, an improved fit was obtained for regressions in which distance was also In-transformed. This transformation corresponds to an inverse power distribution, indicating that for vertebrate-dispersed Ficus seeds, the tail of the seed rain distribution does not drop off as rapidly as in the exponential distribution typically associated with wind dispersed seed shadows. Over 50% of the seed crop was estimated to fall below each fig tree's crown. Up to 22% of the seed crop was dispersed beyond the crown edge, but within 60 m of the tree. Estimates of the maximum numbers of seeds which could have been transported beyond 60 m were 45% for the two largest crops of figs, but were under 24% for the trees with smaller crops. Seed traps positioned where they had an upper canopy layer above them were associated with higher probabilities of being hit by seeds, suggesting that vertebrate dispersal agents are likely to perch or travel through forest layers at the same level as the fig crown and could concentrate seeds in such areas to some degree. The probability of a safe site at 60 m from the fig tree being hit by seeds is calculated to be on the order of 0.01 per fruiting episode. Fig trees do not appear to saturate safe sites with seeds despite their large seed crops. If we in addition consider the rarity of quality establishment sites and post-dispersal factors reducing successful seedling establishment, hemiepiphytic fig trees appear to face severe obstacles to seedling recruitment.     

Spatial frequency response function of lateral geniculate neurons

In 1969 Campbell et al. reported five spatial frequency response curves. Here a good curve fit is found to three of them based on a model of lateral inhibition from four off-centered geniculate cells whose receptive fields were at a common distance from that of an on-centered geniculate cell. The distance was 0.5° when the experimental curve was fitted by a difference of two Gaussians and 1.4° when fitted by a difference of two Bessel Functions. Of the remaining two response curves one was best fitted assuming no lateral inhibition and the other remains to be fitted.     

A dimensionless fit measure for phylogenetic distance trees

We present a dimensionless fit index for phylogenetic trees that have been constructed from distance matrices. It is designed to measure the quality of the fit of the data to a tree in absolute terms, independent of linear transformations on the distance matrix. The index can be used as an absolute measure to evaluate how well a set of data fits to a tree, or as a relative measure to compare different methods that are expected to produce the same tree. The usefulness of the index is demonstrated in three examples.     

Computationally difficult parsimony problems in phylogenetic systematics

A basic problem in phylogenetic systematics is to construct an evolutionary hypothesis, or phylogenetic tree, from available data for a set of operational taxonomic units (OTUs). Associated with the edges of such trees are weights that usually are interpreted as lengths. Methods proposed for constructing phylogenetic trees attempt to select from among the myriad alternatives a tree that optimizes in some sense the fit of tree topology and edge lengths with the original data. One optimization criterion seeks a most parsimonious tree in which the sum of edge lengths is a minimum. Researchers have failed to develop efficient algorithms to compute optimal solutions for important variations of the parsimonious tree construction problem. Recently Graham & Foulds (1982) proved that a special case of the problem is NP-complete, thus making it unlikely that the computational problem for this case can be solved efficiently. I describe three other parsimonious tree construction problems and prove that they, too, are NP-complete.     

USING PHYLOGENETIC TREES TO STUDY SPECIATION AND EXTINCTION

One tool in the study of the forces that determine species diversity is the null, or simple, model. The fit of predictions to observations, good or bad, leads to a useful paradigm or to knowledge of forces not accounted for, respectively. It is shown how simple models of speciation and extinction lead directly to predictions of the structure of phylogenetic trees. These predictions include both essential attributes of phylogenetic trees: lengths, in the form of internode distances; and topology, in the form of internode links. These models also lead directly to statistical tests which can be used to compare predictions with phylogenetic trees that are estimated from data. Two different models and eight data sets are considered. A model without species extinction consistently yielded predictions closer to observations than did a model that included extinction. It is proposed that it may be useful to think of the diversification of recently formed monophyletic groups as a random speciation process without extinction.     

A fast algorithm for computing geodesic distances in tree space

Comparing and computing distances between phylogenetic trees are important biological problems, especially for models where edge lengths play an important role. The geodesic distance measure between two phylogenetic trees with edge lengths is the length of the shortest path between them in the continuous tree space introduced by Billera, Holmes, and Vogtmann. This tree space provides a powerful tool for studying and comparing phylogenetic trees, both in exhibiting a natural distance measure and in providing a euclidean-like structure for solving optimization problems on trees. An important open problem is to find a polynomial time algorithm for finding geodesics in tree space. This paper gives such an algorithm, which starts with a simple initial path and moves through a series of successively shorter paths until the geodesic is attained.     

Minority rule supertrees? MRP,Compatibility, and Minimum Flip may display the least frequent groups

New examples are presented, showing that supertree methods such as matrix representation with parsimony, minimum flip trees, and compatibility analysis of the matrix representing the input trees, produce supertrees that cannot be interpreted as displaying the groups present in the majority of the input trees. These methods may produce a supertree displaying some groups present in the minority of the trees, and contradicted by the majority. Of the three methods, compatibility analysis is the least used, but it seems to be the one that differs the least from majority rule consensus. The three methods are similar in that they choose the supertree(s) that best fit the set of input trees (quantified as some measure of the fit to the matrix representation of the input trees); in the case of complete trees, it is argued that, for a supertree method to be equivalent to majority rule or frequency difference consensus, two necessary (but not sufficient) conditions must be met. First, the measure of fit between a supertree and an input tree must be symmetrical. Second, the fit for a character representing a group must be measured as absolute: either it fits or it does not fit. In the restricted case of complete and equally resolved input trees, compatibility analysis (unlike MRP and minimum flipping) fulfils these two conditions: it is symmetrical (i.e., as long as the trees have the same taxon sets and are equally resolved, the number of characters in the matrix representation of tree A that require homoplasy in tree B is always the same as the number of characters in the matrix representation of tree B that require homoplasy in tree A) and it measures fit as all‐or‐none. In the case of just two complete and equally resolved input trees, the two conditions (symmetry and absolute fit) are necessary and sufficient, which explains why the compatibility analysis of such trees behaves as majority consensus. With more than two such trees, these conditions are still necessary but no longer sufficient for the equivalence; in such cases, the compatibility supertree may differ significantly from the majority rule consensus, even when these conditions apply (as shown by example). MRP and minimum flipping are asymmetric and measure various degrees of fit for each character, which explains why they often behave very differently from majority rule procedures, and why they are very likely to have groups contradicted by each of the input trees, or groups supported by a minority of the input trees. © The Willi Hennig Society 2005.     

The chloroplast generps 4 as a tool for the study ofPoaceae phylogeny

Phylogenetic analyses of 28Poaceae species based on the chloroplastrps 4 gene are presented using parsimony and distance methods. Two monocots from other families were used as outgroups. The chloroplast generps 4 was amplified, cloned, and sequenced for each species. The inferred phylogenetic trees were compared to recent classifications and are shown to fit their general features. There is a dichotomy in our tree between the pooid group and the other grasses. This is in contradiction with other molecular phylogenies, where the bamboos appear first within the family. This result led us to discuss some hypotheses about the relationships of the bambusoids with the other groups of grasses, and also about the relative position of rice and bamboo, which are found close to each other in our trees.     

The K tree score: quantification of differences in the relative branch length and topology of phylogenetic trees

SUMMARY: We introduce a new phylogenetic comparison method that measures overall differences in the relative branch length and topology of two phylogenetic trees. To do this, the algorithm first scales one of the trees to have a global divergence as similar as possible to the other tree. Then, the branch length distance, which takes differences in topology and branch lengths into account, is applied to the two trees. We thus obtain the minimum branch length distance or K tree score. Two trees with very different relative branch lengths get a high K score whereas two trees that follow a similar among-lineage rate variation get a low score, regardless of the overall rates in both trees. There are several applications of the K tree score, two of which are explained here in more detail. First, this score allows the evaluation of the performance of phylogenetic algorithms, not only with respect to their topological accuracy, but also with respect to the reproduction of a given branch length variation. In a second example, we show how the K score allows the selection of orthologous genes by choosing those that better follow the overall shape of a given reference tree. AVAILABILITY: http://molevol.ibmb.csic.es/Ktreedist.html     

Effectiveness of measures requiring and not requiring prior sequence alignment for estimating the dissimilarity of natural sequences

Summary Various measures of sequence dissimilarity have been evaluated by how well the additive least squares estimation of edges (branch lengths) of an unrooted evolutionary tree fit the observed pairwise dissimilarity measures and by how consistent the trees are for different data sets derived from the same set of sequences. This evaluation provided sensitive discrimination among dissimilarity measures and among possible trees. Dissimilarity measures not requiring prior sequence alignment did about as well as did the traditional mismatch counts requiring prior sequence alignment. Application of Jukes-Cantor correction to singlet mismatch counts worsened the results. Measures not requiring alignment had the advantage of being applicable to sequences too different to be critically alignable. Two different measures of pairwise dissimilarity not requiring alignment have been used: (1) multiplet distribution distance (MDD), the square of the Euclidean distance between vectors of the fractions of base singlets (or doublets, or triplets, or…) in the respective sequences, and (2) complements of long words (CLW), the count of bases not occurring in significantly long common words. MDD was applicable to sequences more different than was CLW (noncoding), but the latter often gave better results where both measures were available (coding). MDD results were improved by using longer multiplets and, if the sequences were coding, by using the larger amino acid and codon alphabets rather than the nucleotide alphabet. The additive least squares method could be used to provide a reasonable consensus of different trees for the same set of species (or related genes).     

A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

Background  

Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE) of the species tree (topology, branch lengths, and population sizes) from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE) of species trees, with branch lengths of the species tree in coalescent units.     

Phylogenetic inference under the pure drift model

When pairwise genetic distances are used for phylogenetic reconstruction, it is usually assumed that the genetic distance between two taxa contains information about the time after the two taxa diverged. As a result, upon an appropriate transformation if necessary, the distance usually can be fitted to a linear model such that it is expressed as the sum of lengths of all branches that connect the two taxa in a given phylogeny. This kind of distance is referred to as "additive distance." For a phylogenetic tree exclusively driven by random genetic drift, genetic distances related to coancestry coefficients (theta XY) between any two taxa are more suitable. However, these distances are fundamentally different from the additive distance in that coancestry does not contain any information about the time after two taxa split from a common ancestral population; instead, it reflects the time before the two taxa diverged. In other words, the magnitude of theta XY provides information about how long the two taxa share the same evolutionary pathways. The fundamental difference between the two kinds of distances has led to a different algorithm of evaluating phylogenetic trees when theta XY and related distance measures are used. Here we present the new algorithm using the ordinary- least-squares approach but fitting to a different linear model. This treatment allows genetic variation within a taxon to be included in the model. Monte Carlo simulation for a rooted phylogeny of four taxa has verified the efficacy and consistency of the new method. Application of the method to human population was demonstrated.      

A stepwise algorithm for finding minimum evolution trees

A stepwise algorithm for reconstructing minimum evolution (ME) trees from evolutionary distance data is proposed. In each step, a taxon that potentially has a neighbor (another taxon connected to it with a single interior node) is first chosen and then its true neighbor searched iteratively. For m taxa, at most (m-1)!/2 trees are examined and the tree with the minimum sum of branch lengths (S) is chosen as the final tree. This algorithm provides simple strategies for restricting the tree space searched and allows us to implement efficient ways of dynamically computing the ordinary least squares estimates of S for the topologies examined. Using computer simulation, we found that the efficiency of the ME method in recovering the correct tree is similar to that of the neighbor-joining method (Saitou and Nei 1987). A more exhaustive search is unlikely to improve the efficiency of the ME method in finding the correct tree because the correct tree is almost always included in the tree space searched with this stepwise algorithm. The new algorithm finds trees for which S values may not be significantly different from that of the ME tree if the correct tree contains very small interior branches or if the pairwise distance estimates have large sampling errors. These topologies form a set of plausible alternatives to the ME tree and can be compared with each other using statistical tests based on the minimum evolution principle. The new algorithm makes it possible to use the ME method for large data sets.      

An error-correcting map for quartets can improve the signals for phylogenetic trees

From the DNA sequences for N taxa, the (generally unknown) phylogenetic tree T that gave rise to them is to be reconstructed. Various methods give rise, for each quartet J consisting of exactly four taxa, to a predicted tree L(J) based only on the sequences in J, and these are then used to reconstruct T. The author defines an "error-correcting map" (Ec), which replaces each L(J) with a new tree, Ec(L)(J), which has been corrected using other trees, L(K), in the list L. The "quartet distance" between two trees is defined as the number of quartets J on which the two trees differ, and two distinct trees are shown to always have quartet distance of at least N - 3. If L has quartet distance at most (N - 4)/2 from T, then Ec(L) will coincide with the correct list for T; and this result cannot be improved. In general, Ec can correct many more errors in L. Iteration of the map Ec may produce still more accurate lists. Simulations are reported which often show improvement even when the quartet distance considerably exceeds (N - 4)/2. Moreover, the Buneman tree for Ec(L) is shown to refine the Buneman tree for L, so that strongly supported edges for L remain strongly supported for Ec(L). Simulations show that if methods such as the C-tree or hypercleaning are applied to Ec(L), the resulting trees often have more resolution than when the methods are applied only to L.     

An efficient algorithm for approximating geodesic distances in tree space

The increasing use of phylogeny in biological studies is limited by the need to make available more efficient tools for computing distances between trees. The geodesic tree distance-introduced by Billera, Holmes, and Vogtmann-combines both the tree topology and edge lengths into a single metric. Despite the conceptual simplicity of the geodesic tree distance, algorithms to compute it don't scale well to large, real-world phylogenetic trees composed of hundred or even thousand leaves. In this paper, we propose the geodesic distance as an effective tool for exploring the likelihood profile in the space of phylogenetic trees, and we give a cubic time algorithm, GeoHeuristic, in order to compute an approximation of the distance. We compare it with the GTP algorithm, which calculates the exact distance, and the cone path length, which is another approximation, showing that GeoHeuristic achieves a quite good trade-off between accuracy (relative error always lower than 0.0001) and efficiency. We also prove the equivalence among GeoHeuristic, cone path, and Robinson-Foulds distances when assuming branch lengths equal to unity and we show empirically that, under this restriction, these distances are almost always equal to the actual geodesic.