PhylteR: Efficient Identification of Outlier Sequences in Phylogenomic Datasets.

In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, can decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outliers from a collection of gene trees is still missing. Here, we present PhylteR, a method that allows rapid and accurate detection of outlier sequences in phylogenomic datasets, i.e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, these distance matrices extracted from individual gene phylogenies represent evolutionary distances between species according to each gene. On simulated datasets, we show that PhylteR identifies outliers with more sensitivity and precision than a comparable existing method. We also show that PhylteR is not sensitive to ILS-induced incongruences, which is a desirable feature. On a biological dataset of 14,463 genes for 53 species previously assembled for Carnivora phylogenomics, we show (i) that PhylteR identifies as outliers sequences that can be considered as such by other means, and (ii) that the removal of these sequences improves the concordance between the gene trees and the species tree. Thanks to the generation of numerous graphical outputs, PhylteR also allows for the rapid and easy visual characterization of the dataset at hand, thus aiding in the precise identification of errors. PhylteR is distributed as an R package on CRAN and as containerized versions (docker and singularity).

The global spectrum of plant form and function.

Earth is home to a remarkable diversity of plant forms and life histories, yet comparatively few essential trait combinations have proved evolutionarily viable in today's terrestrial biosphere. By analysing worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled, we found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs. Three-quarters of trait variation is captured in a two-dimensional global spectrum of plant form and function. One major dimension within this plane reflects the size of whole plants and their parts; the other represents the leaf economics spectrum, which balances leaf construction costs against growth potential. The global plant trait spectrum provides a backdrop for elucidating constraints on evolution, for functionally qualifying species and ecosystems, and for improving models that predict future vegetation based on continuous variation in plant form and function.

Are human natal sex ratio differences across the world adaptive? A test of Fisher's principle.

Fisher's principle states that natural selection favours an equal number of male and female births at the population level, unless there are sex differences in rearing costs or sex differences in mortality before the end of the period of parental investment. Sex differences in rearing costs should be more pronounced in low- than in high-resource settings. We, therefore, examined whether human development index and sex differences in child mortality contribute to the natural variation in human sex ratio at birth across the globe. As predicted by Fisher's principle, the proportion of male births increased with both increasing male-biased childhood mortality and level of development of each country. However, these relationships were absent after accounting for spatial autocorrelation in the residuals, which our inference is conditioned on. This work shows how the failure to account for residual spatial autocorrelation can lead to incorrect conclusions regarding support for predictions from sex allocation theory.

Flower phenology as a disruptor of the fruiting dynamics in temperate oak species.

Many perennial plants display masting, that is, fruiting with strong interannual variations, irregular and synchronized between trees within the population. Here, we tested the hypothesis that the early flower phenology in temperate oak species promotes stochasticity into their fruiting dynamics, which could play a major role in tree reproductive success. From a large field monitoring network, we compared the pollen phenology between temperate and Mediterranean oak species. Then, focusing on temperate oak species, we explored the influence of the weather around the time of budburst and flowering on seed production, and simulated with a mechanistic model the consequences that an evolutionary shifting of flower phenology would have on fruiting dynamics. Temperate oak species release pollen earlier in the season than do Mediterranean oak species. Such early flowering in temperate oak species results in pollen often being released during unfavorable weather conditions and frequently results in reproductive failure. If pollen release were delayed as a result of natural selection, fruiting dynamics would exhibit much reduced stochastic variation. We propose that early flower phenology might be adaptive by making mast-seeding years rare and unpredictable, which would greatly help in controlling the dynamics of seed consumers.

Can an herbivore affect where a top predator kills its prey by modifying woody vegetation structure?

In large mammal communities, little is known about modification of interspecific interactions through habitat structure changes. We assessed the effects of African elephants (Loxodonta africana) on features of woody habitat structure that can affect predator-prey interactions. We then explored how this can influence where African lions (Panthera leo) kill their prey. Indeed, lions are stalk-and-ambush predators and habitat structure and concealment opportunities are assumed to influence their hunting success. During 2 years, in Hwange National Park, Zimbabwe, kill sites (n = 167) of GPS-collared lions were characterized (visibility distance for large mammals, distance to a potential ambush site and presence of elephant impacts). We compared characteristics of lion kill sites with characteristics of random sites (1) at a large scale (i.e. in areas intensively used by lions, n = 418) and (2) at the microhabitat scale (i.e. in the direct surrounding available habitat, < 150 m, n = 167). Elephant-impacted sites had a slightly higher visibility and a longer distance to a potential ambush site than non-impacted sites, but these relationships were characterized by a high variability. At large scale, kill sites were characterized by higher levels of elephant impacts compared to random sites. At microhabitat scale, compared to the direct nearby available habitat, kill sites were characterized by a reduced distance to a potential ambush site. We suggest a conceptual framework whereby the relative importance of habitat features and prey abundance could change upon the scale considered.

Diversity indices for ecological networks: a unifying framework using Hill numbers.

Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α-, γ- and ß-diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food-web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time.

Phenotypic plasticity in the invasive pest Drosophila suzukii: activity rhythms and gene expression in response to temperature.

Phenotypic plasticity may contribute to the invasive success of an alien species in a new environment. A highly plastic species may survive and reproduce in more diverse environments, thereby supporting establishment and colonization. We focused on plasticity in the circadian rhythm of activity, which can favour species coexistence in invasion, for the invasive species Drosophila suzukii, which is expected to be a weaker direct competitor than other Drosophila species of the resident community. We compared the circadian rhythms of the locomotor activity in adults and the expression of clock genes in response to temperature in the invasive D. suzukii and the resident Drosophila melanogaster. We showed that D. suzukii is active in a narrower range of temperatures than D. melanogaster and that the activities of the two species overlap during the day, regardless of the temperature. Both species are diurnal and exhibit rhythmic activity at dawn and dusk, with a much lower activity at dawn for D. suzukii females. Our results show that the timeless and clock genes are good candidates to explain the plastic response that is observed in relation to temperature. Overall, our results suggest that thermal phenotypic plasticity in D. suzukii activity is not sufficient to explain the invasive success of D. suzukii and call for testing other hypotheses, such as the release of competitors and/or predators.

Integrating spatial and phylogenetic information in the fourth-corner analysis to test trait-environment relationships.

The fourth-corner analysis aims to quantify and test for relationships between species traits and site-specific environmental variables, mediated by site-specific species abundances. Since there is no common unit of observation, the significance of the relationships is tested using a double permutation procedure (site based and species based). This method implies that all species and sites are independent of each other. However, this fundamental hypothesis might be flawed because of phylogenetic relatedness between species and spatial autocorrelation in the environmental data. Here, using a simulation-based experiment, we demonstrate how the presence of spatial and phylogenetic autocorrelations can, in some circumstances, lead to inflated type I error rates, suggesting that significant associations can be misidentified. As an alternative, we propose a new randomization approach designed to avoid this issue, based on Moran's spectral randomization. In this approach, standard permutations are replaced by constrained randomizations so that the distribution of the statistic under the null hypothesis is built with additional constraints to preserve the phylogenetic and spatial structures of the observed data. The inclusion of this new randomization approach provides total control over type I error rates and should be used in real studies where spatial and phylogenetic autocorrelations often occur.

Optimizing the choice of a spatial weighting matrix in eigenvector-based methods.

Eigenvector-mapping methods such as Moran's eigenvector maps (MEM) are derived from a spatial weighting matrix (SWM) that describes the relations among a set of sampled sites. The specification of the SWM is a crucial step, but the SWM is generally chosen arbitrarily, regardless of the sampling design characteristics. Here, we compare the statistical performances of different types of SWMs (distance-based or graph-based) in contrasted realistic simulation scenarios. Then, we present an optimization method and evaluate its performances compared to the arbitrary choice of the most-widely used distance-based SWM. Results showed that the distance-based SWMs generally had lower power and accuracy than other specifications, and strongly underestimated spatial signals. The optimization method, using a correction procedure for multiple tests, had a correct type I error rate, and had higher power and accuracy than an arbitrary choice of the SWM. Nevertheless, the power decreased when too many SWMs were compared, resulting in a trade-off between the gain of accuracy and the loss of power. We advocate that future studies should optimize the choice of the SWM using a small set of appropriate candidates. R functions to implement the optimization are available in the adespatial package and are detailed in a tutorial.

Beyond neutrality: disentangling the effects of species sorting and spurious correlations in community analysis.

The methods of direct gradient analysis and variation partitioning are the most widely used frameworks to evaluate the contributions of species sorting to metacommunity structure. In many cases, however, species are also driven by spatial processes that are independent of environmental heterogeneity (e.g., neutral dynamics). As such, spatial autocorrelation can occur independently in both species (due to limited dispersal) and the environmental data, leading to spurious correlations between species distributions and the spatialized (i.e., spatially autocorrelated) environment. In these cases, the method of variation partitioning may present high Type I error rates (i.e., reject the null hypothesis more often than the pre-established critical level) and inflated estimates regarding the environmental component that is used to estimate the importance of species sorting. In this paper, we (1) demonstrate that metacommunities driven by neutral dynamics (via limited dispersal) alone or in combination with species sorting leads to inflated estimates and Type I error rates when testing for the importance of species sorting; and (2) propose a general and flexible new variation partitioning procedure to adjust for spurious contributions due to spatial autocorrelation from the environmental fraction. We used simulated metacommunity data driven by pure neutral, pure species sorting, and mixed (i.e., neutral + species sorting dynamics) processes to evaluate the performances of our new methodological framework. We also demonstrate the utility of the proposed framework with an empirical plant dataset in which we show that half of the variation initially due to the environment by the standard variation partitioning framework was due to spurious correlations.

Molecular phylogeny of the highly diversified catfish subfamily Loricariinae (Siluriformes, Loricariidae) reveals incongruences with morphological classification.

The Loricariinae belong to the Neotropical mailed catfish family Loricariidae, the most species-rich catfish family. Among loricariids, members of the Loricariinae are united by a long and flattened caudal peduncle and the absence of an adipose fin. Despite numerous studies of the Loricariidae, there is no comprehensive phylogeny of this morphologically highly diversified subfamily. To fill this gap, we present a molecular phylogeny of this group, including 350 representatives, based on the analysis of mitochondrial and nuclear genes (8426 positions). The resulting phylogeny indicates that Loricariinae are distributed into two sister tribes: Harttiini and Loricariini. The Harttiini tribe, as classically defined, constitutes a paraphyletic assemblage and is here restricted to the three genera Harttia, Cteniloricaria, and Harttiella. Two subtribes are distinguished within Loricariini: Farlowellina and Loricariina. Within Farlowellina, the nominal genus formed a paraphyletic group, as did Sturisoma and Sturisomatichthys. Within Loricariina, Loricaria, Crossoloricaria, and Apistoloricaria are also paraphyletic. To solve these issues, and given the lack of clear morphological diagnostic features, we propose here to synonymize several genera (Quiritixys with Harttia; East Andean members of Crossoloricaria, and Apistoloricaria with Rhadinoloricaria; Ixinandria, Hemiloricaria, Fonchiiichthys, and Leliella with Rineloricaria), to restrict others (Crossoloricaria, and Sturisomatichthys to the West Andean members, and Sturisoma to the East Andean species), and to revalidate the genus Proloricaria.

Interspecific interference competition at the resource patch scale: do large herbivores spatially avoid elephants while accessing water?

Animals may anticipate and try to avoid, at some costs, physical encounters with other competitors. This may ultimately impact their foraging distribution and intake rates. Such cryptic interference competition is difficult to measure in the field, and extremely little is known at the interspecific level. We tested the hypothesis that smaller species avoid larger ones because of potential costs of interference competition and hence expected them to segregate from larger competitors at the scale of a resource patch. We assessed fine-scale spatial segregation patterns between three African herbivore species (zebra Equus quagga, kudu Tragelaphus strepsiceros and giraffe Giraffa camelopardalis) and a megaherbivore, the African elephant Loxodonta africana, at the scale of water resource patches in the semi-arid ecosystem of Hwange National Park, Zimbabwe. Nine waterholes were monitored every two weeks during the dry season of a drought year, and observational scans of the spatial distribution of all herbivores were performed every 15 min. We developed a methodological approach to analyse such fine-scale spatial data. Elephants increasingly used waterholes as the dry season progressed, as did the probability of co-occurrence and agonistic interaction with elephants for the three study species. All three species segregated from elephants at the beginning of the dry season, suggesting a spatial avoidance of elephants and the existence of costs of being close to them. However, contrarily to our expectations, herbivores did not segregate from elephants the rest of the dry season but tended to increasingly aggregate with elephants as the dry season progressed. We discuss these surprising results and the existence of a trade-off between avoidance of interspecific interference competition and other potential factors such as access to quality water, which may have relative associated costs that change with the time of the year.

Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation.

Assessing trait responses to environmental gradients requires the simultaneous analysis of the information contained in three tables: L (species distribution across samples), R (environmental characteristics of samples), and Q (species traits). Among the available methods, the so-called fourth-corner and RLQ methods are two appealing alternatives that provide a direct way to test and estimate trait-nvironment relationships. Both methods are based on the analysis of the fourth-corner matrix, which crosses traits and environmental variables weighted by species abundances. However, they differ greatly in their outputs: RLQ is a multivariate technique that provides ordination scores to summarize the joint structure among the three tables, whereas the fourth-corner method mainly tests for individual trait-environment relationships (i.e., one trait and one environmental variable at a time). Here, we illustrate how the complementarity between these two methods can be exploited to promote new ecological knowledge and to improve the study of trait-environment relationships. After a short description of each method, we apply them to real ecological data to present their different outputs and provide hints about the gain resulting from their combined use.

Statistical ecology comes of age.

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Using the multivariate Hawkes process to study interactions between multiple species from camera trap data.

Interspecific interactions can influence species' activity and movement patterns. In particular, species may avoid or attract each other through reactive responses in space and/or time. However, data and methods to study such reactive interactions have remained scarce and were generally limited to two interacting species. At this time, the deployment of camera traps opens new opportunities but adapted statistical techniques are still required to analyze interaction patterns with such data. We present the multivariate Hawkes process (MHP) and show how it can be used to analyze interactions between several species using camera trap data. Hawkes processes use flexible pairwise interaction functions, allowing us to consider asymmetries and variations over time when depicting reactive temporal interactions. After describing the theoretical foundations of the MHP, we outline how its framework can be used to study interspecific interactions with camera trap data. We design a simulation study to evaluate the performance of the MHP and of another existing method to infer interactions from camera trap-like data. We also use the MHP to infer reactive interactions from real camera trap data for five species from South African savannas (impala Aepyceros melampus, greater kudu Tragelaphus strepsiceros, lion Panthera leo, blue wildebeest Connochaetes taurinus and Burchell's zebra Equus quagga burchelli). The simulation study shows that the MHP can be used as a tool to benchmark other methods of interspecific interaction inference and that this model can reliably infer interactions when enough data are considered. The analysis of real data highlights evidence of predator avoidance by prey and herbivore-herbivore attraction. Lastly, we present the advantages and limits of the MHP and discuss how it can be improved to infer attraction/avoidance patterns more reliably. As camera traps are increasingly used, the multivariate Hawkes process provides a promising framework to decipher the complexity of interactions structuring ecological communities.

Reciprocal modulation of internal and external factors determines individual movements.

Movement is fundamental to individual and population dynamics, as it allows individuals to meet their basic requirements. Although movement patterns reflect interactions between internal and external factors, only few studies have examined the effects of these factors on movement simultaneously, and they generally focused on particular biological contexts (e.g. dispersal, foraging). However, the relative importance of these factors in driving individual routine movements might reflect a species' potential flexibility to cope with landscape changes and therefore buffer their potential impact on fitness. We used data from GPS collars on Scandinavian brown bears to investigate the relative role of these factors, as well as an additional factor (period of the year) on routine movements at two spatial scales (hourly and daily relocations). As expected, internal factors played a major role in driving movement, compared to external factors at both scales, but its relative importance was greater at a finer scale. In particular, the interaction between reproductive status and period of the year was one of the most influential variables, females being constrained by the movement capacity of their cubs in the first periods of the year. The effect of human disturbance on movement was also greater for females with cubs than for lone females. This study showed how reciprocal modulation of internal and external factors is shaping space use of brown bears. We stress that these factors should be studied simultaneously to avoid the risk of obtaining context-dependent inferences. Moreover, the study of their relative contribution is also highly relevant in the context of multiple-use landscapes, as human activities generally affect the landscape more than they affect the internal states of an individual. Species or individuals with important internal constraints should be less responsive to changes in their environment as they have less freedom from internal constraints and should thus be more sensitive to human alteration of the landscape, as shown for females with cubs in this study.

Improved testing of species traits--environment relationships in the fourth-corner problem.

The fourth-corner problem entails estimation and statistical testing of the relationship between species traits and environmental variables from the analysis of three data tables. In a 2008 paper, S. Dray and P. Legendre proposed and evaluated five permutation methods for statistical significance testing, including a new two-step testing procedure. However, none of these attained the correct type I error in all cases of interest. We solve this problem by showing that a small modification of their two-step procedure controls the type I error in all cases. The modification consists of adjusting the significance level from mean square root of alpha to alpha or, equivalently, of reporting the maximum of the individual P. values as the final one. The test is also applicable to the three-table ordination method RLQ.

Predator-prey spatial game as a tool to understand the effects of protected areas on harvester-wildlife interactions.

No-take reserves are sometimes implemented for sustainable population harvesting because they offer opportunities for animals to spatially avoid harvesters, whereas harvesters can benefit in return from the reserve spillover. Here, we used the framework of predator-prey spatial games to understand how protected areas shape spatial interactions between harvesters and target species and determine animal mortality. In these spatial games, the "predator" searches for "prey" and matches their habitat use, unless it meets spatial constraints offering the opportunity for prey to avoid the mortality source. However, such prey refuges could attract predators in the surroundings, which questions the potential benefits for prey. We located, in the Geneva Basin (France), hunting dogs and wild boar Sus scrofa L. during hunting seasons with global positioning systems and very-high-frequency collars. We quantified how the proximity of the reserve shaped the matching between both habitat uses using multivariate analyses and linked these patterns to animals' mortality with a Cox regression analysis. Results showed that habitat uses by both protagonists disassociated only when hunters were spatially constrained by the reserve. In response, hunters increased hunting efforts near the reserve boundary, which induced a higher risk exposure for animals settled over the reserve. The mortality of adult wild boar decreased near the reserve as the mismatch between both habitat uses increased. However the opposite pattern was determined for younger individuals that suffered from the high level of hunting close to the reserve. The predator-prey analogy was an accurate prediction of how the protected area modified spatial relationships between harvesters and target species. Prey-searching strategies adopted by hunters around reserves strongly impacted animal mortality and the efficiency of the protected area for this harvested species. Increasing reserve sizes and/or implementing buffer areas with harvesting limitations can dampen this edge effect and helps harvesters to benefit durably from source populations of reserves. Predator-prey spatial games therefore provide a powerful theoretical background for understanding wildlife-harvester spatial interactions and developing substantial application for sustainable harvesting.

Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity.

The processes that give rise to species richness gradients are not well understood, but may be linked to resource-based limits on the number of species a region can support. Ecological limits placed on regional species richness should also affect population demography, suggesting that these processes could also generate genetic diversity gradients. If true, we might better understand how broad-scale biodiversity patterns are formed by identifying the common causes of genetic diversity and species richness. We develop a hypothetical framework based on the consequences of regional variation in ecological limits set by resource availability and heterogeneity to simultaneously explain spatial patterns of species richness and neutral genetic diversity. Repurposing raw genotypic data spanning 38 mammal species sampled across 801 sites in North America, we show that estimates of genome-wide genetic diversity and species richness share spatial structure. Notably, species richness hotspots tend to harbor lower levels of within-species genetic variation. A structural equation model encompassing eco-evolutionary processes related to resource availability, habitat heterogeneity, and contemporary human disturbance supports the spatial patterns we detect. These results suggest broad-scale patterns of species richness and genetic diversity could both partly be caused by intraspecific demographic and evolutionary processes acting simultaneously across species.