Random walk in genome space: A key ingredient of intermittent dynamics of community assembly on evolutionary time scales

Community assembly is studied using individual-based multispecies models. The models have stochastic population dynamics with mutation, migration, and extinction of species. Mutants appear as a result of mutation of the resident species, while migrants have no correlation with the resident species. It is found that the dynamics of community assembly with mutations are quite different from the case with migrations. In contrast to mutation models, which show intermittent dynamics of quasi-steady states interrupted by sudden reorganizations of the community, migration models show smooth and gradual renewal of the community. As a consequence, instead of the 1/f diversity fluctuations found for the mutation models, 1/f², random-walk like fluctuations are observed for the migration models. In addition, a characteristic species-lifetime distribution is found: a power law that is cut off by a “skewed” distribution in the long-lifetime regime. The latter has a longer tail than a simple exponential function, which indicates an age-dependent species-mortality function. Since this characteristic profile has been observed, both in fossil data and in several other mathematical models, we conclude that it is a universal feature of macroevolution.     

Directional evolution of stockiness coevolves with ecology and locomotion in lizards

Although studied in many taxa, directional macroevolution remains difficult to detect and quantify. We present an approach for detecting directional evolution in subclades of species when relatively few species are sampled, and apply it to studying the evolution of stockiness in Phrynosomatine lizards. Our approach is more sensitive to detecting the tempo of directional evolution than other available approaches. We use ancestral reconstruction and phylogenetic mapping of morphology to characterize the direction and magnitude of trait evolution. We demonstrate a directional trend toward stockiness in horned lizards, but not their sister groups, finding that stockier species tend to have relatively short and wide bodies, and relatively short heads, tails, and limbs. Ornstein–Uhlenbeck models show that the directional trend in horned lizards is due to a shift in selective regime and stabilizing selection as opposed to directional selection. Bayesian evolutionary correlation analyses indicate that stockier species run more slowly and eat a larger proportion of ants. Furthermore, species with larger horns tend to be slower and more ant-specialized. Directional evolution toward a stocky body shape has evolved in conjunction with changes in a suite of traits, representing a complex example of directional macroevolution.     

A meta‐analysis of species–abundance distributions

The species–abundance distribution (SAD) describes the abundances of all species within a community. Many different models have been proposed to describe observed SADs. Best known are the logseries, the lognormal, and a variety of niche division models. They are most often visualized using either species richness – log abundance class (Preston) plots or abundance – species rank order (Whittaker) plots. Because many of the models predict very similar shapes, model distinction and testing become problematic. However, the variety of models can be classified into three basic types: one that predicts a double S‐shape in Whittaker plots and a unimodal distribution in Preston plots (the lognormal type), a second that lacks the mode in Preston plots (the logseries type), and a third that predicts power functions in both plotting types (the power law type). Despite the interest of ecologists in SADs no formal meta‐analysis of models and plotting types has been undertaken so far. Here we use a compilation of 558 species–abundance distributions from 306 published papers to infer the frequency of the three SAD shapes in dependence of environmental variables and type of plotting. Our results highlight the importance of distinguishing between fully censused and incompletely sampled communities in the study of SADs. We show that completely censused terrestrial or freshwater animal communities tend to follow lognormal type SADs more often than logseries or power law types irrespective of species richness, spatial scale, and geographic position. However, marine communities tend to follow the logseries type, while plant communities tend to follow the power law. In incomplete sets the power law fitted best in Whittaker plots, and the logseries in Preston plots. Finally our study favors the use of Whittaker over Preston plots.     

Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change

Species range and climate change risk are often assessed using species distribution models (SDM) that model species niche from presence points and environmental variables and project it in space and time. These presence points frequently originate from occurrence data downloaded from public biodiversity databases, but such data are known to suffer from high biases. There is thus a need to find alternative sources of information to train these models. In this regard, expert-based range maps such as those provided by the International Union for Conservation of Nature (IUCN) have the potential to be used as a source of species presence in a SDM workflow. Here, I compared the predictions of SDM built using true occurrences provided by GBIF or iNaturalist, or using pseudo-occurrences sampled from IUCN expert-based range maps, in current and future climate. I found that the agreement between both types of SDM did not depend on the spatial resolution of environmental data but instead were affected by the number of points sampled from range maps and even more by the spatial congruence between input data. A strong agreement between occurrence data and range maps resulted in very similar SDM outputs, which suggests that expert knowledge can be a valuable alternative source of data to feed SDM and assess potential range shifts when the only available occurrences are biased or fragmentary.     

TESTING MACROEVOLUTIONARY HYPOTHESES WITH CLADISTIC ANALYSIS: EVIDENCE AGAINST RECTANGULAR EVOLUTION

The properties of cladistic data sets from small monophyletic groups (6–12 species) are investigated using computer simulations of macroevolution. Two evolutionary models are simulated: gradualism and the punctuated-equilibrium hypothesis. Under the conditions of our simulations these two models of evolution make consistently different predictions about the distribution of autapomorphies among species. When strict stasis is enforced, the punctuated-equilibrium hypothesis predicts that the most expected number of autapomorphies per species will be zero, no matter how many characters are used in the analysis. As the number of characters used in the analysis increases, the distribution of the number of autapomorphies per species becomes bimodal. Under gradualism, the distribution of autapomorphies remains unimodal under all conditions, but the number of species without autapomorphies can fall to zero. A survey of real cladograms of extant monophyletic groups from a wide range of taxa indicates that the predictions of the punctuated-equilibrium hypothesis about autapomorphies do not hold. This constitutes strong evidence against the punctuated-equilibrium hypothesis.     

Forecasting the future of biodiversity: a test of single‐ and multi‐species models for ants in North America

The geographic distributions of many taxonomic groups remain mostly unknown, hindering attempts to investigate the response of the majority of species on Earth to climate change using species distributions models (SDMs). Multi‐species models can incorporate data for rare or poorly‐sampled species, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single‐species models to those from a multi‐species modeling approach, Generalized Dissimilarity Modeling (GDM). We found that both single‐ and multi‐species models forecasted large changes in ant community composition in relatively warm environments. GDM predicted higher turnover than SDMs and across a larger contiguous area, including the southern third of North America and notably Central America, where the proportion of ants with relatively small ranges is high and where data limitations are most likely to impede the application of SDMs. Differences between approaches were also influenced by assumptions regarding dispersal, with forecasts being more similar if no‐dispersal was assumed. When full‐dispersal was assumed, SDMs predicted higher turnover in southern Canada than did GDM. Taken together, our results suggest that 1) warm rather than cold regions potentially could experience the greatest changes in ant fauna under climate change and that 2) multi‐species models may represent an important complement to SDMs, particularly in analyses involving large numbers of rare or poorly‐sampled species. Comparisons of the ability of single‐ and multi‐species models to predict observed changes in community composition are needed in order to draw definitive conclusions regarding their application to investigating climate change impacts on biodiversity.     

Integrating selection,niche, and diversification into a hierarchical conceptual framework

Recently, new phylogenetic comparative methods have been proposed to test for the association of biological traits with diversification patterns, with species ecological “niche” being one of the most studied traits. In general, these methods implicitly assume natural selection acting at the species level, thus implying the mechanism of species selection. However, natural selection acting at the organismal level could also influence diversification patterns (i.e., effect macroevolution). Owing to our scarce knowledge on multi-level selection regarding niche as a trait, we propose a conceptual model to discuss and guide the test between species selection and effect macroevolution within a hierarchical framework. We first assume niche as an organismal as well as a species’ trait that interacts with the environment and results in species-level differential fitness. Then, we argue that niche heritability, a requirement for natural selection, can be assessed by its phylogenetic signal. Finally, we propose several predictions that can be tested in the future by disentangling both types of evolutionary processes (species selection or effect macroevolution). Our framework can have important implications for guiding analyses that aim to understand the hierarchical perspective of evolution.     

Models of motor-assisted transport of intracellular particles

One-dimensional models are presented for the macroscopic intracellular transport of vesicles and organelles by molecular motors on a network of aligned intracellular filaments. A motor-coated vesicle or organelle is described as a diffusing particle binding intermittently to filaments, when it is transported at the motor velocity. Two models are treated in detail: 1) a unidirectional model, where only one kind of motor is operative and all filaments have the same polarity; and 2) a bidirectional model, in which filaments of both polarities exist (for example, a randomly polarized actin network for myosin motors) and/or particles have plus-end and minus-end motors operating on unipolar filaments (kinesin and dynein on microtubules). The unidirectional model provides net particle transport in the absence of a concentration gradient. A symmetric bidirectional model, with equal mixtures of filament polarities or plus-end and minus-end motors of the same characteristics, provides rapid transport down a concentration gradient and enhanced dispersion of particles from a point source by motor-assisted diffusion. Both models are studied in detail as a function of the diffusion constant and motor velocity of bound particles, and their rates of binding to and detachment from filaments. These models can form the basis of more realistic models for particle transport in axons, melanophores, and the dendritic arms of melanocytes, in which networks of actin filaments and microtubules coexist and motors for both types of filament are implicated.     

Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda

Aim Because of their broad distribution in geographical and ecological dimensions, seaweeds (marine macroalgae) offer great potential as models for marine biogeographical inquiry and exploration of the interface between macroecology and macroevolution. This study aims to characterize evolutionary niche dynamics in the common green seaweed genus Halimeda, use the observed insights to gain understanding of the biogeographical history of the genus and predict habitats that can be targeted for the discovery of species of special biogeographical interest. Location Tropical and subtropical coastal waters. Methods The evolutionary history of the genus is characterized using molecular phylogenetics and relaxed molecular clock analysis. Niche modelling is carried out with maximum entropy techniques and uses macroecological data derived from global satellite imagery. Evolutionary niche dynamics are inferred through application of ancestral character state estimation. Results A nearly comprehensive molecular phylogeny of the genus was inferred from a six‐locus dataset. Macroecological niche models showed that species distribution ranges are considerably smaller than their potential ranges. We show strong phylogenetic signal in various macroecological niche features. Main conclusions The evolution of Halimeda is characterized by conservatism for tropical, nutrient‐depleted habitats, yet one section of the genus managed to invade colder habitats multiple times independently. Niche models indicate that the restricted geographical ranges of Halimeda species are not due to habitat unsuitability, strengthening the case for dispersal limitation. Niche models identified hotspots of habitat suitability of Caribbean species in the eastern Pacific Ocean. We propose that these hotspots be targeted for discovery of new species separated from their Caribbean siblings since the Pliocene rise of the Central American Isthmus.     

Interspecific Competition Models Derived from Competition Among Individuals

This paper demonstrates how discrete-time models describing population dynamics of two competing species can be derived in a bottom-up manner by considering competition for resources among individuals and the spatial distribution of individuals. The competition type of each species is assumed to be either scramble, contest, or an intermediate between them. Individuals of two species are distributed over resource sites or patches following one of three distribution functions. According to the combination of competition types of the two species and the distribution of individuals, various interspecific competition models are derived. Furthermore, a general interspecific competition model that includes various competition models as special cases is derived for each distribution of individuals. Finally, this paper examines dynamics of some of the derived competition models and shows that the likelihood of coexistence of the two species varies greatly, depending on the type of spatial distribution of individuals.     

A new method for handling missing species in diversification analysis applicable to randomly or nonrandomly sampled phylogenies

Chronograms from molecular dating are increasingly being used to infer rates of diversification and their change over time. A major limitation in such analyses is incomplete species sampling that moreover is usually nonrandom. While the widely used γ statistic with the Monte Carlo constant-rates test or the birth-death likelihood analysis with the δ AICrc test statistic are appropriate for comparing the fit of different diversification models in phylogenies with random species sampling, no objective automated method has been developed for fitting diversification models to nonrandomly sampled phylogenies. Here, we introduce a novel approach, CorSiM, which involves simulating missing splits under a constant rate birth-death model and allows the user to specify whether species sampling in the phylogeny being analyzed is random or nonrandom. The completed trees can be used in subsequent model-fitting analyses. This is fundamentally different from previous diversification rate estimation methods, which were based on null distributions derived from the incomplete trees. CorSiM is automated in an R package and can easily be applied to large data sets. We illustrate the approach in two Araceae clades, one with a random species sampling of 52% and one with a nonrandom sampling of 55%. In the latter clade, the CorSiM approach detects and quantifies an increase in diversification rate, whereas classic approaches prefer a constant rate model; in the former clade, results do not differ among methods (as indeed expected since the classic approaches are valid only for randomly sampled phylogenies). The CorSiM method greatly reduces the type I error in diversification analysis, but type II error remains a methodological problem.     

Paris-type mycorrhizas in Smilax aspera L. growing in a Mediterranean scherophyllous wood

Paris- type mycorrhiza is described in Smilax aspera L., an evergreen climbing plant of Mediterranean sclerophyllous woods. Wild plants were sampled from a protected area inside the Regional Natural Park Migliarino-S.Rossore-Massaciuccoli, on the northwestern coast of Italy, near Pisa. Mycorrhizas formed by S. aspera were identified as a variation of Paris-type arbuscular mycorrhizas. Detailed observations on stained roots and on fresh root sections showed that, after forming the appressorium, the fungus colonized the root by penetrating individual cells, growing intracellularly from cell to cell, and producing many coils and terminal arbuscules. S. aspera seedlings inoculated with the arbuscular mycorrhizal fungi Glomus mosseae and G. viscosum, which are known to form Arum-type mycorrhizas in many plant species, produced the same Paris-type-like mycorrhizas found in nature. This confirms that the type of arbuscular mycorrhizal infection is largely governed by the plant host genotype. Plants of S. aspera inoculated with G. mosseae and G. viscosum had larger growth increments than uninoculated plants. Thus Paris-type mycorrhizas produce growth responses comparable to those of Arum-type mycorrhizas. Accepted: 11 January 2000     

Fixation in haploid populations exhibiting density dependence II: the quasi-neutral case

We determine fixation probabilities in a model of two competing types with density dependence. The model is defined as a two-dimensional birth-and-death process with density-independent death rates, and birth rates that are a linearly decreasing function of total population density. We treat the 'quasi-neutral case' where both types have the same equilibrium population densities. This condition results in birth rates that are proportional to death rates. This can be viewed as a life history trade-off. The deterministic dynamics possesses a stable manifold of mixtures of the two types. We show that the fixation probability is asymptotically equal to the fixation probability at the point where the deterministic flow intersects this manifold. The deterministic dynamics predicts an increase in the proportion of the type with higher birth rate in growing populations (and a decrease in shrinking populations). Growing (shrinking) populations therefore intersect the manifold at a higher (lower) than initial proportion of this type. On the center manifold, the fixation probability is a quadratic function of initial proportion, with a disadvantage to the type with higher birth rate. This disadvantage arises from the larger fluctuations in population density for this type. These results are asymptotically exact and have relevance for allele fixation, models of species abundance, and epidemiological models.     

Deep learning image analysis models pretrained on daily objects are useful for the preliminary characterization of particulate pharmaceutical samples

Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput instrumentation and analyzing the populations data. The analysis includes conventional metrics such as the particle size distribution but can be more sophisticated by interpreting other visual/morphological features. To avoid the hurdles of building new image analysis models capable of extracting such relevant features from scratch, we propose using well-established pretrained deep learning image analysis models such as EfficientNet. We demonstrate that such models are useful as a prescreening tool for high-level characterization of biopharmaceutical particle image data. We show that although these models are originally trained for completely different tasks (such as the classification of daily objects in the ImageNet database), the visual feature vectors extracted by such models can be useful for studying different types of subvisible particles. This applicability is illustrated through multiple case studies: (i) particle risk assessment in prefilled syringe formulations containing different particle types such as silicone oil, (ii) method comparability with the example of accelerated forced degradation, and (iii) excipient influence on particle morphology with the example of Polysorbate 80 (PS80). As examples of agnostic applicability of pretrained models, we also elucidate the application to two high-throughput microscopy methods: microflow and background membrane imaging. We show that different particle populations with different morphological and visual features can be identified in different samples by leveraging out-of-the-box pretrained models to analyze images from each sample.     

Individual-Based Competition Between Species with Spatial Correlation and Aggregation

In order to clarify the theoretical relationship between individual behavior and population-level competition between two species with spatial correlation, this paper describes how discrete-time competition equations for the two species can be derived from local resource competition among individuals. Competition type of each species is either scramble, contest, or modified contest, and for various combinations of two competition types, different competition models are derived. Simple competition models that can approximate the above models when competition is weak are also derived. Furthermore, the derived models are used to investigate how coexistence conditions and coexistence probability depend on spatial correlation and aggregation of individuals. For the weak competition models, spatial aggregation and non-correlation, in terms of measures adopted here, play exactly symmetric roles in promoting coexistence. In contrast, for the fully developed models, spatial aggregation generally exerts stronger effects than non-correlation on coexistence. Coexistence probability also depends greatly on competition types. For example, two species are generally more likely to coexist when they are of the same competition type than of different competition types. Coexistence probabilities from the mathematical analysis are in good agreement with those from individual-based simulations.     

Arum- and Paris-type arbuscular mycorrhizas in a mixed pine forest on sand dune soil in Niigata Prefecture, central Honshu, Japan

Arbuscular mycorrhizas (AM) are the most widespread mycorrhiza in nature and form two morphologies, Arum- and Paris-type. The determining factors defining the two different morphologies are not well understood. In this study, the distribution of Arum- and Paris-type AM was determined in a mixed pine forest. A total of 35 plant species belonging to 20 families and 32 genera were identified and examined for AM colonization and morphological types. AM morphological types in 14 families were confirmed as follows: Arum-type in Rosaceae, Oleaceae, Lauraceae, Vitaceae and Compositae, Paris-type in Aquifoliaceae, Ulmaceae, Araliaceae, Theaceae, Magnoliaceae, Rubiaceae and Dioscoraceae, and both and/or intermediate types in Caprifoliaceae and Gramineae. Plant families whose AM morphological status was previously unknown were clarified as follows: Polygonaceae and Commelinaceae showed Arum-type morphology; Celastraceae, Menispermaceae and Elaeagnaceae had typical Paris-type morphology. The proportion of Arum-type to Paris-type species decreased in the following order: annuals > perennials > deciduous species > evergreen species, and pioneer group > early successional group > late successional group. Evergreen plants had a higher tendency to form Paris-type AM than annuals, perennials and deciduous plants. The results indicate that environmental changes, such as shade during plant succession, control the distribution of plant growth forms in mixed pine forest and may also play a part in the distribution of Arum- and Paris-type morphology. The identity of the plant seems to strongly influence AM morphology, though control by the fungal genome cannot be ruled out.     

On linear perturbations of the Ricker model

A class of linearly perturbed discrete-time single species scramble competition models, like the Ricker map, is considered. Perturbations can be of both recruitment and harvesting types. Stability (bistability) is considered for models, where parameters of the map do not depend on time. For models with recruitment, the result is in accordance with Levin and May conjecture [S.A. Levin, R.M. May, A note on difference delay equations, Theor. Pop. Biol. 9 (1976) 178]: the local stability of the positive equilibrium implies its global stability. For intrinsic growth rate r-->infinity the way to chaos is broken down to get extinction of population for the depletion case and to establish a stable two-cycle period for models with immigration. The latter behaviour is also studied for models with random discrete constant perturbations of recruitment type. Extinction, persistence and existence of periodic solutions are studied for the perturbed Ricker model with time-dependent parameters.     

Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors

Summary Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point‐process models and binary‐regression models for case‐augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point‐process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence‐only sample sizes. Analyses of presence‐only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site‐occupancy analyses of detections and nondetections of these species.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.