Branching patterns in phylogenies cannot distinguish diversity-dependent diversification from time-dependent diversification.

One of the primary goals of macroevolutionary biology has been to explain general trends in long-term diversity patterns, including whether such patterns correspond to an upscaling of processes occurring at lower scales. Reconstructed phylogenies often show decelerated lineage accumulation over time. This pattern has often been interpreted as the result of diversity-dependent (DD) diversification, where the accumulation of species causes diversification to decrease through niche filling. However, other processes can also produce such a slowdown, including time dependence without diversity dependence. To test whether phylogenetic branching patterns can be used to distinguish these two mechanisms, we formulated a time-dependent, but diversity-independent model that matches the expected diversity through time of a DD model. We simulated phylogenies under each model and studied how well likelihood methods could recover the true diversification mode. Standard model selection criteria always recovered diversity dependence, even when it was not present. We correct for this bias by using a bootstrap method and find that neither model is decisively supported. This implies that the branching pattern of reconstructed trees contains insufficient information to detect the presence or absence of diversity dependence. We advocate that tests encompassing additional data, for example, traits or range distributions, are needed to evaluate how diversity drives macroevolutionary trends.

Low-coverage genomic data resolve the population divergence and gene flow history of an Australian rain forest fig wasp.

Population divergence and gene flow are key processes in evolution and ecology. Model-based analysis of genome-wide data sets allows discrimination between alternative scenarios for these processes even in nonmodel taxa. We used two complementary approaches (one based on the blockwise site frequency spectrum [bSFS], the second on the pairwise sequentially Markovian coalescent [PSMC]) to infer the divergence history of a fig wasp, Pleistodontes nigriventris. Pleistodontes nigriventris and its fig tree mutualist Ficus watkinsiana are restricted to rain forest patches along the eastern coast of Australia and are separated into The Northern population is to the north of the Southern populations by two dry forest corridors (the Burdekin and St. Lawrence Gaps). We generated whole genome sequence data for two haploid males per population and used the bSFS approach to infer the timing of divergence between northern and southern populations of P. nigriventris, and to discriminate between alternative isolation with migration (IM) and instantaneous admixture (ADM) models of postdivergence gene flow. Pleistodontes nigriventris has low genetic diversity (π = 0.0008), to our knowledge one of the lowest estimates reported for a sexually reproducing arthropod. We find strongest support for an ADM model in which the two populations diverged ca. 196 kya in the late Pleistocene, with almost 25% of northern lineages introduced from the south during an admixture event ca. 57 kya. This divergence history is highly concordant with individual population demographies inferred from each pair of haploid males using PSMC. Our analysis illustrates the inferences possible with genome-level data for small population samples of tiny, nonmodel organisms and adds to a growing body of knowledge on the population structure of Australian rain forest taxa.

Above- and belowground carbon stocks are decoupled in secondary tropical forests and are positively related to forest age and soil nutrients respectively.

Reducing atmospheric CO2 is an international priority. One way to assist stabilising and reducing CO2 is to promote secondary tropical forest regrowth on abandoned agricultural land. However, relationships between above- and belowground carbon stocks with secondary forest age and specific soil nutrients remain unclear. Current global estimates for CO2 uptake and sequestration in secondary tropical forests focus on aboveground biomass and are parameterised using relatively coarse metrics of soil fertility. Here, we estimate total carbon stocks across a chronosequence of regenerating secondary forest stands (40-120 years old) in Panama, and assess the relationships between both above- and belowground carbon stocks with stand age and specific soil nutrients. We estimated carbon stocks in aboveground biomass, necromass, root biomass, and soil. We found that the two largest carbon pools - aboveground biomass and soil - have distinct relationships with stand age and soil fertility. Aboveground biomass contained ~61-97 Mg C ha-1 (24-39% total carbon stocks) and significantly increased with stand age, but showed no relationship with soil nutrients. Soil carbon stocks contained ~128-206 Mg C ha-1 (52-70% total stocks) and were unrelated to stand age, but were positively related to soil nitrogen. Root biomass carbon stocks tracked patterns exhibited by aboveground biomass. Necromass carbon stocks did not increase with stand age, but stocks were held in larger pieces of deadwood in older stands. Comparing our estimates to published data from younger and older secondary forests in the surrounding landscape, we show that soil carbon recovers within 40 years of forest regeneration, but aboveground biomass carbon stocks continue to increase past 100 years. Above- and belowground carbon stocks appear to be decoupled in secondary tropical forests. Paired measures of above- and belowground carbon stocks are necessary to reduce uncertainty in large-scale models of atmospheric CO2 uptake and storage by secondary forests.

The Latitudinal Diversity Gradient: Novel Understanding through Mechanistic Eco-evolutionary Models.

The latitudinal diversity gradient (LDG) is one of the most widely studied patterns in ecology, yet no consensus has been reached about its underlying causes. We argue that the reasons for this are the verbal nature of existing hypotheses, the failure to mechanistically link interacting ecological and evolutionary processes to the LDG, and the fact that empirical patterns are often consistent with multiple explanations. To address this issue, we synthesize current LDG hypotheses, uncovering their eco-evolutionary mechanisms, hidden assumptions, and commonalities. Furthermore, we propose mechanistic eco-evolutionary modeling and an inferential approach that makes use of geographic, phylogenetic, and trait-based patterns to assess the relative importance of different processes for generating the LDG.

Whole-genome data reveal the complex history of a diverse ecological community.

How widespread ecological communities assemble remains a key question in ecology. Trophic interactions between widespread species may reflect a shared population history or ecological fitting of local pools of species with very different population histories. Which scenario applies is central to the stability of trophic associations and the potential for coevolution between species. Here we show how alternative community assembly hypotheses can be discriminated using whole-genome data for component species and provide a likelihood framework that overcomes current limitations in formal comparison of multispecies histories. We illustrate our approach by inferring the assembly history of a Western Palearctic community of insect herbivores and parasitoid natural enemies, trophic groups that together comprise 50% of terrestrial species. We reject models of codispersal from a shared origin and of delayed enemy pursuit of their herbivore hosts, arguing against herbivore attainment of "enemy-free space." The community-wide distribution of species expansion times is also incompatible with a random, neutral model of assembly. Instead, we reveal a complex assembly history of single- and multispecies range expansions through the Pleistocene from different directions and over a range of timescales. Our results suggest substantial turnover in species associations and argue against tight coevolution in this system. The approach we illustrate is widely applicable to natural communities of nonmodel species and makes it possible to reveal the historical backdrop against which natural selection acts.

Survival and divergence in a small group: The extraordinary genomic history of the endangered Apennine brown bear stragglers.

About 100 km east of Rome, in the central Apennine Mountains, a critically endangered population of ∼50 brown bears live in complete isolation. Mating outside this population is prevented by several 100 km of bear-free territories. We exploited this natural experiment to better understand the gene and genomic consequences of surviving at extremely small population size. We found that brown bear populations in Europe lost connectivity since Neolithic times, when farming communities expanded and forest burning was used for land clearance. In central Italy, this resulted in a 40-fold population decline. The overall genomic impact of this decline included the complete loss of variation in the mitochondrial genome and along long stretches of the nuclear genome. Several private and deleterious amino acid changes were fixed by random drift; predicted effects include energy deficit, muscle weakness, anomalies in cranial and skeletal development, and reduced aggressiveness. Despite this extreme loss of diversity, Apennine bear genomes show nonrandom peaks of high variation, possibly maintained by balancing selection, at genomic regions significantly enriched for genes associated with immune and olfactory systems. Challenging the paradigm of increased extinction risk in small populations, we suggest that random fixation of deleterious alleles (i) can be an important driver of divergence in isolation, (ii) can be tolerated when balancing selection prevents random loss of variation at important genes, and (iii) is followed by or results directly in favorable behavioral changes.

Woody lianas increase in dominance and maintain compositional integrity across an Amazonian dam-induced fragmented landscape.

Tropical forest fragmentation creates insular biological communities that undergo species loss and changes in community composition over time, due to area- and edge-effects. Woody lianas thrive in degraded and secondary forests, due to their competitive advantage over trees in these habitats. Lianas compete both directly and indirectly with trees, increasing tree mortality and turnover. Despite our growing understanding of liana-tree dynamics, we lack detailed knowledge of the assemblage-level responses of lianas themselves to fragmentation, particularly in evergreen tropical forests. We examine the responses of both sapling and mature liana communities to landscape-scale forest insularization induced by a mega hydroelectric dam in the Brazilian Amazon. Detailed field inventories were conducted on islands created during reservoir filling, and in nearby mainland continuous forest. We assess the relative importance of variables associated with habitat fragmentation such as area, isolation, surrounding forest cover, fire and wind disturbance, on liana community attributes including abundance, basal area, diversity, and composition. We also explore patterns of liana dominance relative to tree saplings and adults ≥10 cm diameter at breast height. We find that 1) liana community composition remains remarkably similar across mainland continuous forest and islands, regardless of extreme area- and edge- effects and the loss of vertebrate dispersers in the latter; and 2) lianas are increasing in dominance relative to trees in the sapling layer in the most degraded islands, with both the amount of forest cover surrounding islands and fire disturbance history predicting liana dominance. Our data suggest that liana communities persist intact in isolated forests, regardless of extreme area- and edge-effects; while in contrast, tree communities simultaneously show evidence of increased turnover and supressed recruitment. These processes may lead to lianas becoming a dominant component of this dam-induced fragmented landscape in the future, due to their competitive advantage over trees in degraded forest habitats. Additional loss of tree biomass and diversity brought about through competition with lianas, and the concurrent loss of carbon storage, should be accounted for in impact assessments of future dam development.

Using targeted enrichment of nuclear genes to increase phylogenetic resolution in the neotropical rain forest genus Inga (Leguminosae: Mimosoideae).

Evolutionary radiations are prominent and pervasive across many plant lineages in diverse geographical and ecological settings; in neotropical rainforests there is growing evidence suggesting that a significant fraction of species richness is the result of recent radiations. Understanding the evolutionary trajectories and mechanisms underlying these radiations demands much greater phylogenetic resolution than is currently available for these groups. The neotropical tree genus Inga (Leguminosae) is a good example, with ~300 extant species and a crown age of 2-10 MY, yet over 6 kb of plastid and nuclear DNA sequence data gives only poor phylogenetic resolution among species. Here we explore the use of larger-scale nuclear gene data obtained though targeted enrichment to increase phylogenetic resolution within Inga. Transcriptome data from three Inga species were used to select 264 nuclear loci for targeted enrichment and sequencing. Following quality control to remove probable paralogs from these sequence data, the final dataset comprised 259,313 bases from 194 loci for 24 accessions representing 22 Inga species and an outgroup (Zygia). Bayesian phylogenies reconstructed using either all loci concatenated or a gene-tree/species-tree approach yielded highly resolved phylogenies. We used coalescent approaches to show that the same targeted enrichment data also have significant power to discriminate among alternative within-species population histories within the widespread species I. umbellifera. In either application, targeted enrichment simplifies the informatics challenge of identifying orthologous loci associated with de novo genome sequencing. We conclude that targeted enrichment provides the large volumes of phylogenetically-informative sequence data required to resolve relationships within recent plant species radiations, both at the species level and for within-species phylogeographic studies.

Inferring Bottlenecks from Genome-Wide Samples of Short Sequence Blocks.

The advent of the genomic era has necessitated the development of methods capable of analyzing large volumes of genomic data efficiently. Being able to reliably identify bottlenecks--extreme population size changes of short duration--not only is interesting in the context of speciation and extinction but also matters (as a null model) when inferring selection. Bottlenecks can be detected in polymorphism data via their distorting effect on the shape of the underlying genealogy. Here, we use the generating function of genealogies to derive the probability of mutational configurations in short sequence blocks under a simple bottleneck model. Given a large number of nonrecombining blocks, we can compute maximum-likelihood estimates of the time and strength of the bottleneck. Our method relies on a simple summary of the joint distribution of polymorphic sites. We extend the site frequency spectrum by counting mutations in frequency classes in short sequence blocks. Using linkage information over short distances in this way gives greater power to detect bottlenecks than the site frequency spectrum and potentially opens up a wide range of demographic histories to blockwise inference. Finally, we apply our method to genomic data from a species of pig (Sus cebifrons) endemic to islands in the center and west of the Philippines to estimate whether a bottleneck occurred upon island colonization and compare our scheme to Li and Durbin's pairwise sequentially Markovian coalescent (PSMC) both for the pig data and using simulations.

ABC inference of multi-population divergence with admixture from unphased population genomic data.

Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida.

Likelihood-based inference of population history from low-coverage de novo genome assemblies.

Short-read sequencing technologies have in principle made it feasible to draw detailed inferences about the recent history of any organism. In practice, however, this remains challenging due to the difficulty of genome assembly in most organisms and the lack of statistical methods powerful enough to discriminate between recent, nonequilibrium histories. We address both the assembly and inference challenges. We develop a bioinformatic pipeline for generating outgroup-rooted alignments of orthologous sequence blocks from de novo low-coverage short-read data for a small number of genomes, and show how such sequence blocks can be used to fit explicit models of population divergence and admixture in a likelihood framework. To illustrate our approach, we reconstruct the Pleistocene history of an oak-feeding insect (the oak gallwasp Biorhiza pallida), which, in common with many other taxa, was restricted during Pleistocene ice ages to a longitudinal series of southern refugia spanning the Western Palaearctic. Our analysis of sequence blocks sampled from a single genome from each of three major glacial refugia reveals support for an unexpected history dominated by recent admixture. Despite the fact that 80% of the genome is affected by admixture during the last glacial cycle, we are able to infer the deeper divergence history of these populations. These inferences are robust to variation in block length, mutation model and the sampling location of individual genomes within refugia. This combination of de novo assembly and numerical likelihood calculation provides a powerful framework for estimating recent population history that can be applied to any organism without the need for prior genetic resources.