Search bioRxiv⌕ Search

SEARCH · Search bioRxiv

Results for “ecology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources
bioRxiv2018

Interaction Modifications Disrupt The Structure And Stability Of Ecological Communities

Trophic interaction modifications, where a consumer-resource interaction is influenced by an additional species, are established as being prevalent throughout ecological networks. Despite this, their influence on the structure of interaction distributions within communities has not yet been examined. Although empirical information about the distribution of interaction modifications is currently sparse, the non-trophic effects they induce will be structured by the underlying network of trophic interactions. Here we test the impact of interaction modifications, introduced under a range of distributional assumptions to artificial and empirical trophic networks, on the overall structure of interactions within communities. We show that local stability and reactivity is critically dependent on the inter-relationship between the trophic and non-trophic effects. Depending on their distribution, interaction modifications can generate significant additional structure to community interactions making analyses of the stability of ecological systems based solely on trophic networks unreliable. Empirical information on the topological and strength distributions of interaction modifications will be a key part of understanding the dynamics of communities.

ecology↗
bioRxiv2018

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems intrinsic predictability - the highest achievable predictability given the degree to which system dynamics are the result of deterministic v. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. By means of simulations we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a dataset of 461 empirical ecological time series. We show how deviations from the expected PE-FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics.\n\nThese results demonstrate a theoretically-grounded basis for a model-free evaluation of a systems intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.\n\nGlossaryActive information: The amount of information that is available to forecasting models (redundant information minus lost information; Fig. 1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC=\"FIGDIR/small/350017_fig1a.gif\" ALT=\"Figure 1A\">\nView larger version (9K):\norg.highwire.dtl.DTLVardef@934d9eorg.highwire.dtl.DTLVardef@ccdc10org.highwire.dtl.DTLVardef@1839ed9org.highwire.dtl.DTLVardef@31bd70_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1A.C_FLOATNO The total information content of an observation of a system at a given state in time, St, is depicted by filled circles with past states (St-1 and St-2) represented by shades of grey, i) lack of overlap between past and present states illustrating a case where no information is transmitted from past states (i.e. a purely stochastic system), with low redundancy and high Shannon entropy rate, ii) intermediate overlap indicating a case when some information is transferred from past to present (i.e. a deterministic system strongly driven by stochastic forcing), with intermediate redundancy and Shannon entropy rate, iii) large overlap indicating a case when the current state is mostly determined by the previous state (i.e. a highly deterministic system), with high redundancy and low Shannon entropy rate. Note that both the redundancy and Shannon entropy rate of a system are intrinsic properties of the system and will only change if the system itself changes.\n\nC_FIG Forecasting error (FE): A measure of the discrepancy between a models forecasts and the observed dynamics of a system. Common measures of forecast error are root mean squared error and mean absolute error.\n\nEntropy: Measures the average amount of information in the outcome of a stochastic process.\n\nInformation: Any entity that provides answers and resolves uncertainty about a process. When information is calculated using logarithms to the base two (i.e. information in bits), it is the minimum number of yes/no questions required, on average, to determine the identity of the symbol (Jost 2006). The information in an observation consists of information inherited from the past (redundant information), and of new information.\n\nIntrinsic predictability: the maximum achievable predictability of a system (Beckage et al. 2011).\n\nLost information: The part of the redundant information lost due to measurement or sampling error, or transformations of the data (Fig. 1).\n\nNew information, Shannon entropy rate: The Shannon entropy rate quantifies the average amount of information per observation in a time series that is unrelated to the past, i.e., the new information (Fig. 1).\n\nNonlinearity: When the deterministic processes governing system dynamics depend on the state of the system.\n\nPermutation entropy (PE): permutation entropy is a measure of the complexity of a time series (Bandt & Pompe, 2002) that is negatively correlated with a systems predictability (Garland et al. 2015). Permutation entropy quantifies the combined new and lost information. PE is scaled to range between a minimum of 0 and a maximum of 1.\n\nRealized predictability: the achieved predictability of a system from a given forecasting model.\n\nRedundant information: The information inherited from the past, and thus the maximum amount of information available for use in forecasting (Fig. 1).\n\nSymbols, words, permutations: symbols are simply the smallest unit in a formal language such as the letters in the English alphabet i.e., {\"A\", \"B\",..., \"Z\"}. In information theory the alphabet is more abstract, such as elements in the set {\"up\", \"down\"} or {\"1\", \"2\", \"3\"}. Words, of length m refer to concatenations of the symbols (e.g., up-down-down) in a set. Permutations are the possible orderings of symbols in a set. In this manuscript, the words are the permutations that arise from the numerical ordering of m data points in a time series.\n\nWeighted permutation entropy (WPE): a modification of permutation entropy (Fadlallah et al., 2013) that distinguishes between small-scale, noise-driven variation and large-scale, system-driven variation by considering the magnitudes of changes in addition to the rank-order patterns of PE.

ecology↗
bioRxiv2018

Assessing the impact of taxon resolution on network structure, with implication for comparative ecology

Constructing networks has become an indispensable approach in understanding how different taxa interact. However, methodologies vary widely among studies, potentially limiting our ability to meaningfully compare results. In particular, how network architecture is influenced by the extent to which nodes are resolved to either taxa or taxonomic units is poorly understood. To address this, here we collate nine datasets of ecological interactions, from both observations and DNA metabarcoding, and construct networks under a range of commonly-used node resolutions. We demonstrate that small changes in node resolution can cause wide variation in almost all key metric values, including robustness and nestedness. Moreover, relative values of metrics such as robustness were seen to fluctuate continuously with node resolution, thereby potentially confounding comparisons of networks, as well as interpretations concerning their constituent ecological interactions. These findings highlight the need for care when comparing networks, especially where these differ with respect to node resolution.\n\nStatement of authorshipDRHB, SJR and ELC conceived of the project, DRHB facilitated fieldwork in Malaysia, DRHB, HFMO and ELC undertook field collections, DRHB, SCLC and HFMO analysed the data, and DRHB wrote the manuscript with input from all authors.\n\nData Accessibility StatementAll data used in this analysis will be archived in Dryad and made available by DOI. Specific analysis scripts are available on GitHub with links given in the manuscript.

ecology↗
bioRxiv2018

A framework for dissecting ecological mechanisms underlying the island species-area relationship

The relationship between an islands size and the number of species on that island--the island species-area relationship (ISAR)--is one of the most well-known patterns in biogeography, and forms the basis for understanding biodiversity loss in response to habitat loss and fragmentation. Nevertheless, there is contention about exactly how to estimate the ISAR, and the influence of the three primary ecological mechanisms--random sampling, disproportionate effects, and heterogeneity-- that drive it. Key to this contention is that estimates of the ISAR are often confounded by sampling and estimates of measures (i.e., island-level species richness) that are not diagnostic of potential mechanisms. Here, we advocate a sampling-explicit approach for disentangling the possible ecological mechanisms underlying the ISAR using parameters derived from individual-based rarefaction curves estimated across spatial scales. If the parameters derived from rarefaction curves at each spatial scale show no relationship with island area, we cannot reject the hypothesis that ISARs result only from random sampling. However, if the derived metrics change with island area, we can reject random sampling as the only operating mechanism, and infer that effects beyond sampling (i.e., disproportionate effects and/or heterogeneity) are also operating. Finally, if parameters indicative of within-island spatial variation in species composition (i.e., {beta}-diversity) increase with island area, we can conclude that intra-island compositional heterogeneity plays a role in driving the ISAR. We illustrate this approach using representative case studies, including oceanic islands, natural island-like patches, and habitat fragments from formerly continuous habitat, illustrating several combinations of underlying mechanisms. This approach will offer insight into the role of sampling and other processes that underpin the ISAR, providing a more complete understanding of how, and some indication of why, patterns of biodiversity respond to gradients in island area.

ecology↗
bioRxiv2018

The diversity of interaction types drives the functioning of ecological communities

Ecological communities are undeniably diverse, both in terms of the species that compose them as well as the type of interactions that link species to each other. Despite this long-recognition of the coexistence of multiple interaction types in nature, little is known about the consequences of this diversity for community functioning. In the ongoing context of global change and increasing species extinction rates, it seems crucial to improve our understanding of the drivers of the relationship between species diversity and ecosystem functioning.\n\nHere, using a multispecies dynamical model of ecological communities including various interaction types (e.g. competition for space, predator interference, recruitment facilitation), we studied the role of the presence and the intensity of these interactions for species diversity, community functioning (biomass and production) and the relationship between diversity and functioning.\n\nTaken jointly, the diverse interactions have significant effects on species diversity, whose amplitude and sign depend on the type of interactions involved and their relative abundance. They however consistently increase the slope of the relationship between diversity and functioning, suggesting that species losses might have stronger effects on community functioning than expected when ignoring the diversity of interaction types and focusing on feeding interactions only.

ecology↗
bioRxiv2018

The variability spectrum of ecological communities: How common and rare species shape stability patterns

Empirical knowledge of ecosystem stability and diversity-stability relationships is mostly based on the analysis of temporal variability of population and ecosystem properties. Variability, however, often depends on external factors that act as disturbances, making it difficult to compare its value across systems and relate it to other stability concepts. Here we show how variability, when viewed as a response to stochastic perturbations, can reveal inherent stability properties of ecological communities, with clear connections with other stability notions. This requires abandoning one-dimensional representations, in which a single variability measurement is taken as a proxy for how stable a system is, and instead consider the whole set of variability values associated to a given community, reflecting the whole set of perturbations that can generate variability. Against the vertiginous dimensionality of the perturbation set, we show that a generic variability-abundance pattern emerges from community assembly, which relates variability to the abundance of perturbed species. As a consequence, the response to stochastic immigration is governed by rare species while common species drive the response to environmental perturbations. In particular, the contrasting contributions of different species abundance classes can lead to opposite diversity-stability patterns, which can be understood from basic statistics of the abundance distribution. Our work shows that a multidimensional perspective on variability allows one to better appreciate the dynamical richness of ecological systems and the underlying meaning of their stability patterns.

ecology↗
bioRxiv2018

A machine learning approach for the spatiotemporal forecasting of ecological phenomena using dates of species occurrence records

O_LISpatiotemporal forecasts of ecological phenomena are highly useful and significant in scientific and socio-economic applications. Nevertheless, developing the correlative models to make these forecasts is often stalled by the inadequate availability of the ecological time-series data. On the contrary, considerable amounts of temporally discrete biological records are being stored in public databases, and often include the sites and dates of the observation. While these data are reasonably suitable for the development of spatiotemporal forecast models, this possibility remains mostly untested.\nC_LIO_LIIn this paper, we test an approach to develop spatiotemporal forecasts based on the dates and locations found in species occurrence records. This approach is based on time-series classification, a field of machine learning, and involves the application of a machine-learning algorithm to classify between time-series representing the environmental conditions that precede the occurrence records and time-series representing other environmental conditions, such as those that generally occur in the sites of the records. We employed this framework to predict the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in countries of Europe, from 2009 to 2015. We compared the predictions from this approach with those from a null model, based on the calendar dates of the records.\nC_LIO_LIForecasts made from the environmental-based approach were consistently superior to those drawn from the date-based approach, averaging an area under the receiver operating characteristic curve (AUC) of 0.9 for B. edulis and 0.88 for M. procera, compared to an average AUC of 0.83 achieved by the null models for both species. Prediction errors were distributed across the study area and along the years, lending support to the spatiotemporal representativeness of the values of accuracy measured.\nC_LIO_LIOur approach, based on species occurrence records, was able to provide useful forecasts of the timing of emergence of two mushroom species across Europe. Given the increased availability and information contained in this type of records, particularly those supplemented with photographs, the range of events that could be possible to forecast is vast.\nC_LI

ecology↗
bioRxiv2018

Ecological niche modeling the potential geographic distribution of four Culicoides species of veterinary significance in Florida

Epizootic hemorrhagic disease (EHD) is a viral arthropod-borne disease affecting wild and domestic ruminants. EHD virus (EHDV) is transmitted to vertebrate animal hosts by biting midges in the genus Culicoides. Culicoides sonorensis Latreille is the only confirmed vector of EHDV in the United States but is considered rare in Florida and not sufficiently abundant to support EHDV transmission. This study used ecological niche modeling to map the potential geographical distributions and associated ecological variable space of four Culicoides species suspected of transmitting EHDV in Florida, including Culicoides insignis, Culicoides stellifer, Culicoides debilipalpis and Culicoides venustus. Models were developed with the Genetic Algorithm for Rule Set Production in DesktopGARP v1.1.3 using species occurrence data from field sampling along with environmental variables from WorldClim and Trypanosomiasis and Land use in Africa. For three Culicoides species (C. insignis, C. stellifer and C. debilipalpis) 96 - 98% of the presence points were predicted across the Florida landscape (63.77% - 72.53%). For C. venustus, models predicted 98.00% of presence points across 27.42% of Florida. Geographic variations were detected between species. Culicoides insignis was predicted to be restricted to peninsular Florida, and in contrast, C. venustus was predicted to be primarily in north Florida and the panhandle region. Culicoides stellifer and C. debilipalpis were predicted nearly statewide. Environmental conditions also differed by species, with some species ranges predicted by more narrow ranges of variables than others. The Normalized Difference Vegetation Index (NDVI) was a major predictor of C. venustus and C. insignis presence. For C. stellifer, Land Surface Temperature, Middle Infrared were the most limiting predictors of presence. The limiting variables for C. debilipalpis were NDVI Bi-Annual Amplitude and NDVI Annual Amplitude at 22.45% and 28.09%, respectively. The model outputs, including maps and environmental variable range predictions generated from these experiments provide an important first pass at predicting species of veterinary importance in Florida. Because EHDV cannot exist in the environment without the vector, model outputs can be used to estimate the potential risk of disease for animal hosts across Florida. Results also provide distribution and habitat information useful for integrated pest management practices.

ecology↗
bioRxiv2026

An ecological model of masting reproduction matches empirical dynamics

Masting, characterized by highly variable, synchronized, and intermittent seed or fruit production, represents a common reproductive strategy among perennial plants and has profound ecological consequences. Resource provisioning and pollen limitation have long been viewed as central physiological mechanisms underlying this strategy, recent empirical evidence also highlights the role of weather cues in initiating and synchronizing reproductive effort. Drawing on mechanisms that drive periodicity in disease dynamics, this study proposes an alternative proximate mechanism for masting. We develop and analyze a stage-structured population growth model in which developmental delays create population-level cycles, and demographic stochasticity adds individual-level variation; together, yielding masting-like patterns. We compare the behaviour of this novel model with that of the widely used resource budget model and empirically observed patterns of masting in perennial plants. To quantify and compare model outputs and empirical observations, we employ three continuous metrics of masting that capture volatility, synchrony, and periodicity. Our study provides an alternative proximate mechanism for masting. Comparison of this novel mechanism and the established resource-budget model to empirical time-series reveals that both represent realistic yet distinct forms of masting reproduction. Together, these models provide a foundation for further exploration of the conditions under which this reproductive strategy can evolve. Beyond masting, our results highlight the general importance of life-history timing and demographic stochasticity in shaping population ecology.

ecology↗
bioRxiv2014

Ecological and Evolutionary Oscillations in Host-Parasite Population Dynamics, and The Red Queen

In a host-parasite system, the constitutive interaction among the species, regulated by the growth rates and functional response, may induce populations to approach equilibrium or sometimes to exhibit simple cycles or peculiar oscillations, such as chaos. A large carrying capacity coupled with appropriate parasitism effectiveness frequently drives long-term apparent oscillatory dynamics in population size. We name these oscillations due to the structure of the constitutive interaction among species as ecological.\n\nOn the other hand, there are also exceptional cases when the evolving quantitative traits of the hosts and parasites induce oscillating population size, which we call as evolutionary. This oscillatory behavior is dependent on the speed of evolutionary adaptation and degree of evolutionary trade-off. A moderate level of negative trade-off is essential for the existence of oscillations. Evolutionary oscillations due to the host-parasite coevolution (known as the Red Queen) can be observed beyond the ecological oscillations, especially when there are more than two competing species involved.\n\nOne Sentence SummaryWe investigate several cases yielding to oscillating host-parasite populations, and we found that the Red Queen hypothesis can explain some of the exceptional cases.\n\nGraphical Abstract:\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC=\"FIGDIR/small/001735_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1280478org.highwire.dtl.DTLVardef@3d6e28org.highwire.dtl.DTLVardef@10b2718org.highwire.dtl.DTLVardef@133a78d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Evolutionary Biology↗
bioRxiv2014

The dark side of theoretical ecology

Good science must be clearly transparent in its theories, models and experiments. Earlier David Tilman drew attention to the fact that ecologists investigate interspecific competition phenomenologically, rather than mechanistically. To create a mechanistic model of a complex dynamic system we need to logically describe interactions of its subsystems which lead to emergence of new properties on the macro-level. There are black-box, grey-box and white-box models of complex systems. Black-box models are completely nonmechanistic. We cannot investigate interactions of subsystems of such non-transparent model. A white-box model of a complex system has \"transparent walls\" and directly shows underlined mechanistic mechanisms - all events at micro-, meso-and macro-levels of the modeled dynamic system are directly visible at all stages. Grey-box models are intermediate. Basic ecological models are of black-box type, e.g. Malthusian, Verhulst, Lotka-Volterra models. These models are not individual-based and cannot show features of local interactions of individuals of competing species. That is why they principally cannot provide a mechanistic insight into interspecific competition. To create a white-box model we need a physical theory of the object domain and its intrinsic axiomatic system. On the basis of axiomatic system there is a possibility to logically generate a new knowledge by logical deterministic cellular automata. Understanding of biodiversity mechanisms is the global research priority. Only knowledge of mechanisms of interspecific interactions can allow us to efficiently operate in the field of biodiversity conservation. Obviously that such knowledge must be based on mechanistic models of species coexistence. In order to create a serviceable theory of biodiversity it is necessary to renew attempts to create a basic mechanistic model of species coexistence. But the question arises: Why ecological modelers prefer to use the heaviest black-box mathematical methods which cannot produce mechanistic models of complex dynamic systems in principle, and why they do not use simple and long-known pure logical deterministic cellular automata, which easily can produce white-box models and directly generate clear mechanistic insights into dynamics of complex systems?

Preprint↗
bioRxiv2014

SpeciesGeoCoder: Fast categorisation of species occurrences for analyses of biodiversity, biogeography, ecology and evolution

Understanding the patterns and processes underlying the uneven distribution of biodiversity across space and time constitutes a major scientific challenge in evolutionary biology. With rapidly accumulating species occurrence data, there is an increasing need for making the process of coding species into operational units for biogeographic and evolutionary analyses faster, automated, transparent and reproducible. Here we present SpeciesGeoCoder, a free software package written in Python and R, that allows for easy coding of species into user-defined areas. These areas may be of any size and be purely geographical (i.e., polygons) such as political units, conservation areas, biomes, islands, biodiversity hotspots, and areas of endemism, but may also include altitudinal ranges. This flexibility allows scoring species into complex categories, such as those encountered in topographically and ecologically heterogeneous landscapes. In addition, SpeciesGeoCoder can be used to facilitate sorting and cleaning of occurrence data. The various outputs of SpeciesGeoCoder include quantitative biodiversity statistics, global and local distribution maps, and NEXUS files that can be directly used in many phylogeny-based applications for ancestral state reconstruction, investigations on biome evolution, and diversification rate analyses. Our simulations indicate that even datasets containing hundreds of millions of records can be analysed in relatively short time using a regular desktop computer. We exemplify the use of our program through two contrasting examples: i) inferring historical dispersal of birds across the Isthmus of Panama, separating lowland vs. montane species and optimising the results onto a species-level, dated phylogeny; and ii) exploring seasonal variations in the occurrence of 10 GPS-tracked individuals of moose (Alces alces) over one year in northern Sweden. These analyses show that SpeciesGeoCoder allows an easy, flexible and fast categorisation of species distribution data for various analyses in ecology and evolution, with potential use at different spatial, taxonomic and temporal scales.

Evolutionary Biology↗
bioRxiv2016

Integrating ecological genomics and eco-evo-devo reveals multiple adaptive peaks in ant populations of the Arizona Sky Islands

Uncovering the genetic basis of adaptation is one of the greatest challenges facing modern evolutionary biology, often sparking much controversy, especially among practitioners within different subfields. Here we ask where is the locus of adaptation from the perspective of ecological genomics (ecogen) and evolutionary developmental biology (evodevo). Ecogen focuses on identifying loci under selection between populations living in different environments by scanning genome-wide patterns of genetic divergence, while evodevo focuses on candidate developmental regulatory genes and networks underlying phenotypic differences between species and higher taxa. We attempt to reconcile these alternative perspectives by studying the response of ant populations to past climate change on the Arizona Sky Islands, which are high elevation mountain ranges that represents a replicated natural experiment. We previously showed that adaptation to climatic changes in the Arizona Sky Islands in ant species Monomorium emersoni occurred through repeatable changes within the gene network underlying the development of dispersal alternative phenotypes: winged and wingless queens. Here, we uncover several loci under positive selection which associate with habitat temperature, a pattern consistent with a predictable and repeated increase in frequency following climatic changes on each of the Sky Islands. Furthermore, we show that gene flow between locations within a Sky Island is not a consequence of dispersal phenotypes and its gene network, but rather, is restricted by temperature adaptation along the ecological gradient. This suggests that the determination of winged and wingless queens may be developmentally plastic, and this plasticity and may facilitate jumps between adaptive peaks on the fitness landscape. This complex interplay promotes predictability on both genetic and phenotypic levels for multiple traits in the same species.

Evolutionary Biology↗
bioRxiv2016

Phylogenetic scale in ecology and evolution

AimMany important patterns and processes vary across the phylogeny and depend on phylogenetic scale. Yet, phylogenetic scale has never been formally conceptualized and its potential remains largely unexplored. Here, we formalize the concept of phylogenetic scale, review how phylogenetic scale has been considered across multiple fields, and provide practical guidelines for the use of phylogenetic scale to address a range of biological questions.\n\nMethodsWe summarize how phylogenetic scale has been treated in macroevolution, community ecology, biogeography, and macroecology, illustrating how it can inform, and possibly resolve, some of the longstanding controversies in these fields. To promote the concept empirically, we define phylogenetic grain and extent, scale-dependence, scaling, and the domains of phylogenetic scale. We illustrate how existing phylogenetic data and statistical tools can be employed to investigate the effects of scale on a variety of well-known patterns and processes, including diversification rates, community structure, niche conservatism, or species-abundance distributions.\n\nMain conclusionsExplicit consideration of phylogenetic scale can provide new and more complete insight into many longstanding questions across multiple fields (macroevolution, community ecology, biogeography, macroevolution). Building on the existing resources and isolated efforts across fields, future research centered on phylogenetic scale might enrich our understanding of the processes that together, but over different scales, shape the diversity of life.

Evolutionary Biology↗
bioRxiv2016

Social ecology of a woodland songbird community: from individual movements to the emergence of population social structure

Social structure can have profound evolutionary and ecological implications for animal populations. Structure can arise and be maintained via social preferences or be indirectly shaped by habitat structure. Understanding how social structure emerges is important for understanding the potential links between social structure and evolutionary and ecological processes. Here, we study a large community of wild birds fitted with uniquely-coded passive integrated transponder (PIT) tags and recorded on a grid of automated feeders fitted with radio frequency identification (RFID) antennae. We show that both large-scale and fine-scale network communities are consistent across years in this population, despite high generational turn-over. Studying the process that generates community structure, here the movement of individual birds across the woodland, suggests an important role of habitat geometry in shaping population-level social community structure. Our study highlights how relatively simple factors can produce apparent emergent social structure at the population scale, which has widespread implications for understanding eco-evolutionary dynamics.

animal behavior and cognition↗
bioRxiv2016

Buchnera has changed flatmate but the repeated replacement of co-obligate symbionts is not associated with the ecological expansions of their aphid hosts

Symbiotic associations with bacteria have facilitated important evolutionary transitions in insects and resulted in long-term obligate interactions. Recent evidence suggests that these associations are not always evolutionarily stable and that symbiont replacement and/or supplementation of an obligate symbiosis by an additional bacterium has occurred during the history of many insect groups. Yet, the factors favoring one symbiont over another in this evolutionary dynamic are not well understood; progress has been hindered by our incomplete understanding of the distribution of symbionts across phylogenetic and ecological contexts. While many aphids are engaged into an obligate symbiosis with a single Gammaproteobacterium, Buchnera aphidicola, in species of the Lachninae subfamily, this relationship has evolved into a \"menage a trois\", in which Buchnera is complemented by a cosymbiont, usually Serratia symbiotica. Using deep sequencing of 16S rRNA bacterial genes from 128 species of Cinara (the most diverse Lachninae genus), we reveal a highly dynamic dual symbiotic system in this aphid lineage. Most species host both Serratia and Buchnera but, in several clades, endosymbionts related to Sodalis, Erwinia or an unnamed member of the Enterobacteriaceae have replaced Serratia. Endosymbiont genome sequences from four aphid species+confirm that these coresident symbionts fulfill essential metabolic functions not ensured by Buchnera. We further demonstrate through comparative phylogenetic analyses that co-symbiont replacement is not associated with the adaptation of aphids to new ecological conditions. We propose that symbiont succession was driven by factors intrinsic to the phenomenon of endosymbiosis, such as rapid genome deterioration or competitive interactions between bacteria with similar metabolic capabilities.

evolutionary biology↗
bioRxiv2016

Maximizing ecological and evolutionary insight from bisulfite sequencing data sets

The role of DNA methylation in development, divergence, and the response to environmental stimuli is of substantial interest in ecology and evolutionary biology. Measuring genome-wide DNA methylation is increasingly feasible using sodium bisulfite sequencing. Here, we analyze simulated and published data sets to demonstrate how effect size, kinship/population structure, taxonomic differences, and cell type heterogeneity influence the power to detect differential methylation in bisulfite sequencing data sets. Our results reveal that the effect sizes typical of evolutionary and ecological studies are modest, and will thus require data sets larger than those currently in common use. Additionally, our findings emphasize that statistical approaches that ignore the properties of bisulfite sequencing data (e.g., its count-based nature) or key sources of variance in natural populations (e.g., population structure or cell type heterogeneity) often produce false negatives or false positives, thus leading to incorrect biological conclusions. Finally, we provide recommendations for handling common issues that arise in bisulfite sequencing analyses and a freely available R Shiny application for simulating and performing power analyses on bisulfite sequencing data. This app, available at www.tung-lab.org/protocols-and-software.html, allows users to explore the effects of sequencing depth, sample size, population structure, and expected effect size, tailored to their own system.

genomics↗
bioRxiv2016

Ecological speciation within the Phytophthora group

Over the past few years, symptoms akin to late blight disease have been reported on a variety of crop plants in South America. Despite the economic importance of these crops, the causal agents of the diseases belonging to the genus Phytophthora have not been completely characterized. In this study, we used an integrative approach that leveraged morphological, ecological, and genetic approaches to explore cryptic speciation within P. infestans sensu lato. We described a new Phytophthora species collected in Colombia from tree tomato (Solanum betaceum), a semi-domesticated fruit. All morphological traits and population genetic analyses, using microsatellite data and a reduced representation of single nucleotide polymorphism (SNP) data, support the description of the new species, Phytophthora betacei sp. nov. We have demonstrated that ecological differences are important in the persistence of P. infestans and P. betacei as genetically isolated units across an overlapping area in the northern Andes.

evolutionary biology↗