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Results for “Ecology”

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At least 37 records · Page 2Linked to original sources

bioRxiv2015

Radiating despite a lack of character: closely related, morphologically similar, co-occurring honeyeaters have diverged ecologically

The 75 species of Australian honeyeaters (Meliphagidae) are morphologically and ecologically diverse, with species feeding on nectar, insects, fruit, and other resources. We investigated ecomorphology and community structure of honeyeaters across Australia. First, we asked to what degree morphology and ecology (foraging behavior) are concordant. Second, we estimated rates of trait evolution. Third, we compared phylogenetic and trait community structure across the broad environmental gradients of continental Australia. We found that morphology explained 37% of the variance in ecology (and 62% vice versa), and that recovered multivariate ecomorphological relationships incorporated well-known bivariate relationships. Clades of large-bodied species exhibited elevated rates of morphological trait evolution, while members of Melithreptus showed slightly faster rates of ecological trait evolution. Finally, ecological trait diversity did not decline in parallel with phylogenetic diversity along a gradient of decreasing precipitation. We employ a new method (trait fields) and extend another (phylogenetic fields) to show that while species from phylogenetically clustered assemblages co-occur with morphologically similar species, these species are as varied in foraging behavior as those from more diverse assemblages. Thus, although closely related, these arid-adapted species have diverged in ecological space to a similar degree as their mesic counterparts, perhaps mediated by competition.

Ecology↗

bioRxiv2017

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward

Growing evidence makes a strong case that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology from dissecting developmental processes to understanding aspects of human health and disease. In ecology, recent studies have merged ecological experimental design with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress response, adaptation to habitat, or species range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, many studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which allow for a more mechanistic understanding but have limited ecological realism. To understand the true significance of epigenetics for plant ecology and evolution, we must combine both approaches transferring knowledge and methods from model-species research to genomes of evolutionarily divergent species, and examining responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration between molecular epigeneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

ecology↗

bioRxiv2018

Species-habitat networks: Bridging applied ecology and network theory

Land-use change is massively reshaping terrestrial ecosystems worldwide, and is recognized as a key driver of biodiversity loss with negative consequences on ecosystem functioning. Understanding how species use resources across landscapes is essential for the design of effective management strategies. Despite recent advances in theoretical ecology, there is still a gap between theory and applied ecological science and we lack the tools to manage entire landscapes to maximize biodiversity conservation and ecosystem service delivery. Here, we propose a new approach that uses existing bipartite networks to create species-habitat networks. Networks enable powerful visualizations via a common language that defines most processes in terms of nodes and links. This approach explicitly links multiple species and habitat resources, provides tools to estimate the importance of particular species in a given landscape, and quantifies emerging properties of entire habitat networks. Most existing metrics used to study properties of bipartite ecological networks can easily be adapted to investigate species-habitat relationships. One key advantage of this approach is that the scale of the derived ecological information will match the scale of management interventions. The flexibility of the proposed approach is that it can be easily applied across a range of ecological fields such as species conservation, habitat restoration, ecosystem services management, or invasion ecology. Network emerging properties could also be used to test the effects of large scale drivers of global change upon ecosystem structure and stability.

ecology↗

bioRxiv2018

Applications for deep learning in ecology

A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.

ecology↗

bioRxiv2015

Quantitative Genetics Meets Integral Projection Models: Unification of Widely Used Methods from Ecology and Evolution

O_LIMicro-evolutionary predictions are complicated by ecological feedbacks like density dependence, while ecological predictions can be complicated by evolutionary change. A widely used approach in micro-evolution, quantitative genetics, struggles to incorporate ecological processes into predictive models, while structured population modelling, a tool widely used in ecology, rarely incorporates evolution explicitly.\nC_LIO_LIIn this paper we develop a flexible, general framework that links quantitative genetics and structured population models. We use the quantitative genetic approach to write down the phenotype as an additive map. We then construct integral projection models for each component of the phenotype. The dynamics of the distribution of the phenotype are generated by combining distributions of each of its components. Population projection models can be formulated on per generation or on shorter time steps.\nC_LIO_LIWe introduce the framework before developing example models with parameters chosen to exhibit specific dynamics. These models reveal (i) how evolution of a phenotype can cause populations to move from one dynamical regime to another (e.g. from stationarity to cycles), (ii) how additive genetic variances and covariances (the G matrix) are expected to evolve over multiple generations, (iii) how changing heritability with age can maintain additive genetic variation in the face of selection and (iii) life history, population dynamics, phenotypic characters and parameters in ecological models will change as adaptation occurs.\nC_LIO_LIOur approach unifies population ecology and evolutionary biology providing a framework allowing a very wide range of questions to be addressed. The next step is to apply the approach to a variety of laboratory and field systems. Once this is done we will have a much deeper understanding of eco-evolutionary dynamics and feedbacks.\nC_LI

Evolutionary Biology↗

bioRxiv2016

Ecological Representations

Mainstream cognitive science and neuroscience both rely heavily on the notion of representation in order to explain the full range of our behavioral repertoire. The relevant feature of representation is its ability to designate (stand in for) spatially or temporally distant properties, When we organize our behavior with respect to mental or neural representations, we are (in principle) organizing our behavior with respect to the property it designates. While representational theories are a potentially a powerful foundation for a good cognitive theory, problems such as grounding and system-detectable error remain unsolved. For these and other reasons, ecological explanations reject the need for representations and do not treat the nervous system as doing any mediating work. However, this has left us without a straight-forward vocabulary to engage with so-called representation-hungry problems or the role of the nervous system in cognition. In an effort to develop such a vocabulary, here we show that James J Gibsons ecological information functions to designate the ecologically-scaled dynamical world to an organism. We then show that this designation analysis of information leads to an ecological conceptualization of the neural activity caused by information, which in turn we argue can together support intentional behavior with respect to spatially and temporally distal properties. Problems such as grounding and error detection are solved via law-based specification. This analysis extends the ecological framework into the realm of representation-hungry problems, making it as powerful a potential basis for theories of behavior as traditional cognitive approaches. The resulting analysis does, according to some definitions, allow information and the neural activity to be conceptualized as representations; however, the key work is done by information and the analysis remains true to Gibsons ecological ontology.

Animal Behavior and Cognition↗

bioRxiv2018

Sexual isolation with and without ecological isolation between sympatric isopods Jaera albifrons and J. praehirsuta

Sexual isolation is often coupled with other barriers, and sexual isolation mechanisms can be intrinsically tied to ecological conditions or natural selection against heterospecific gene flow. The independence and relative strength of sexual isolation are thus difficult to assess. Here we take advantage of a pair of isopod species (Jaera albifrons and J. praehirsuta) that show sexual isolation and coexist in populations where they share the same habitat or not (i.e. without or with ecological isolation). Using no-choice trials and a free-choice experimental population, we estimated the strength of sexual isolation between J. albifrons and J. praehirsuta individuals originating from these different ecological contexts. We found that sexual isolation is strong in presence and absence of ecological isolation, but that it is asymmetric and fails to prevent gene flow entirely. Post-zygotic barriers were low, and there was no sexual isolation within J. praehirsuta across habitats. The J. albifrons / J. praehirsuta species pair thus provides an example where the role of sexual isolation as a barrier to gene flow i) does not depend upon current ecological isolation, ii) seems to have evolved independently of local ecological conditions and hybridization costs, but iii) is unable to complete speciation entirely on its own.

evolutionary biology↗

bioRxiv2018

Habitat choice stabilizes metapopulation dynamics through increased ecological specialisation.

Dispersal is a key trait responsible for the spread of individuals and genes among local populations, thereby generating eco-evolutionary interactions. Especially in heterogeneous metapopulations, a tight coupling between dispersal, population dynamics and the evolution of local adaptation is expected. In this respect, current theory predicts dispersal to counteract ecological specialisation by redistributing locally selected phenotypes (i.e. migration load). However, in nature we observe that some specialists exhibit a strong dispersal capacity.\n\nHabitat choice following informed dispersal decisions, provides a possible mechanism for individuals to match the environment to their phenotype, thereby enabling the persistence of evolved ecological specialisation. How such informed decisions affect the evolution of dispersal and ecological specialisation and how these, in turn, influence metapopulation dynamics is yet to be determined.\n\nBy means of individual-based modelling, we show that informed decisions on both departure and settlement decouples the evolution of dispersal and generalism, favouring highly dispersive specialists. Choice at settlement decouples dispersal from ecological specialisation most effectively. Additionally, habitat choice stabilizes local and metapopulation demography because of the maintenance of ecological specialisation at all levels of dispersal propensity.\n\nWe advocate considering habitat choice in spatially structured ecological models to improve demographic predictions in the face of environmental change.

evolutionary biology↗

bioRxiv2015

Elevating the status of code in ecology

Introduction Introduction The role of code... Current standards for code... Promoting code in ecology... References Code is increasingly central to research in ecology. From running statistical analyses for field and lab projects to developing software intended to be used broadly for modeling and data analysis, most ecologists now commonly write code as part of their research. It is important that the communication of research results addresses the fact that code is now a key component of most ecological studies.\n\nThe role of code in ecological research Introduction The role of code... Current standards for code... Promoting code in ecology... References The transition to a greate ...

Ecology↗

bioRxiv2016

How ecological networks evolve

Ecological networks represent the backbone of biodiversity. As species diversify over macro-evolutionary time-scales, the structure of these networks changes; this happens because species are gained and lost, and therefore add or remove interactions in their communities. The mechanisms underlying such dynamic changes in ecological network structure, however, remain poorly understood. Here we show that several types of ecological interactions share common evolutionary mechanisms that can be parametrised based on extant interaction data. In particular, we found that a model mimicking birth-death processes for species interactions describes the structure of extant networks remarkably well. Moreover, the various types of ecological interactions we considered--seed dispersal, herbivory, parasitism, bacteriophagy, and pollination--only differed in the position they occupy in the parameters multi-dimensional space. Notably, we found no clustering of parameters values between antagonistic and mutualistic interactions. Our results provide a common modelling framework for the evolution of ecological networks that we anticipate will contribute to the greater consideration of the explicit role played by species interactions in models of macro-evolution and adaptive radiations.

Ecology↗

bioRxiv2017

Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States, a region of high model conflict. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the unacceptable and often undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

ecology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗

bioRxiv2017

Extinction models of robustness for weighted ecological networks

O_LIAnalysis of ecological networks is a valuable approach to understanding the vulnerability of systems to environmental change. The tolerance of ecological networks to co-extinctions, resulting from sequences of primary extinctions, is a widely-used tool for modelling network robustness. Previously, these extinction models have been developed for and applied mostly to binary networks and recently used to predict cascades of co-extinctions in plant-pollinator networks. There is a need for robustness models that can make the most of the weighted data available and most importantly there is a need to understand how the structure of a network affects its robustness.\nC_LIO_LIHere, we developed a framework of extinction models for bipartite ecological networks (specifically plant-pollinator networks). In previous models co-extinctions occurred when nodes lost all their links, but by relaxing this rule (according to a set threshold) our models can be applied to binary and weighted networks, and can permit structurally correlated extinctions, i.e. the potential for avalanches of extinctions. We tested how the average and the range of robustness values is impacted by network structure and the impact of structurally-correlated extinctions sampling non-uniformly from the distribution of random extinction sequences.\nC_LIO_LIWe found that the way that structurally-correlated extinctions are modelled impacts the results; our two ecologically-plausible models produce opposing effects which shows the importance of understanding the model. We found that when applying the models to networks with weighted interactions, the effects are amplified and the variation in robustness increases. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity (i.e. the skewness of the degree distribution) of nodes (specifically, plant nodes) in the network.\nC_LIO_LIOur new framework of models enables us to calculate robustness with weighted, as well as binary, bipartite networks, and to make direct comparisons between models and between networks. This allows us to differentiate effects of the model and of the data (network structure) which is vital for those making ecological inferences from robustness models. The models can be applied to mutualistic and antagonistic networks, and can be extended to food webs.\nC_LI

ecology↗

bioRxiv2018

Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect

O_LIGlobally, protected areas are being established to protect biodiversity and to promote ecosystem resilience. The typical spatial conservation planning process leading to the creation of these protected areas focuses on representation and replication of ecological features, often using decision support systems such as Marxan. Unfortunately, Marxan currently requires manual input or specialised scripts to explicitly consider ecological connectivity, a property critical to metapopulation persistence and resilience.\nC_LIO_LI\"Marxan Connect\" is a new open source, open access Graphical User Interface (GUI) designed to assist conservation planners in the systematic operationalization of ecological connectivity in protected area network planning.\nC_LIO_LIMarxan Connect is able to incorporate estimates of demographic connectivity (e.g. derived from tracking data, dispersal models, or genetics) or structural landscape connectivity (e.g. isolation by resistance). This is accomplished by calculating metapopulation-relevant connectivity metrics (e.g. eigenvector centrality) and treating those as conservation features, or using the connectivity data as a spatial dependency amongst sites to be included in the prioritization process.\nC_LIO_LIMarxan Connect allows a wide group of users to incorporate directional ecological connectivity into conservation plans. The least-cost conservation solutions provided by Marxan Connect, combined with ecologically relevant post-hoc testing, are more likely to support persistent and resilient metapopulations (e.g. fish stocks) and provide better protection for biodiversity than if connectivity is ignored.\nC_LI

ecology↗

bioRxiv2017

Ecological suicide in microbes

The growth and survival of organisms often depend on interactions between them. In many cases, these interactions are positive and caused by a cooperative modification of the environment, such as the cooperative breakdown of complex nutrients in microbes [1]-[3] or the construction of elaborate architectures in social insects [4]. However, organisms can similarly display negative interactions by changing the environment in ways that are detrimental for them, eg by resource depletion or the production of toxic byproducts [5]. Here we find an extreme type of negative interactions, in which bacteria modify the environmental pH to such a degree that it leads to a rapid extinction of the whole population, a phenomenon we call ecological suicide. Modification of the pH is more pronounced at higher population densities, and thus ecological suicide is more likely with increasing bacterial density. Correspondingly, promoting bacterial growth can drive populations extinct whereas inhibiting bacterial growth by the addition of harmful substances - like antibiotics - can rescue them. Moreover, ecological suicide can cause oscillatory dynamics, even in single-species populations. We find ecological suicide in a wide variety of microbes, suggesting that it could play a significant role in microbial ecology and evolution.

systems biology↗

bioRxiv2018

Evolution of an intratumoral ecology susceptible to successive treatment in breast cancer xenografts

The processes by which tumors evolve are essential to the efficacy of treatment, but quantitative understanding of intratumoral dynamics has been limited. Although intratumoral heterogeneity is common, quantification of evolution is difficult from clinical samples because treatment replicates cannot be performed and because matched serial samples are infrequently available. To circumvent these problems we derived and assayed large sets of human triple-negative breast cancer xenografts and cell cultures from two patients, including 86 xenografts from cyclophosphamide, doxorubicin, cisplatin, docetaxel, or vehicle treatment cohorts as well as 45 related cell cultures. We assayed these samples via exome-seq and/or high-resolution droplet digital PCR, allowing us to distinguish complex therapy-induced selection and drift processes among endogenous cancer subclones with cellularity uncertainty <3%. For one patient, we discovered two predominant subclones that were granularly intermixed in all 48 co-derived xenograft samples. These two subclones exhibited differential chemotherapy sensitivity -- when xenografts were treated with cisplatin for 3 weeks, the post-treatment volume change was proportional to the post-treatment ratio of subclones on a xenograft-to-xenograft basis. A subsequent cohort in which xenografts were treated with cisplatin, allowed a drug holiday, then treated a second time continued to exhibit this proportionality. In contrast, xenografts from other treatment cohorts, spatially dissected xenograft fragments, and cell cultures evolved unsystematically but with substantial population bottlenecks. These results show that ecologies susceptible to successive retreatment can arise spontaneously in breast cancer in spite of a background of irregular subclonal bottlenecks, and our work provides to our knowledge the first quantification of the population genetics of such a system. Intriguingly, in such an ecology the ratio of common subclones is predictive of the state of treatment susceptibility, suggesting that this ratio can be measured to optimize dynamic treatment protocols in patients.\n\nAUTHOR SUMMARYAn overarching challenge of cancer is that patients develop resistance to treatment -- an essentially evolutionary process. However, there is currently very little understanding of how tumor evolution can be exploited to improve treatment. One reason for this is that usually only 1-2 samples can be obtained per patient, so cancer evolutionary processes are still poorly understood. To solve this problem, we created many dozens of copies of the tumors from two breast cancer patients using xenografting and cell culture methods. We then compared the evolution in these tumor copies in response to different treatments, including four of the most common breast cancer chemotherapies. These studies present the most exhaustive comparisons of treatment-induced evolution that have yet been performed for individual cancer patients. Unexpectedly, high-resolution sequencing of these samples revealed a special dynamically treatable ecology in one tumor, in which tumor growth during platinum therapy was determined by the ecological balance of two tumor cell populations. Our work shows that ecologies that can be targeted by dynamic treatment strategies arise spontaneously in breast cancers. Population heterogeneity is common within cancers, and our work suggests how tracking of intratumoral evolution can be used to optimize treatment.

cancer biology↗

bioRxiv2018

Ecological feedback on diffusion dynamics

Spatial patterns are ubiquitous across different scales of organization. Animal coat pattern, spatial organization of insect colonies, and vegetation in arid areas are prominent examples from such diverse ecologies. Typically, pattern formation has been described by reaction-diffusion equations, which considers individuals dispersing between sub-populations of a global pool. This framework applied to public goods game nicely showed the endurance of populations via diffusion and generation of spatial patterns. However, how the spatial characteristics, such as diffusion, are related to the eco-evolutionary process as well as the nature of the feedback from evolution to ecology and vice versa, has been so far neglected. We present a thorough analysis of the ecologically driven evolutionary dynamics in a spatially extended version of ecological public goods games. We show how these evolutionary dynamics feedback into shaping the ecology thus together determining the fate of the system.

evolutionary biology↗

bioRxiv2018

Nonlinear functional responses and ecological pleiotropy alter the strength of disruptive selection in consumers

Much of the theory on disruptive selection has focused on selection in generalist consumers caused by ecological opportunity through the availability of alternative resources and intraspecific competition for those resources. This theory, however, makes several ecologically unrealistic assumptions. First, this theory assumes that consumers have a linear, resource-dependent functional response, ignoring well-documented effects of resource handling times and consumer dependence. Second, this theory assumes that the trait under selection only influences the percapita attack rates of the consumer, ignoring other effects of the trait that may influence feeding rates and hence fitness. Here, I develop a one consumer-two resource model to investigate how nonlinear functional responses and ecological pleiotropy (traits that have multiple simultaneous ecological effects) influence the strength of disruptive selection. I find that handling times and interference among consumers are capable of altering disruptive selection by changing feeding rates differentially across consumer phenotypes. In particular, handling times cause a decrease in the strength of disruptive selection while the effects of interference depend on the mechanism through which interference occurs. The effects of handling times and interference, however, are dependent on whether and how ecological pleiotropy causes correlations between handling times or interference rates and attack rates. Overall, my results suggest that features defining the functional responses of consumers and the relationships among those features determine the likelihood and strength of disruptive selection. In particular, disruptive selection should be strongest in generalist populations composed of individual diet specialists who experience lower handling times and interference rates on the resources for which their attack rates are highest.

evolutionary biology↗