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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↗

bioRxiv2018

The interaction of phylogeny and community structure: linking clades’ ecological structures and trait evolution

1AimCommunity phylogenetic studies use information about species evolutionary relationships to understand the processes of community ecological assembly. A central premise of the field is that species evolution maps onto ecological patterns, and phylogeny reveals something more than species traits alone. We argue, therefore, that there is a need to better understand and model the interaction of phylogeny with species traits and community composition.\n\nInnovationWe outline a new method that identifies clades with unusual ecological structures, based around partitioning the variation of species site occupancies ({beta}-diversity). Eco-phylogenetic theory would predict that these clades should also demonstrate distinct evolutionary trajectories. We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between species ecological structure and evolutionary history.\n\nMain conclusionsUsing an empirical dataset of mammals from around the world, we identify two clades of rodents that tend not to co-occur (are phylogenetically overdispersed), and then find independent evidence of slower rates of body mass evolution in these clades. We suggest that our approach, which assumes nothing about the mode of species trait evolution but rather seeks to explain it using ecological information, presents a new way to examine eco-phylogenetic structure.

evolutionary biology↗

bioRxiv2019

A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns

Surveys of microbial biodiversity such as the Earth Microbiome Project (EMP) and the Human Microbiome Project (HMP) have revealed robust ecological patterns across different environments. A major goal in ecology is to leverage these patterns to identify the ecological processes shaping microbial ecosystems. One promising approach is to use minimal models that can relate mechanistic assumptions at the microbe scale to community-level patterns. Here, we demonstrate the utility of this approach by showing that the Microbial Consumer Resource Model (MiCRM) - a minimal model for microbial communities with resource competition, metabolic crossfeeding and stochastic colonization - can qualitatively reproduce patterns found in survey data including compositional gradients, dissimilarity/overlap correlations, richness/harshness correlations, and nestedness of community composition. By using the MiCRM to generate synthetic data with different environmental and taxonomical structure, we show that large scale patterns in the EMP can be reproduced by considering the energetic cost of surviving in harsh environments and HMP patterns may reflect the importance of environmental filtering in shaping competition. We also show that recently discovered dissimilarity-overlap correlations in the HMP likely arise from communities that share similar environments rather than reflecting universal dynamics. We identify ecologically meaningful changes in parameters that alter or destroy each one of these patterns, suggesting new mechanistic hypotheses for further investigation. These findings highlight the promise of minimal models for microbial ecology.

microbiology↗

bioRxiv2019

functionInk: An efficient method to detect functional groups in multidimensional networks reveals the hidden structure of ecological communities

O_LIComplex networks have been useful to link experimental data with mechanistic models, and have become widely used across many scientific disciplines. Recently, the increasing amount and complexity of data, particularly in biology, has prompted the development of multidimensional networks, where dimensions reflect the multiple qualitative properties of nodes, links, or both. As a consequence, traditional quantities computed in single dimensional networks should be adapted to incorporate this new information. A particularly important problem is the detection of communities, namely sets of nodes sharing certain properties, which reduces the complexity of the networks, hence facilitating its interpretation. C_LIO_LIIn this work, we propose an operative definition of "function" for the nodes in multidimensional networks, and we exploit this definition to show that it is possible to detect two types of communities: i) modules, which are communities more densely connected within their members than with nodes belonging to other communities, and ii) guilds, which are sets of nodes connected with the same neighbours, even if they are not connected themselves. We provide two quantities to optimally detect both types of communities, whose relative values reflect their importance in the network. C_LIO_LIThe flexibility of the method allowed us to analyze different ecological examples encompassing mutualistic, trophic and microbial networks. We showed that by considering both metrics we were able to obtain deeper ecological insights about how these different ecological communities were structured. The method mapped pools of species with properties that were known in advance, such as plants and pollinators. Other types of communities found, when contrasted with external data, turned out to be ecologically meaningful, allowing us to identify species with important functional roles or the influence of environmental variables. Furthermore, we found the method was sensitive to community-level topological properties like the nestedness. C_LIO_LIIn ecology there is often a need to identify groupings including trophic levels, guilds, functional groups, or ecotypes.The method is therefore important in providing an objective means of distinguishing modules and guilds. The method we developed, functionInk (functional linkage), is computationally efficient at handling large multidimensional networks since it does not require optimization procedures or tests of robustness. The method is available at: HTTPS://GITHUB.COM/APASCUALGARCIA/FUNCTIONINK. C_LI

bioinformatics↗

bioRxiv2019

Teaching R in the undergraduate ecology classroom: approaches, lessons learned, and recommendations

Ecology requires training in data management and analysis. In this paper, we present data from the last 10 years demonstrating the increase in the use of R, an open-source programming environment, in ecology and its prevalence as a required skill in job descriptions. Because of its transparent and flexible nature, R is increasingly used for data management and analysis in the field of ecology. Consequently, job postings targeting candidates with a bachelors degree and a required knowledge of R have increased over the past ten years. We discuss our experiences teaching undergraduates R in two advanced ecology classes using different approaches. One approach, in a course with a field lab, focused on collecting, cleaning, and preparing data for analysis. The other approach, in a course without a field lab, focused on analyzing existing data sets and applying the results to content discussed in the lecture portion of the course. Our experiences determined that each approach had strengths and weaknesses. We recommend that above all, instructors of ecology and related subjects should be encouraged to include R in their coursework. Furthermore, instructors should be aware of the following: learning R is a separate skill from learning statistics; writing R assignments is a significant time sink for course preparation; and, there is a tradeoff between teaching R and teaching content. Determining how ones course fits into the curriculum and identifying resources outside of the classroom for students continued practice will ensure that R training is successful and will extend beyond a one-semester course.

scientific communication and education↗

bioRxiv2019

A data-based guide to the North American ecology faculty job market

Every year for three years (2016 to 2018), I tried to identify every single person hired as a tenure track prof in ecology or an allied field (e.g., fish & wildlife) in N. America. I identified a total of 566 hires. I used public sources to compile various data on the new hires and the institutions that hired them (e.g., number of publications, teaching experience, hiring institution Carnegie class). I also compiled data provided by anonymous ecology faculty job seekers on ecoevojobs.net (e.g., number of positions applied for, number of publications, numbers of interviews and offers). And I polled readers of the Dynamic Ecology blog to get information about applicant and search committee behavior (e.g., regarding customization of applications to the hiring institution). These data address some widespread anxieties and misunderstandings about the ecology faculty job market, and also speak to gender diversity and equity in recent ecology faculty hiring. They complement, and in some cases improve on, other sources of information, such as anecdotal personal experiences.

scientific communication and education↗

bioRxiv2019

Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database.

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalising syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterise taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth "CS") to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from the survey metadata. Specifically, we modelled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on putative landscape scale pH-abundance responses. We identify that most of the soil OTUs examined exhibit predictable abundance responses across soil pH gradients, though with the exception of known acidophilic lineages, the pH optima of OTU relative abundance was variable and could not be generalised by broad taxonomy. This highlights the need for tools and databases to predict ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER), and as a DADA2 database for use in bioinformatics pipelines. The further development of advanced informatics infrastructures incorporating modelled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

microbiology↗

bioRxiv2019

Allopatric speciation is more prevalent than parapatric ecological divergence in tropical montane systems (Asteraceae: Piofontia)

Elucidating how species accumulate in diversity hotspots is an ongoing debate in evolutionary biology. The paramo, in the Northern Andes, has remarkably high indices of plant diversity, endemicity, and diversification rates. A hypothesis for explaining such indices is that allopatric speciation is high in the paramo given its island-like distribution; an alternative hypothesis is that the altitudinal gradient of the Andean topography provides a variety of niches that drive vertical parapatric ecological speciation. A formal test for evaluating the relative roles of allopatric speciation and parapatric ecological divergence is lacking. The main aim of our study is to test which kind of speciation is more common in an endemic paramo genus. We developed a framework incorporating phylogenetics, species distributions, and a morpho-ecological trait (leaf area) to compare sister species and infer whether allopatric or parapatric ecological divergence caused their speciation. We applied our framework to the species-rich genus Linochilus (63 spp.) and found that the majority of recent speciation events in it (12, 80%) have been driven by allopatric speciation, while a smaller fraction (1, 6.6%) is attributed to parapatric ecological divergence; two pairs produced inconclusive results (13.3%). We conclude that paramo autochthonous diversification is primarily driven by allopatric speciation.

evolutionary biology↗

bioRxiv2026

Song evolution in light of ecosystem differences: exploring effects of urbanization and ecology on temporal and frequency traits of Spotted and Eastern towhee songs

The Eastern towhee (Pipilo erythrophthalmus) and Spotted towhee (Pipilo maculatus) are large New World sparrows found across North America. These two species were previously classified as a single species, the Rufous-sided towhee, which was separated in 1995 based on differences in plumage, geographic range, and song. Previous studies have shown that ecological factors, such as urbanization and climate, can affect learned vocalizations, particularly frequency-related song characteristics such as minimum frequency; however, most studies have been conducted on one species in a specific location. The extensive geographic distributions of these towhee species, along with their ample publicly available song-recording data, give us the unique opportunity to assess whether ecological pressures influence song variation. Here, we extract frequency-related song features from 2916 Spotted and Eastern towhee recordings and investigate whether geography and ecology--including recording location (latitude and longitude), tree cover, urbanization (nighttime lights, human population density, and distance to road), climate zones, elevation, and ecoregions--explain patterns in these song frequency variables of this sister-species pair. Our results show that geographic location, particularly longitude, contributes more strongly to the variation of song frequency features than urbanization, environment, and climate, suggesting that culturally transmitted differences in learned song, not ecology or anthropogenic structure, drive this variation. However, there was not a clear pattern of isolation-by-distance despite the geographic patterns that we found in their songs. Further, we did not find strong support for behavioral adaptation to habitat structure, but we did find a weak signal that urbanization was associated with increased frequency in Spotted towhees. Overall, we provide a detailed study on the interactions between ecology and song evolution, and provide new insight into the evolution of birdsong.

evolutionary biology↗

bioRxiv2026

HepI and OpsX are functionally coupled but evolutionarily asymmetric heptosyltransferase variants: ecological transitions and operon modularization drive divergent constraints and flexibility

Lipopolysaccharide (LPS) inner-core biosynthesis is classically initiated by a heptosyltransferase enzyme most commonly Heptosyltranferase I (HepI), a conserved WaaC-like enzyme. An alternative heptosyltranferase variant, OpsX, occurs alone in a subset of Gram-negative bacteria and co-exist with WaaC-like enzyme within the same genome of other organisms, raising questions about the origin of these two vairants and their functional partitioning. Here, we present a comparative evolutionary analysis of HepI (K02841) and OpsX (K12982) across Gram-negative bacteria to resolve their functional coupling and divergence. Selection analyses reveal a consistent evolutionary asymmetry, with OpsX exhibiting elevated {omega} values relative to HepI across global datasets and within genomes encoding both systems. Residue-level analyses indicate conserved catalytic cores in both enzymes, but a broader distribution of relaxed constraints in OpsX, suggesting differential partitioning of functional pressure. HepI has undergone intensified purifying selection in host-associated lineages, whereas OpsX shows no corresponding shift, indicating distinct responses to ecological context. Gene-species tree reconciliation further reveals contrasting horizontal gene transfer (HGT) architectures: HepI displays an ecologically structured network enriched in pathogen- and opportunist-associated lineages, with recurrent hub-mediated exchanges and deeper lineage-integrated events, whereas OpsX exhibits a diffuse transfer regime dominated by non-pathogenic taxa and primarily recent terminal acquisitions. These differences persist in genomes co-encoding both systems, where HepI transfer signal remains strongly associated with lifestyle, while OpsX is largely uncoupled from ecological structure. Analysis of operon architecture reveals pathway partitioning between the two genes: HepI is embedded in a conserved downstream operon linked to glycosyltransferase-mediated core assembly, whereas OpsX occurs in a more variable context enriched for upstream ADP-heptose precursor biosynthesis genes. In dual-system genomes, HepI is reduced to a minimal downstream module while OpsX retains upstream functions, indicating coordinated operon modularization. Together, HepI and OpsX form a functionally coupled but evolutionarily asymmetric system shaped by ecological transitions and genomic reorganization. HighlightsO_LIHepI and OpsX represent functionally coupled but evolutionarily asymmetric LPS inner-core biosynthesis systems across Gram-negative bacteria. C_LIO_LIOpsX shows relaxed selective constraint and a diffuse horizontal gene transfer pattern, whereas HepI is under stronger purifying selection and ecologically structured transfer. C_LIO_LIOperon organization reveals pathway modularization, with HepI embedded in conserved downstream assembly modules and OpsX retaining upstream precursor-associated flexibility. C_LI

evolutionary biology↗

bioRxiv2026

Ecological inference and contaminant detection from fungal microbiome data with q2-fungal-traits

Fungi are key members of microbial communities, yet microbiome surveys often lack trait-based information required for ecologically meaningful interpretation of mycobiome data. To demonstrate the value of fungal trait-based phenotyping in microbiome research, we re-analyzed N=3,221 samples across four case studies spanning human, agricultural, and environmental systems. In human cancer and vineyard datasets, trait-based analysis detected fungi producing macroscopic fruiting bodies, likely introduced via airborne spore dispersal, indicating widespread contributions of transient or contaminant fungi that can confound interpretation of sequencing data from tumor biopsies and grape berries. In sourdough fermentations, filamentous fungi were highly abundant alongside traditionally-recognized yeast and occupied distinct ecological niches. In forest soils, increasing habitat disturbance was associated with increased prevalence and abundance of plant pathogens, and a marked decline in ectomycorrhizal and lichenized fungi. These changes were accompanied by a shift toward large-spored taxa in urban soils, consistent with enhanced stress tolerance. To facilitate broader adoption of fungal phenotyping in microbiome studies, we introduce q2-fungal-traits, a QIIME2 plugin for automated integration of fungal taxonomy derived from marker-gene or shotgun metagenome sequencing surveys with ecological and functional trait data. The plugin assigns lifestyle-related traits and spore size estimates through hierarchical taxonomic matching and integrates directly into standard microbiome workflows. Our case studies demonstrate that integrating trait-based ecology with mycobiota datasets can generate novel findings and testable hypotheses, enabling inference of the functional (ir)relevance of community constituents. Our work contributes to bridging the gap between descriptive community profiling and functional ecology in microbiome research.

microbiology↗

bioRxiv2026

Genetic Diversity and Antifungal Susceptibility among Ecological Niche Populations of Aspergillus flavus in Yaounde, Cameroon

Aspergillus flavus is a ubiquitous fungus commonly found in a variety of ecological niches including soil, crops, the air, and humans. In humans, it is the second leading cause of invasive aspergillosis (IA) and is linked to other illnesses through contamination of agricultural products with aflatoxins. As such, A. flavus threatens food safety, economic wellbeing, and human health, particularly in less developed regions and countries such as Cameroon. To mitigate these effects, farmers and clinicians rely on triazoles to reduce aflatoxin contamination and treat IA. However, a consequence of increasing triazole use is the emergence and spread of triazole resistance in both agriculture and clinics. To identify the prevalence of triazole resistance and the potential relationships among triazole resistant and susceptible strains, this study investigated antifungal susceptibility to both clinical and agricultural triazoles and analyzed genetic variation among various ecological niche populations of A. flavus in Cameroon. Strain genotypes were obtained through analysis of six polymorphic microsatellite markers. Our analyses revealed that 30.3% (17/56) of the strains were resistant to at least one of the four triazoles, with increased minimum inhibitory concentrations found among crop-isolated strains. A Permutational Multivariate Analysis of Variance suggested limited ecological niche-based clustering of genotypes, consistent with frequent gene flow and dispersal among ecological niches. A multilocus linkage disequilibrium analysis revealed evidence of non-random recombination in A. flavus. Overall, this study elucidates the interplay between ecological pressures, antifungal resistance, and genetic differentiation, and invites alternative methods to control aflatoxin contamination in foods without the use of agricultural fungicides.

microbiology↗

bioRxiv2026

reserBUGS: A reservoir computing framework for probabilistic forecasting of ecological abundance time series

Forecasting species population dynamics is a central challenge in computational ecology, yet existing approaches rarely combine flexible nonlinear modelling, support for count-based ecological data, and systematic uncertainty quantification within a single, scalable framework. Here we introduce reserBUGS, an open-source Python framework for ecological forecasting based on reservoir computing, a recurrent neural network architecture in which only a simple readout layer is trained while a fixed high-dimensional dynamical system encodes temporal memory and nonlinear dependencies. reserBUGS integrates species abundance time series with environmental covariates retrieved automatically from global climate products, generates probabilistic ensemble forecasts, and provides tools for forecast evaluation and reliability assessment. We evaluated reserBUGS using insect abundance time series from available biodiversity monitoring datasets, comparing its performance against seven statistical and machine-learning baselines over one- to five-year forecast horizons. Reservoir-based models consistently outperformed alternatives in both predicting future abundance and capturing forecast uncertainty, with environmental predictors increasing the proportion of stable forecasts and contributing additional predictive value beyond historical abundance dynamics alone, particularly at 3-4-year forecast horizons. Probabilistic forecasts further enabled the identification of conditions associated with reduced predictive skill, providing a practical basis for communicating forecast confidence to end users. While default configurations already achieved competitive performance across a taxonomically and geographically diverse set of time series, hyperparameter optimisation revealed substantial room for performance gains through series-specific tuning. reserBUGS offers a computationally efficient and extensible framework for ecological forecasting that is well suited to the short, heterogeneous time series typical of biodiversity monitoring programmes. Its combination of flexible nonlinear modelling, probabilistic uncertainty quantification, and automated environmental data integration addresses key practical barriers to the adoption of modern forecasting methods in conservation and ecological research.

bioinformatics↗

bioRxiv2014

Early warning signs in social-ecological networks

A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance - measured, for example, by the maximum element of the covariance matrix of the network - is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.

Ecology↗

bioRxiv2016

EVIDENCE FOR ALTERNATE STABLE STATES IN COLLAPSING ECOLOGICAL NETWORKS

BackgroundThere has been considerable interest and progress in our perception of organized complexity in recent years. Recurrent debates on the dynamics and stability of complex systems have enriched our understanding of these systems, but generalities in the relationship between structure and dynamics are hard to come by. Although traditionally an arena for theoreticians, much of this research has been invigorated by demonstration of the existence of alternate stable equilibria in real world ecosystems such as lakes, coral reefs, forests and grasslands.\n\nResultsLinking up systems thinking with recent advances in our understanding of ecological networks opens up exciting possibilities. In an attempt to obtain general patterns of behaviour of complex systems, we have analyzed the response of eighty-six real world ecological networks to targeted extinctions, and the findings suggest that most networks are robust to loss of specialists until specific thresholds are reached in terms of geodesics. If the extinctions persist, a state change or flip occurs and the structural properties are altered drastically, although the network does not collapse. Further, we find that as opposed to simpler networks, larger networks have several such alternate states that ensure their long-term persistence and that indeed complexity does endow resilience to such networks.\n\nConclusionsThis is the first report of critical transitions in ecological networks and the implications of these findings for complex systems characterized by networks are likely to be profound with immediate significance in conservation biology, invasion biology and restoration ecology.

Ecology↗

bioRxiv2016

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but also by the way these interactions vary across time and space. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time i.e. a novel approach to interaction distribution modelling. We apply this framework to host parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modelling and community ecology.

Ecology↗