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bioRxiv2017

Fighting over food unites the birds of North America in a continental dominance hierarchy

The study of aggressive interactions between species has, to date, usually been restricted to interactions among small numbers of ecologically close competitors. Nothing is known about interspecific dominance hierarchies that include numerous, ecologically varied species. Such hierarchies are of interest because they could be used to address a variety of research questions, e.g. do similarly ranked species tend to avoid each other in time or space, and what will happen when such species come into contact as climates change? Here, we propose a method for creating a continental-scale hierarchy, and we make initial analyses based on this hierarchy. We quantified the extent to which a dominance hierarchy of feeder birds was linear, as intransitivities can promote local species coexistence. Using the existing network of citizen scientists participating in Project FeederWatch, we collected the data with which to create a continent-spanning interspecific dominance hierarchy that included species that do not currently have overlapping geographic distributions. Overall, the hierarchy was nearly linear, and largely predicted by body mass, although there were clade-specific deviations from the average mass-dominance relationship. Most of the small number of intransitive relationships in the hierarchy were based on small samples of observations. Few observations were made of interactions between close relatives and ecological competitors like Melanerpes woodpeckers and chickadees, as such species often have only marginally overlapping geographic distributions. Yet, these species ranks--emergent properties of the interaction network--were usually in agreement with published literature on dominance relationships between them. Interspecific dominance hierarchy, aggression, displacement, citizen science\n\nLAY SUMMARYWhen it comes to fighting over food, bigger is better but woodpeckers are best. The outcome of aggressive encounters between birds frequently determines which individual gains access to contested resources like food, but until now, little was known about such encounters between individuals of different species. We partnered with citizen scientists to record interspecific behavioral interactions at bird feeders around North America, and assembled these interactions into a continental dominance hierarchy.

ecology↗

bioRxiv2017

Nests of red wood ants (Formica rufa-group) are positively associated with tectonic faults: a double-blind test

Ecological studies aim to better understand the distribution and abundances of organisms. Yet ecological works often are subjected to unintentional biases thus an improved framework for hypothesis testing should be used. Double-blind ecological studies are rare but necessary to minimize sampling biases and omission errors and improve the reliability of research. We used a double-blind design to evaluate associations between nests of red wood ants (Formica rufa, RWA) and the distribution of tectonic faults. We randomly sampled two regions in western Denmark to map the spatial distribution of RWA nests. We then calculated nest proximity to the nearest active tectonic faults. Red wood ant nests were eight times more likely to be found within 60 meters of known tectonic faults than were random points in the same region but without nests. This pattern paralleled the directionality of the fault system, with NNE-SSW faults having the strongest associations with RWA nests. The nest locations were collected without knowledge of the spatial distribution of active faults thus we are confident that the results are neither biased nor artefactual. This example highlights the benefits of double-blind designs in reducing sampling biases, testing controversial hypotheses, and increasing the reliability of the conclusions of research.

ecology↗

bioRxiv2017

Spatially Explicit Approach To Population Abundance Estimation In Field Surveys

The abundance of species is a fundamental consideration in ecology and conservation biology. Although broad models have been proposed to estimate the population abundance using existing data, available data is often limited. With no information available, a population estimation will rely on time consuming field surveys. Typically, time is a critical constraint in conservation and often management decisions must be made quickly under the data limited situation. Depending on time and budgetary constraints, the required accuracy of field survey changes significantly. Hence, it is desirable to set up an effective survey design to minimize time and effort of sampling given required accuracy. We examine a spatially-explicit approach to population estimation using spatial point processes, enabling us to explicitly and consistently discuss various sampling designs. We find that the accuracy of abundance estimation varies with both ecological factors and survey design. Although the spatial scale of sampling does not affect estimation accuracy when the underlying individual distribution is random, it decreases with the sampled unit size if individuals tend to form clusters. These results are derived analytically and checked numerically. Obtained insights provide a benchmark to predict the quality of population estimation, and improve survey designs for ecological studies and conservation.

ecology↗

bioRxiv2017

Modeling Zero-Inflated Count Data With glmmTMB

Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-resistant hosts.\n\nWe present a new R package, glmmTMB, that increases the range of models that can easily be fitted to count data using maximum likelihood estimation. The interface was developed to be familiar to users of the lme4 R package, a common tool for fitting GLMMs. To maximize speed and flexibility, estimation is done using Template Model Builder (TMB), utilizing automatic differentiation to estimate model gradients and the Laplace approximation for handling random effects. We demonstrate glmmTMB and compare it to other available methods using two ecological case studies.\n\nIn general, glmmTMB is more flexible than other packages available for estimating zero-inflated models via maximum likelihood estimation and is faster than packages that use Markov chain Monte Carlo sampling for estimation; it is also more flexible for zero-inflated modelling than INLA, but speed comparisons vary with model and data structure. Our package can be used to fit GLMs and GLMMs with or without zero-inflation as well as hurdle models. By allowing ecologists to quickly estimate a wide variety of models using a single package, glmmTMB makes it easier to find appropriate models and test hypotheses to describe ecological processes.

ecology↗

bioRxiv2017

Empirical evidence reveals the phase diagram of vegetation patterns in Mediterranean drylands

Drylands are ecosystems with limited water resources, often subjected to desertification. Conservation and restoration efforts towards these ecosystems depend on the interplay between ecological functioning and spatial patterns formed by local vegetation. Despite recent advances on the subject, an adequate description of phase transitions between the various vegetated phases remains an open issue. Here, we gather vegetation data of drylands from Southern Spain using satellite images. Our findings support three vegetated phases, separated by two distinct phase transitions, including a continuous phase transition, with new relations between scaling exponents of ecological variables. The phase diagram is obtained without a priori assumption about underlying ecological dynamics. We apply our analysis to a different dryland system in the Western United States and verify a compatible critical behavior, in agreement with the universality hypothesis.

ecology↗

bioRxiv2017

Stochastic microbial community assembly decreases biogeochemical function

Ecological mechanisms influence relationships among microbial communities, which in turn impact biogeochemistry. In particular, microbial communities are assembled by deterministic (e.g., selection) and stochastic (e.g., dispersal) processes, and the relative influence of these two process types is hypothesized to alter the influence of microbial communities over biogeochemical function, which we define generically to represent any biogeochemical reaction of interest. We used an ecological simulation model to evaluate this hypothesis. We assembled receiving communities under different levels of dispersal from a source community that was assembled purely by deterministic selection. The dispersal scenarios ranged from no dispersal (i.e., selection-only) to dispersal rates high enough to overwhelm selection (i.e., homogenizing dispersal). We used an aggregate measure of community fitness to infer its biogeochemical function relative to other communities. We also used ecological null models to further link the relative influence of deterministic assembly to function. We found that increasing rates of dispersal decrease biogeochemical function by increasing the proportion of maladapted taxa in a local community. Niche breadth was also a key determinant of biogeochemical function, suggesting a tradeoff between the function of generalist and specialist species. Together, our results highlight the influence of spatial processes on biogeochemical function and indicate the need to account for such effects in models that aim to predict biogeochemical function under future environmental scenarios.

ecology↗

bioRxiv2017

Invasion triple trouble: Environmental fluctuations, fluctuation-adapted invaders and fluctuation-mal-adapted communities all govern invasion success

It has been suggested that climate change will lead to increased environmental fluctuations, which will undoubtedly have evolutionary consequences for all biota. For instance, fluctuations can directly increase the risk of invasions of alien species into new areas, as these species have repeatedly been proposed to benefit from disturbances. At the same time increased environmental fluctuations may also select for better invaders. However, selection by fluctuations may also influence the resistance of communities to invasions, which has rarely been tested. We tested eco-evolutionary dynamics of invasion with bacterial clones, evolved either in constant or fluctuating temperatures, and conducted experimental invasions in both conditions. We found clear evidence that ecological fluctuations, as well as adaptation to fluctuations by both the invader and community, all affected invasions, but played different roles at different stages of invasion. Ecological fluctuations clearly promoted invasions, especially into fluctuation mal-adapted communities. The evolutionary background of the invader clearly played a smaller role, and only at the later stages of invasion in the fluctuating environments. Our results indicate that climate change associated disturbances can directly increase the risk of invasions by altering ecological conditions during invasions, as well as via the evolution of both the invader and communities. Our experiment provides novel information on the complex consequences of climate change on invasions in general, and also charts risk factors associated with the spread of environmentally growing opportunistic pathogens.\n\nData AccessibilityUpon acceptance the data will be made publicly available.\n\nStatement of authorshipAuthors planned the questions and experiment together. KS conducted the laboratory work and wrote the first draft of the manuscript. TK conducted statistical analyses. All authors wrote the final version of the manuscript.

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bioRxiv2017

Biological communities as interacting compartments: thermodynamic properties and diversity indices

Diversity indices provide simple and powerful metrics for assessing biological communities. Based on entropy measures, the approach considers statistical and thermodynamic inferences to deduce ecological patterns. However, concerns exist regarding the accuracy of diversity indices. Because relative quantities depend on the sorting of organisms (e.g., guilds and species) and their interactions, field observations carry inherent imprecision, thus leading to misinterpretation. Here, we present a framework that is able to appropriately achieve the thermodynamic properties in ecological systems and ensure the inference power. We demonstrate that effective abundances rather than raw abundances provide a trustful estimator of probabilities, which is evaluated through massive tests. We use empirical and synthetic data to show the advantages and reliability of this new framework under a broad range of conditions. The tests demonstrate that the replication principle is always optimized by the new estimator. Compared to other methods, this approach is simpler and reduces the importance of schemes used for sorting organisms. We highlight the robustness and the valor of effective abundances for ecological contexts: i) to assess and monitor the biodiversity, ii) to define the best sorting of organisms according to maximum entropy principles, and iii) to link local to regional diversity (-, {beta}-, and{gamma} -diversity).

ecology↗

bioRxiv2017

Fitting functional responses: Direct parameter estimation by simulating differential equations

O_LIThe feeding functional response is one of the most widespread mathematical frameworks in Ecology, Marine Biology, Freshwater Biology, Microbiology and related scientific fields describing the resource-dependent uptake of a consumer. Since the exact knowledge of its parameters is crucial in order to predict, for example, the efficiency of biocontrol agents, population dynamics, food web structure and subsequently biodiversity, a trustful parameter estimation is of utmost importance for scientists using this framework. Classical approaches for estimating functional response parameters lack flexibility and can often only serve as approximation for a correct parameter estimation. Moreover, they do not allow to incorporate side effects such as resource growth or background mortality. Both call for a new method to be established solving these problems.\nC_LIO_LIHere, we combined ordinary differential equation models (ODE models), that were numerically solved using computer simulations, with an iterative maximum likelihood fitting approach. We compared our method to classical approaches of fitting functional responses, using data both with and without additional resource growth and mortality.\nC_LIO_LIWe found that for classical functional response models, like the often used type II and type III functional response, the established fitting methods are reliable. However, using more complex and flexible functional responses, our new established method outperforms the traditional methods. Additionally, only our method allows to analyze experiments correctly when resources experience growth or background mortality.\nC_LIO_LIOur method will enable researchers from different scientific fields that are measuring functional responses to estimate parameters correctly. These estimates will enable community ecologists to parameterize their models more precisely, allowing for a deeper understanding of complex ecological systems, and will increase the quality of ecological prediction models.\nC_LI

ecology↗

bioRxiv2017

Diel predator activity drives a dynamic landscape of fear

AO_SCPLOWBSTRACTC_SCPLOWA landscape of fear (LOF) is a map that describes continuous spatial variation in an animals perception of predation risk. The relief on this map reflects, for example, places that an animal avoids to minimize risk. Although the LOF concept is a potential unifying theme in ecology that is often invoked to explain the ecological and conservation significance of fear, quantified examples of a LOF over large spatial scales are lacking as is knowledge about the daily dynamics of a LOF. Despite theory and data to the contrary, investigators often assume, implicitly or explicitly, that a LOF is a static consequence of a predators mere presence. We tested the prediction that a LOF in a large-scale, free-living system is a highly-dynamic map with peaks and valleys that alternate across the diel (24-hour) cycle in response to daily lulls in predator activity. We did so with extensive data from the case study of Yellowstone elk (Cervus elaphus) and wolves (Canis lupus) that was the original basis for the LOF concept. We quantified the elk LOF, defined here as spatial allocation of time away from risky places and times, across nearly 1000-km2 of northern Yellowstone National Park and found that it fluctuated with the crepuscular activity pattern of wolves, enabling elk to use risky places during wolf downtimes. This may help explain evidence that wolf predation risk has no effect on elk stress levels, body condition, pregnancy, or herbivory. The ability of free-living animals to adaptively allocate habitat use across periods of high and low predator activity within the diel cycle is an underappreciated aspect of animal behavior that helps explain why strong antipredator responses may trigger weak ecological effects, and why a LOF may have less conceptual and practical importance than direct killing.

ecology↗

bioRxiv2017

Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

ContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species potential to link patches or populations are of importance.\n\nObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.\n\nMethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.\n\nResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.\n\nConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

ecology↗

bioRxiv2017

Niche separation increases with genetic distance among bloom-forming cyanobacteria

Bacterial communities are composed of distinct groups of potentially interacting lineages, each thought to occupy a distinct ecological niche. It remains unclear, however, how quickly niche preference evolves and whether more closely related lineages are more likely to share ecological niches. We addressed these questions by following the dynamics of two bloom-forming cyanobacterial genera over an 8-year time-course in Lake Champlain, Canada, using 16S amplicon sequencing and measurements of several environmental parameters. The two genera, Microcystis (M) and Dolichospermum (D), are frequently observed simultaneously during bloom events and thus have partially overlapping niches. However, the extent of their niche overlap is debated, and it is also unclear to what extent niche partitioning occurs among strains within each genus. To identify strains within each genus, we applied minimum entropy decomposition (MED) to 16S rRNA gene sequences. We confirmed that at a genus level, M and D have different preferences for nitrogen and phosphorus concentrations. Within each genus, we also identified strains differentially associated with temperature, precipitation, and concentrations of nutrients and toxins. In general, niche similarity between strains (as measured by co-occurrence over time) declined with genetic distance. This pattern is consistent with habitat filtering - in which closely-related taxa are ecologically similar, and therefore tend to co-occur under similar environmental conditions. In contrast with this general pattern, similarity in certain niche dimensions (notably particulate nitrogen and phosphorus) did not decline linearly with genetic distance, and instead showed a complex polynomial relationship. This observation suggests the importance of processes other than habitat filtering - such as competition between closely-related taxa, or convergent trait evolution in distantly-related taxa - in shaping particular traits in microbial communities.

ecology↗

bioRxiv2017

The predictability of a lake phytoplankton community, from hours to years

Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

ecology↗

bioRxiv2018

Habitat loss is information loss: Species distribution models are compromised in anthropogenic landscapes

Species distribution models (SDMs) are valuable tools to estimate species distributions, but are vulnerable to biases in the probability of a species being observed. One such bias is habitat loss, which has affected a substantial and increasing proportion of the Earth. In regions of severe habitat loss, data on a species occurrence may represent a small, non-random subset of sites it once occupied. This could cause distorted reconstructions of species distributions, and misleading inferences of evolutionary and ecological processes. We present a statistical approach for quantifying the influence on SDMs of habitat loss, and generating distribution predictions that are robust to these biases. We explored some of the effects of accounting for habitat loss on inferences from common downstream biogeographic and ecological analysis methods.\n\nWe used herbarium record data to model the distribution of 325 plant species in the genera Banksia and Hakea across Australia, using point process models. We accounted for biases in the models by including a proxy variable representing habitat loss, and compared the fit of models without this variable to those with it. We explored the influence of habitat loss by mapping biodiversity patterns predicted with and without accounting for it.\n\nGenerally, accounting for habitat loss in SDMs led to increases in the mean area of modelled species distributions of ~10% for Banksia and ~12% for Hakea across Australia (in some cases, up to several 100,000 km2 increases in predicted range), with somewhat greater average increases (11% and 15%) for species in the southwest Australian biodiversity hotspot. Accounting for habitat loss leads to an increase in predicted species richness (Alpha and Gamma diversity), but a decrease in compositional turnover (Beta diversity), across most of Australia. Accounting for habitat loss in SDMs had minimal influence on a downstream macroevolutionary analysis (Age-Range Correlation) that utilizes species distributions, seemingly because exposure to habitat loss did not show a phylogenetic pattern in this taxonomic group.\n\nThe influence of habitat loss on species distributions estimated with SDM is likely to be context-dependent and difficult to generalize, but will tend to cause underestimates of range sizes. This may have consequences for mapping spatial patterns of diversity and for some downstream analyses of biogeographic, evolutionary, or ecological processes, based on species distributions, as well as conservation measures that rely on accurate species mapping.

ecology↗

bioRxiv2018

Two levels of host-specificity in a fig-associated Caenorhabditis

BackgroundBiotic interactions are ubiquitous and require information from ecology, evolutionary biology, and functional genetics in order to be completely understood. However, study systems that are amenable to investigations across such disparate fields are rare. Figs and fig wasps are a classic system for ecology and evolutionary biology with poor functional genetics; C. elegans is a classic system for functional genetics with poor ecology. In order to help bridge these disciplines, here we describe the natural history of a close relative of C. elegans, C. sp. 34, that is associated with the fig Ficus septica and its pollinating Ceratosolen wasps.\n\nResultsTo understand the natural context of fig-associated Caenorhabditis, fresh F. septica figs from four Okinawan islands were sampled, dissected, and observed under microscopy. C. sp. 34 was found in all islands where F. septica figs were found. C. sp. 34 was routinely found in the fig interior and almost never observed on the outside surface. Caenorhabditis was only found in pollinated figs, and C. sp. 34 was more likely to be observed in figs with more foundress pollinating wasps. Actively reproducing C. sp. 34 dominated younger figs, whereas older figs with emerging wasp progeny harbored C. sp. 34 dispersal larvae. Additionally, C. sp. 34 was observed dismounting from plated Ceratosolen pollinating wasps. C. sp. 34 was never found on non-pollinating, parasitic Philotrypesis wasps. Finally, C. sp. 34 was only observed in F. septica figs among five Okinawan Ficus species sampled.\n\nConclusionThese observations suggest a natural history where C. sp. 34 proliferates in young F. septica figs and disperses from old figs on Ceratosolen pollinating fig wasps. The fig and wasp host specificity of this Caenorhabditis is highly divergent from its close relatives and frames hypotheses for future investigations. This natural co-occurrence of the fig/fig wasp and Caenorhabditis study systems sets the stage for an integrated research program that can help to explain the evolution of interspecific interactions.

ecology↗

bioRxiv2018

Dispersal limitation and thermodynamic constraints govern spatial structure of permafrost microbial communities

Understanding drivers of permafrost microbial community composition is critical for understanding permafrost microbiology and predicting ecosystem responses to thaw, however studies describing ecological controls on these communities are lacking. We hypothesize that permafrost communities are uniquely shaped by constraints imposed by prolonged freezing, and decoupled from factors that influence non-permafrost soil communities. To test this hypothesis, we characterized patterns of environmental variation and microbial community composition in permafrost across an Alaskan boreal forest landscape. We used null modeling to estimate the relative importance of selective and neutral assembly processes on community composition, and identified environmental factors influencing ecological selection through regression and structural equation modeling (SEM). Proportionally, the strongest process influencing community composition was dispersal limitation (0.36), exceeding the influence of homogenous selection (0.21), variable selection (0.16), and homogenizing dispersal (0.05). Fe(II) content was the most important factor explaining variable selection, and was significantly associated with total selection by univariate regression (R2=0.14, p=0.003). SEM supported a model in which Fe(II) content mediated influences of the Gibbs free energy of the organic matter pool and organic acid concentration on total selection. These findings reveal that the processes shaping microbial communities in permafrost are distinct from those in non-permafrost soils, as the stability of the permafrost environment imposes dispersal and thermodynamic constraints on permafrost communities. Models of permafrost community composition will need to account for these unique drivers in order to predict community characteristics across permafrost landscapes, and in efforts to understand how pre-thaw conditions will influence post-thaw ecological and biogeochemical processes.

ecology↗

bioRxiv2018

Forfeiting the founder effect: turnover defines biofilm community succession

Microbial community succession is a fundamental process that effects underlying functions of almost all ecosystems; yet the roles and fates of the most abundant colonizers are poorly understood. Does early abundance spur long term persistence? How do deterministic and stochastic processes influence the roles of founder species? We performed a succession experiment within a hypersaline microbial mat ecosystem to investigate how ecological processes contributed to the turnover of founder species. Bacterial and micro-eukaryotic founder species were identified from primary succession and tracked through a defined maturation period using 16S and 18S rRNA gene amplicon sequencing in combination with high resolution imaging that utilized stable isotope tracers to evaluate basic functional capabilities. The majority of the founder species did not maintain high relative abundances in later stages of succession. Turnover (versus nestedness) was the dominant process shaping the final community structure. We also asked if different ecological processes acted on bacteria versus eukaryotes during successional stages and found that deterministic and stochastic forces corresponded more with eukaryote and bacterial colonization, respectively. Our results show that taxa from different kingdoms, that share habitat in the tight spatial confines of a biofilm, were influenced by different ecological forces and time scales of succession.

ecology↗

bioRxiv2018

bmotif: a package for counting motifs in bipartite networks

O_LIBipartite networks are widely-used to represent a diverse range of species interactions, such as pollination, herbivory, parasitism and seed dispersal. The structure of these networks is usually characterised by calculating one or more metrics that capture different aspects of network architecture. While these metrics capture useful properties of networks, they only consider structure at the scale of the whole network (the macro-scale) or individual species (the micro-scale). Meso-scale structure between these scales is usually ignored, despite representing ecologically-important interactions. Network motifs are a framework for capturing this meso-scale structure and are gaining in popularity. However, there is no software available in R, the most popular programming language among ecologists, for conducting motif analyses in bipartite networks. Similarly, no mathematical formalisation of bipartite motifs has been developed.\nC_LIO_LIHere we introduce bmotif: a package for counting motifs, and species positions within motifs, in bipartite networks. Our code is primarily an R package, but we also provide MATLAB and Python code of the core functionality. The software is based on a mathematical framework where, for the first time, we derive formal expressions for motif frequencies and the frequencies with which species occur in different positions within motifs. This framework means that analyses with bmotif are fast, making motif methods compatible with the permutational approaches often used in network studies, such as null model analyses.\nC_LIO_LIWe describe the package and demonstrate how it can be used to conduct ecological analyses, using two examples of plant-pollinator networks. We first use motifs to examine the assembly and disassembly of an Arctic plant-pollinator community, and then use them to compare the roles of native and introduced plant species in an unrestored site in Mauritius.\nC_LIO_LIbmotif will enable motif analyses of a wide range of bipartite ecological networks, allowing future research to characterise these complex networks without discarding important meso-scale structural detail.\nC_LI

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