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bioRxiv2017

Rethinking the importance of ecological networks through the glass of environmental variations

A major quest in network and community ecology has been centered on understanding the importance of structural patterns in species interaction networks--the synthesis of who interacts with whom in a given location and time. In the past decades, much effort has been devoted to infer the importance of a particular structure by its capacity to tolerate an external perturbation on its structure or dynamics. Here we demonstrate that such a perspective leads to inconsistent conclusions. That is, the importance of a network structure changes as a function of the external perturbations acting on a community at any given point in time. Thus, we discuss a research agenda to investigate the relative importance of the structure of ecological networks under an environment-dependent framework. We hypothesize that only by studying systematically the link between network structure and community dynamics under an environment-dependent framework, we can uncover the limits at which communities can tolerate environmental changes.\n\n\"Variation stands out as the only meaningful reality\"\n\nStephen J. Gould.

ecology↗
bioRxiv2017

Gender-biased perceptions of important ecology articles

Gender bias is still unfortunately rife in the sciences, and men co-author most articles (> 70%) in ecology. Whether ecologists subconsciously rate the quality of their peers work more favourably if men are the dominant co-authors is still unclear. To test this hypothesis, we examined how expert ecologists ranked important ecology articles based on a previously compiled list. Women proposed articles with a higher average proportion of women co-authors (0.18) than did men proposers (0.07). For the 100 top-ranked articles, women voters placed more emphasis on articles co-authored by women (0.06) than did men (0.02). However, women voters were still biased because they ranked men-dominated articles more highly, albeit not by as much as men did. This effect disappeared after testing read-only articles. This indicates a persistent, subconscious bias that men-dominated articles are considered to be of higher quality before actual assessment. We add that ecologists need to examine their own subconscious biases when appointing students, hiring staff, and choosing colleagues with whom to publish.

ecology↗
bioRxiv2017

Identifying Important Interaction Modifications in Ecological Systems

Trophic interaction modifications, where a consumer-resource link is affected by additional species, are widespread and significant causes of indirect effects in ecological networks. The sheer number of potential interaction modifications in ecological systems poses a considerable challenge, making prioritisation for empirical study essential. Here, we introduce measures to quantify the topological relationship of individual interaction modifications relative to the underlying network. We use these, together with measures for the strength of trophic interaction modifications to identify modifications that are most likely to exert significant effects on the dynamics of whole systems. Using a set of simulated food webs and randomly distributed interaction modifications, we test whether a subset of interaction modifications important for the local stability and direction of species responses to perturbation of complex networks can be identified. We show that trophic interaction modifications affecting interactions with a high biomass flux, those that connect species otherwise distantly linked, and those where high trophic-level species modify to interactions lower in the web have particular importance for dynamics. In contrast, the centrality of modifications in the network provided little information. This work demonstrates that analyses of interaction modifications can be tractable at the network scale and highlights the importance of understanding the relationship between the distributions of trophic and non-trophic effects.

ecology↗
bioRxiv2018

Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution

O_LIEcological and evolutionary research questions are increasingly requiring the integration of research fields along with larger datasets to address fundamental local and global scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns.\nC_LIO_LIPlanned missing data design (PMDD), where data are randomly and deliberately missed during data collection, is a simple and effective strategy to working under greater research constraints while ensuring experiments have sufficient power to address fundamental research questions. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and evaluating how data imputation procedures work under PMDD situations.\nC_LIO_LIUsing realistic examples and simulations of multilevel data we show how a variety of research questions and data types, common in ecology and evolution, can be aided by using a PMDD with data imputation procedures. More specifically, we show how PMDD can improve statistical power in detecting effects of interest even with high levels (50%) of missing data and moderate sample sizes. We also provide examples of how PMDD can facilitate improved animal welfare and potentially alleviate research costs and constraints that would make endeavours for integrative research challenging.\nC_LIO_LIPlanned missing data designs are still in their infancy and we discuss some of the difficulties in their implementation and provide tentative solutions. Nonetheless, data imputation procedures are becoming more sophisticated and more easily implemented and it is likely that PMDD will be an effective and powerful tool for a wide range of experimental designs, data types and problems in ecology and evolution.\nC_LI

ecology↗
bioRxiv2018

Ecological assessment of the River Nile around Gizert El-Warrak by Phytoplankton and macroinvertebrates assemblages

Biological assessment is considered a useful tool for assessing the ecological status of the aquatic ecosystems. Therefore, the goal of the present study was to use phytoplankton and macroinvertebrates as biological tools for ecological assessment of the River Nile around Gizert El-Warrak. A total of 33 phytoplankton species identified in the studied stations; these include 16 species of Chlorophyta, 5 species of Cyanophyta and 12 species of Bacillariophyta. All investigated stations characterized by high organic pollution according to Palmer's index. Trophic state index showed a hyper-eutrophic status in stations S1, S2, S4, S6 and S8 and an eutrophic status in stations S3, S5 and S7.Gastropoda and Oligochaeta were the most dominant of macroinvertebrates taxa recorded 50.8 and 24.6%, respectively. Diversity Index (H') ranged (1.14 - 2) which indicated that the structure of macroinvertebrates habitat was poor. Also, Evenness Index (J) ranged (0.016 - 0.043) which indicated that individuals were not distributed equally. The values of biotic index depending on macroinvertebrates categories showed that the River Niles water quality is fairly poor with significant organic pollution.\n\nSummary statementBioassessment based on non-taxonomic measurements of algae and biotic indices of macroinvertebrates may be considered as vital methods that reflect disturbances in aquatic systems for both short-term and long-term.

ecology↗
bioRxiv2018

Whence Lotka-Volterra? Conservation Laws and Integrable Systems in Ecology.

Competition in ecology is often modeled in terms of direct, negative effects of one individual on another. An example is logistic growth, modeling the effects of intraspecific competition, while the Lotka-Volterra equations for competition extend this to systems of multiple species, with varying strengths of intra- and inter-specific competition. These equations are a classic and well-used staple of quantitative ecology, providing a framework to understand species interactions, species coexistence, and community assembly. They can be derived from an assumption of random mixing of organisms, and an outcome of each interaction that removes one or more individuals. However, this framing is some-what unsatisfactory, and ecologists may prefer to think of phenomenological equations for competition as deriving from competition for a set of resources required for growth, which in turn may undergo their own complex dynamics. While it is intuitive that these frameworks are connected, and the connection is well-understood near to equilibria, here we ask the question: when can consumer dynamics alone become an exact description of a full system of consumers and resources? We identify that consumer-resource systems with this property must have some kind of redundancy in the original description, or equivalently there is one or more conservation laws for quantities that do not change with time. Such systems are known in mathematics as integrable systems. We suggest that integrability in consumer-resource dynamics can only arise in cases where each species in an assemblage requires a distinct and unique combination of resources, and even in these cases it is not clear that the resulting dynamics will lead to Lotka-Volterra competition.\n\nI acknowledge the Simons Foundation Grant #376199 and the McDonnell Foundation Grant #220020439.

ecology↗
bioRxiv2018

Mapping Social Ecological Systems Archetypes

While sustainable development goals are by their nature global, their achievement requires local action and thus targeting and monitoring sustainable solutions tailored to different social and ecological contexts. Ostrom stressed that there are no panaceas or universal solutions to environmental problems, and developed a social-ecological systems (SES) framework -a nested multi-tier set of variables- to help diagnose problems, identify complex interactions, and solutions tailored to each SES arena. The framework has been applied to over a hundred cases, typically reflecting in-depth analysis of local case studies, but with relatively small coverage in space and time. While case studies are context rich and necessary, it can be difficult to upscale their lessons to policy making realms. Here we develop a data driven method for upscaling Ostroms SES framework and apply it to a context where data is scarce, incomplete, but also where sustainable solutions are needed. The purpose of upscaling the framework is to create a tool that facilitates decision-making on data scarce contexts such as developing countries. We mapped SES by applying the SES framework to poverty alleviation and food security issues in the Volta River basin in Ghana and Burkina Faso. We found archetypical configurations of SES in space. Given data availability, we study their change over time, and discuss where agricultural innovations such as water reservoirs might have a stronger impact at increasing food security and therefore alleviating poverty and hunger. We conclude by outlining how the method can be used in other SES comparative studies.

ecology↗
bioRxiv2018

Ecological Network assembly: how the regional meta web influence local food webs

O_LILocal food webs result from a sequence of colonisations and extinctions by species from the regional pool or metaweb, i.e., the assembly process. Assembly is theorised to be a selective process: whether or not certain species or network structures can persist is partly determined by local processes including habitat filtering and dynamical constraints. Consequently, local food web structure should reflect these processes. C_LIO_LIThe goal of this study was to test evidence for these selective processes by comparing the structural properties of real food webs to the expected distribution given the metaweb. We were particularly interested in ecological dynamics; if the network properties commonly associated with dynamical stability are indeed the result of stability constraints, then they should deviate from expectation in the direction predicted by theory. C_LIO_LITo create a null expectation, we used the novel approach of randomly assembling model webs by drawing species and interactions from the empirical metaweb. The assembly model permitted colonisation and extinction, and required a consumer species to have at least one prey, but had no habitat type nor population dynamical constraints. Three data sets were used: (1) the marine Antarctic metaweb, with 2 local food-webs; (2) the 50 lakes of the Adirondacks; and (3) the arthropod community from Florida Keys classic defaunation experiment. C_LIO_LIContrary to our expectations, we found that there were almost no differences between empirical webs and those resulting from the null assembly model. Few empirical food webs showed significant differences with network properties, motif representations and topological roles. Network properties associated with stability did not deviate from expectation in the direction predicted by theory. C_LIO_LIOur results suggest that -- for the commonly used metrics we considered -- local food web structure is not strongly influenced by dynamical nor habitat restrictions. Instead, the structure is inherited from the metaweb. This suggests that the network properties typically attributed as causes or consequences of ecological stability are instead a by-product of the assembly process (i.e., spandrels), and may potentially be too coarse to detect the true signal of dynamical constraint. C_LI

ecology↗
bioRxiv2018

Interaction Modifications Disrupt The Structure And Stability Of Ecological Communities

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

The diversity of interaction types drives the functioning of ecological communities

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

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

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

ecology↗
bioRxiv2018

Interpreting mosquito feeding patterns in Australia through an ecological lens; an analysis of blood meal studies.

Mosquito-borne pathogens contribute significantly to the global burden of disease, infecting millions of people each year. Mosquito feeding is critical to the transmission dynamics of pathogens, and thus it is important to understanding and interpreting mosquito feeding patterns. In this paper we explore mosquito feeding patterns and their implications for disease ecology through a meta-analysis of published blood meal results collected across Australia from more than 12,000 blood meals from 22 species. To assess mosquito-vertebrate associations and identify mosquitoes on a spectrum of generalist or specialist feeders, we analysed blood meal data in two ways; first using a novel odds ratio analysis, and secondly by calculating Shannon diversity scores. We find that each mosquito species had a unique feeding association with different vertebrates, suggesting species-specific feeding patterns. Broadly, mosquito species could be grouped broadly into those that were primarily ornithophilic and those that fed more often on livestock. Aggregated feeding patterns observed across Australia were not explained by intrinsic variables such as mosquito genetics or larval habitats. We discuss the implications for disease transmission by vector mosquito species classified as generalist-feeders (such as Aedes vigilax and Culex annulirostris), or specialists (such as Aedes aegypti) in light of potential influences on mosquito host choice. Overall, we find that whilst existing blood meal studies in Australia are useful for investigating mosquito feeding patterns, standardisation of blood meal study methodologies and analyses, including the incorporation of vertebrate surveys, would improve predictions of the impact of vector-host interactions on disease ecology. Our analysis can also be used as a framework to explore mosquito-vertebrate associations, in which host availability data is unavailable, in other global systems.

ecology↗
bioRxiv2019

Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil

Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. Our environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.71). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and lagged (mechanistic) environmental risk, vaccine coverage, population density, temperature, and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts, and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventative action to reduce yellow fever spillover and prevent onward epidemics in humans.

ecology↗