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1. Vector demography, dispersal and the spread of disease: Experimental epidemics under elevated resource supply

   https://doi.org/10.1111/1365-2435.13672  

   Strauss, Alexander T. ; Henning, Jeremiah A. ; Porath‐Krause, Anita ; Asmus, Ashley L. ; Shaw, Allison K. ; Borer, Elizabeth T. ; Seabloom, Eric W. ; Biere, ed., Arjen ( September 2020 , Functional Ecology)

   The spread of many diseases depends on the demography and dispersal of arthropod vectors. Classic epidemiological theory typically ignores vector dynamics and instead makes the simplifying assumption of frequency‐dependent transmission. Yet, vector ecology may be critical for understanding the spread of disease over space and time and how disease dynamics respond to environmental change.

   Here, we ask how environmental change shapes vector demography and dispersal, and how these traits of vectors govern the spatiotemporal spread of disease.

   We developed disease models parameterised by traits of vectors and fit them to experimental epidemics. The experiment featured a viral pathogen (CYDV‐RPV) vectored by aphidsRhopalosiphum padiamong populations of grass hostsAvena sativaunder two rates of environmental resource supply (i.e. fertilisation of the host). We compared anon‐spatialmodel that ignores vector movement, alagged dispersalmodel that emphasises the delay between vector reproduction and dispersal, and atravelling wavemodel that generates waves of infections across space and time.

   Resource supply altered both vector demography and dispersal. Thelagged dispersalmodel fit best, indicating that vectors first reproduced locally and then dispersed globally among hosts in the experiment. Elevated resources decreased vector population growth rates, nearly doubled their carrying capacity per host, increased dispersal rates when vectors carried the virus, and homogenised disease risk across space.

   Together, the models and experiment show how environmental eutrophication can shape spatial disease dynamics—for example, homogenising disease risk across space—by altering the demography and behaviour of vectors.

   A freePlain Language Summarycan be found within the Supporting Information of this article.

    

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2. Can dynamic network modelling be used to identify adaptive microbiomes?

   https://doi.org/10.1111/1365-2435.13491  

   Garcia, Joshua ; Kao‐Kniffin, Jenny ; Bennett, ed., Alison ( December 2019 , Functional Ecology)

   In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.

   To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.

   Ultimately, we argue that components of microbial community interactions can be characterized through mathematical models to reveal insights into complex dynamics associated with a plant or animal host trait. The inclusion of interaction networks in microbiome studies could provide insights into the behaviour of complex communities in tandem with host trait modification and evolution.

   A freePlain Language Summarycan be found within the Supporting Information of this article.

    

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3. Intraspecific variation in realized dispersal probability and host quality shape nectar microbiomes

   https://doi.org/10.1111/nph.19195  

   Francis, Jacob S. ; Mueller, Tobias G. ; Vannette, Rachel L. ( August 2023 , New Phytologist)

   Epiphytic microbes frequently affect plant phenotype and fitness, but their effects depend on microbe abundance and community composition. Filtering by plant traits and deterministic dispersal‐mediated processes can affect microbiome assembly, yet their relative contribution to predictable variation in microbiome is poorly understood.

   We compared the effects of host‐plant filtering and dispersal on nectar microbiome presence, abundance, and composition. We inoculated representative bacteria and yeast into 30 plants across four phenotypically distinct cultivars ofEpilobium canum. We compared the growth of inoculated communities to openly visited flowers from a subset of the same plants.

   There was clear evidence of host selection when we inoculated flowers with synthetic communities. However, plants with the highest microbial densities when inoculated did not have the highest microbial densities when openly visited. Instead, plants predictably varied in the presence of bacteria, which was correlated with pollen receipt and floral traits, suggesting a role for deterministic dispersal.

   These findings suggest that host filtering could drive plant microbiome assembly in tissues where species pools are large and dispersal is high. However, deterministic differences in microbial dispersal to hosts may be equally or more important when microbes rely on an animal vector, dispersal is low, or arrival order is important.

    

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4. Fitness costs and benefits vary for two facultative Burkholderia symbionts of the social amoeba, Dictyostelium discoideum

   https://doi.org/10.1002/ece3.5529  

   Garcia, Justine R. ; Larsen, Tyler J. ; Queller, David C. ; Strassmann, Joan E. ( August 2019 , Ecology and Evolution)

   Hosts and their associated microbes can enter into different relationships, which can range from mutualism, where both partners benefit, to exploitation, where one partner benefits at the expense of the other. Many host–microbe relationships have been presumed to be mutualistic, but frequently only benefits to the host, and not the microbial symbiont, have been considered. Here, we address this issue by looking at the effect of host association on the fitness of two facultative members of theDictyostelium discoideummicrobiome (Burkholderia agricolarisandBurkholderia hayleyella). Using two indicators of bacterial fitness, growth rate and abundance, we determined the effect ofD. discoideumonBurkholderiafitness. In liquid culture, we found thatD. discoideumamoebas lowered the growth rate of bothBurkholderiaspecies. In soil microcosms, we tracked the abundance ofBurkholderiagrown with and withoutD. discoideumover a month and found thatB. hayleyellahad larger populations when associating withD. discoideumwhileB. agricolariswas not significantly affected. Overall, we find that bothB. agricolarisandB. hayleyellapay a cost to associate withD. discoideum, butB. hayleyellacan also benefit under some conditions. Understanding how fitness varies in facultative symbionts will help us understand the persistence of host–symbiont relationships.

   This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available athttps://openscholarship.wustl.edu/data/15/

    

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5. Natural history and ecological effects on the establishment and fate of Florida carpenter ant cadavers infected by the parasitic manipulator Ophiocordyceps camponoti ‐ floridani

   https://doi.org/10.1111/1365-2435.14224  

   Will, Ian ; Linehan, Sara ; Jenkins, David G. ; de Bekker, Charissa ( November 2022 , Functional Ecology)

   Ophiocordycepsfungi manipulate the behaviour of their ant hosts to produce a summit disease phenotype, thereby establishing infected ant cadavers onto vegetation at elevated positions suitable for fungal growth and transmission. Multiple environmental and ecological factors have been proposed to shape the timing, positioning and outcome of these manipulations.

   We conducted a long‐term field study ofOphiocordyceps camponoti‐floridaniinfections ofCamponotus floridanusants—the Florida zombie ants. We propose and refine hypotheses on the factors that shape infection outcomes by tracking the occurrence of and fungal growth from hundreds of ant cadavers. We modelled and report these data in relation to weather, light, vegetation and attack by hyperparasites.

   We investigated environmental factors that could affect the occurrence and location of newly manipulated ant cadavers. New cadaver occurrence was preferentially biased towards epiphyticTillandsiabromeliads, canopy openness and summer weather conditions (an interactive effect of temperature, humidity and precipitation). Furthermore, we suggest that incident light at the individual cadaver level reflects microhabitat choice by manipulated ants or selective pressure on cadaver maintenance for conditions that improve fungal survival.

   We also asked which environmental conditions affect fungal fitness. Continued fungal development of reproductive structures and putative transmission increased with moist weather conditions (interaction of humidity and precipitation) and canopy openness, while being reduced by hyperparasitic mycoparasite infections. Moreover, under the most open canopy conditions, we found an atypicalOphiocordycepsgrowth morphology that could represent a plastic response to conditions influenced by high light levels.

   Taken together, we explore general trends and the effects of various ecological conditions on host and parasite disease outcomes in the Florida zombie ant system. These insights from the field can be used to inform experimental laboratory setups that directly test the effects of biotic and abiotic factors on fungus–ant interactions or aim to uncover underlying molecular mechanisms.

   Read the freePlain Language Summaryfor this article on the Journal blog.

    

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