Understanding plant disease risk under environmental change and biodiversity loss

4-year PhD position in community ecology at the University of Zürich

Applications are invited for a 4-year PhD position to study biodiversity loss, environmental change, and the community ecology of infectious diseases by combining experimental and observational field approaches, using plants and their foliar pathogens as a model system.

Plant pathogens and their host plants often occur together in complex communities with many interacting partners. These host and pathogen communities and ensuing host-pathogen interactions often change predictably as habitats are disturbed and species are lost. But how do these processes interact with changes in environmental conditions? One possibility is that changing environmental conditions might affect these processes by altering host traits related to growth, reproduction, and defense against enemies.

The aim of this PhD project is to study how changes in key host traits affect host susceptibility to infection across an elevation gradient in Switzerland through a combination of experiments and observational surveys. This project shares a research setting with a study aimed at using mountain ecosystems to understand the interaction between biodiversity and global change, which will provide students with an opportunity to interact with researchers from a variety of backgrounds (and a beautiful backdrop for fieldwork).

Students with interest in disease ecology, community ecology, plant biology, or other related fields are encouraged to apply. Prior expertise in trait-based approaches, experimental design, statistical analysis, or basic molecular and disease diagnostic skills are a bonus, but your most important assets are enthusiasm for research, motivation to learn new things, and ability to work independently while being an active member of a research team.

The project is supervised by Dr. Fletcher Halliday in the research group of Prof. Anna-Liisa Laine at the University of Zürich. The Laine lab has broad expertise in studying the ecological and evolutionary dynamics of species interactions in natural populations (e.g. Halliday et al. 2021 eLife, Laine et al. 2019 eLife, Sallinen et al. 2020 Nature Communications).

To apply, please send a single PDF file merged from the following parts to fletcher.halliday@ieu.uzh.ch:  CV (with possible publications included), a copy of your academic transcript records, contact details of two references (e.g. MSc thesis supervisor), and a cover letter (MAX 1 page) with a description of your research interests and why you would be a suitable candidate for the project. Please include the word “PhDDISEASE22” in the subject line.

Applications will be considered until the position is filled. The position is available from January 1, 2022. For more information, please visit https://fletcherhalliday.com/ or contact fletcher.halliday@ieu.uzh.ch. The working language is English. German skills, although not essential, are helpful. Zürich is a highly attractive city in beautiful surroundings, with a multinational population, and many educational and recreational opportunities.

Evaluating the frequency and common drivers of within-host priority effects during coinfection

Fletcher W Halliday1†, Tuomas Aivelo†, Pablo Beldomenico†, Aaron Blackwell†, Vanessa Ezenwa†, Anna Jolles†, Erin Gorsich†, Claudia Fichtel†, Charlotte Defolie†, Anni M Hämäläinen†, Josue H Rakotoniaina†, Eva Kaesler†, Corey Freeman-Gallant†, Michael Huffman†, Natalie Olifers†, Arnaldo M Júnior†, Elisa P de Araujo†, Rita de Cassia Bianchi†,  Ana PN Gomes†, Paulo HD Cançado†, Matthew E Gompper†, & Paulo S D’Andrea†, Miroslava Soldanova†, Christopher Taylor†, Jason R Rohr

† contributed dataset

1Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, CH

See the poster here.

Download the pdf of this document here.


A current frontier in disease ecology is understanding how interactions among parasite species influence their epidemics. Interactions among parasites can result when prior infection by one parasite alters host susceptibility to a second parasite, generating priority effects among parasite species within host individuals. The increasing number of laboratory studies that aim to measure priority effects highlights growing interest among disease ecologists to understand these processes. Yet, laboratory studies, which are implemented at the scale of host individuals, are poorly suited to understand parasite epidemics, which occur at the scale of host populations, posing a key challenge for disease ecologists. One way to overcome this challenge is to mark and then repeatedly recapture sentinel hosts in the field, then analyze the resulting data using longitudinal regression models (Fenton et al. 2014; Hellard et al. 2015; Halliday et al. 2017, 2018). The purpose of this project was to implement this approach across empirical systems to test two broad questions:

  1. Is disease risk influenced by infection sequence during natural epidemics (i.e., do parasites generally experience/exhibit priority effects)?
  2. To what extent do these within-host priority effects depend on traits of individual parasite species?

To evaluate the role of within-host priority effects during parasite epidemics, we compiled longitudinal datasets of coinfection in host individuals across 13 host and 110 parasite species, including over 25,000 observations of host plants, primates, ungulates, small mammals, birds, and invertebrates. To evaluate the role of within-host priority effects, we performed time-until event analysis, specifically testing whether infection sequence among co-occurring parasites influenced their risk of infection (e.g., Halliday et al. 2017, 2019).  Surprisingly, the sequence of infection predicted fewer than 10% of the more than 1000 pairwise combinations of potentially interacting parasites. This rarity of within-host priority effects may result from a lack of natural variation in infection sequence, indicating that within-host priority effects may be a less common driver of parasite epidemics than previously thought, and highlighting the need for experimental manipulations of parasite infection in the field (e.g., Ezenwa & Jolles 2015; Pedersen & Fenton 2015; Halliday et al. 2017). Showing weak support for ecological theory, parasites with a high degree of niche overlap were slightly more sensitive to priority effects than parasites with a low degree of niche overlap, and this effect was stronger for parasites with narrow niche requirements than with wide niche-requirements. However, in contrast with theory, the frequency of priority effects could not be explained by a parasite’s impact on its host. Together, these results indicate that priority effects may be less commonly detected during natural epidemics than previously expected, and that within-host priority effects among parasites may not necessarily follow the same ecological rules of community assembly as their free-living counterparts.

 The database:

We compiled a database of longitudinal surveys of host individuals (i.e., mark-recapture data), where hosts were unmanipulated (sentinels were ok), and surveyed for infection by multiple parasites. This study focused on interspecific interactions among parasites rather than intraspecific interactions among strains of the same parasite species. The final database includes plants, invertebrate, and vertebrate hosts (Table 1).

Table 1. Contributing authors and associated host datasets
Contributors Host species Host taxon
T. Aivelo Microcebus rufus Primate
P. Beldomenico Hydrochoerus hydrochaeris Rodent
A. Blackwell Homo sapiens Human
V. Ezenwa, A. Jolles, E. Gorsich Syncerus caffer Wild ungulate
AM Hämäläinen, JH Rakotoniaina, E. Kaesler, C. Kraus, P. Kappeler Microcebus murinus Primate
C. Fichtel, C. Defolie Eulemur rufifrons Primate
C. Freeman-Gallant Geothlypis trichas Bird
F. Halliday Festuca arundinacea Plant
M. Huffman Pan troglodytes Primate
N. Olifers, AM Júnior, EP de Araujo , R de Cassia Bianchi,  APN Gomes, PHD Cançado, ME Gompper, & PS D’Andrea Nasua nasua &              Cerdocyon thous Carnivore
M. Soldanova Lymnaea stagnalis Invertebrate
C. Taylor Microtus agrestis Rodent

 

A standard measurement of within-host priority effects across systems:

To facilitate comparisons across systems, data were analyzed using a common analytical method (described in Halliday et al 2017). Briefly, to detect priority effects, we recorded the sequence of infection by each parasite on each host individual. Time in days since the first survey of each host individual was used as a proxy for exposure to parasite propagules. To model within-host priority effects, we constructed a series of models following Halliday et al (2017). These models explicitly measure priority effects by testing whether the sequence of infection on an individual host influences the rate of infection by each parasite. Each model included one dependent variable pertaining to one parasite species (“the focal parasite”).  To explicitly model priority effects, we used Cox Regression with Firth’s Penalized Likelihood to reduce bias from factors exhibiting (quasi)complete separation (Heinze & Schemper 2002) from the R package, coxphf (Ploner & Heinze 2015), to estimate the probability of a host transitioning from uninfected to infected. Specifically, the dependent variable in each model was time to infection, modeled as the transition rate from uninfected to infected as a function of exposure time. We modeled time to infection resulting from a baseline rate of infection shared by all individuals and modified by the infection status of the host by each other parasite during the previous survey (treated as a time-varying coefficient). Exponentiated coefficients are interpreted as multiplicative changes in infection rate, providing a standardized estimate of priority effects across study systems. Statistically significant (p<0.05) positive and negative coefficients were interpreted as evidence of facilitative and antagonistic priority effects, respectively, whereas non-significant coefficients were interpreted as lacking sufficient evidence of a priority effect occurring.

Our hypotheses:

Hypothesis 1 – Niche overlap. Priority effects are expected to occur more commonly among species with a high degree of niche overlap (Vannette & Fukami 2014). A host comprises the entire niche available to parasites during infection (Kuris et al. 1980; Rynkiewicz et al. 2015), and thus coinfecting parasites often exhibit high niche overlap (Sousa & Zoologist 1992; Graham 2008; Seabloom et al. 2015). The degree of niche overlap among parasites may additionally vary depending on whether parasites share common vectors, employ similar feeding strategies, or share infection sites. We hypothesized that such parasites would therefore more commonly experience priority effects.

Hypothesis 2 – Parasite virulence. Priority effects are expected to occur when early arriving species have a high impact on shared resources with later arriving species (Vannette & Fukami 2014). Parasites require host resources for survival, growth, and reproduction (Lafferty & Kuris 2002). We hypothesized that early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects.

Hypothesis 3 – Host breadth. Priority effects are expected occur when the late arriving species have high requirements for shared resources with early arriving species (Vannette & Fukami 2014). Late arriving species with narrow niche requirements may therefore experience the strongest priority effects. For free-living species, the breadth of niche requirements may manifest as generality in a species’ use of habitat types or tolerance to environmental conditions. For parasites, the breadth of niche requirements may be related to host specificity, defined as the ability to infect multiple unrelated host taxa (Barrett et al. 2009; Park et al. 2018). We hypothesized that late-arriving host specialists would therefore more commonly experience priority effects.

Analytical methods

Data analysis was performed using R version 3.5.2 (R Core Team 2015). To test whether parasites that shared similar traits more commonly experienced priority effects (Hypothesis 1), we used a series of generalized linear mixed models using the lme4 package, version 1.1-20 (Bates et al. 2014), treating the source dataset and host species (Table 1) as random intercepts in each model. The dependent variable in each model was whether or not a pairwise combination of parasites exhibited a priority effect (0,1), and the independent variables where whether or not parasites were the same type (e.g., both macroparasites), shared a common infection site (e.g., both gastrointestinal), or shared the same transmission mode (e.g., both tick-vectored).  Data for every trait was not available for every parasite species (Table S1), so in order to maximize the number of studies contributing to the analysis, each independent variable was therefore tested in a separate analysis. We used inverse-variance weighting based on the number of surveys per host to give more explanatory weight to studies with a greater number of surveys of each host individual. However, because this weighting strongly influenced the results of the analyses, we report results from both weighted and unweighted analyses.

To test whether early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects, we again tested whether or not a pairwise combination of parasites exhibited a priority effect, this time as a function of host virulence. We coded host virulence as 1 if the parasite had few reported adverse effects on the host, 2 if the parasite was known to moderately reduce host fitness, and 3 if the parasite was known to seriously impact host survival (e.g., infections frequently leading to host mortality).

To test whether late-arriving host specialists more commonly experienced priority effects, we used a similar, weighted generalized linear mixed model in lme4, testing whether or not a pairwise combination of parasites exhibited a priority effect as a function of the host specificity of the late-arriving parasite (generalist = known to infect more than one host species; specialist = known to infect only a single host species).

We finally tested whether niche-overlap lead to stronger priority effects among late-arriving host specialists by analyzing the interaction coefficient for each pairwise combination of parasites as a dependent variable.  Because more closely related species tend to exhibit higher niche-overlap, we used the phylogenetic relatedness of parasites as a metric of niche-overlap, and modeled its interaction with host specificity of the late-arriving parasite and their main effects as independent variables in this analysis. Parasites were defined as “closely related” if they shared at least the same order, and “distantly related” if they were from different orders or more distantly related.

Results

Contrary to expectations, we found surprisingly limited evidence of priority effects across our database. Specifically, out of 1128 potential pairwise interactions, we detected only 98 interactions resulting from prior infection (i.e., priority effects). 64 were facilitative (prior infection by one parasite species facilitated subsequent infection by another species), 34 interactions were antagonistic (prior infection by one parasite species inhibited infection by a subsequent species), and the remaining 1030 lacked sufficient evidence to support a priority effect occurring. This surprising result may stem from a lack of variation in infection sequence among study systems. Early infections were often dominated by one or a few parasite species (Fig. 1), potentially resulting from variation in parasite phenology (Halliday et al. 2017) or variation in parasite transmission rates (Clay et al. 2019). Together, these results point to the need for studies that systematically manipulate infection sequence during natural epidemics, potentially through the use of pesticides or anti-parasite treatments of hosts (Pedersen & Fenton 2015).

 

first_infection_plot_4col
Fig 1. In most study systems, early infections were dominated by one or a few parasites. This lack of variation in infection sequence may explain why fewer priority effects were observed than expected based on laboratory studies. Each point represents a single parasite species, and the y-axis represents the proportion of hosts whose first infection was associated with that parasite. Values on the y-axis sum to >1 due to simultaneous infections.

We found weak support for the hypothesis that parasites with high niche overlap would more commonly experience priority effects. Specifically, when weighted by the number of samples per host individual, parasites of the same type (e.g., both macroparasites), parasites that infected the same site (e.g., gastrointestinal parasites), and parasites with the same transmission mode (e.g., both tick vectored), all experienced significantly more priority effects than parasites of different types, different sites, and different transmission modes (p= 0.006, p<0.001, and p = 0.004, respectively; Fig 2). However, these effects were of small magnitude and were not statistically significant when the same analyses were performed with unweighted data (p = 0.98, p = 0.48, p = 0.68, respectively). We found no support for the hypothesis that early-arriving parasites that were more damaging to their hosts would more commonly experience priority effects. Even though the weighted regression indicated that parasite virulence significantly predicted the priority effects (p < 0.001), the direction of this effect was not consistent with our prediction, with moderately virulent parasites exhibiting the lowest rate of priority effects (Fig 2). The effect of host virulence on the probability of observing a priority effect was not significant when the analysis was performed with unweighted data (p = 0.25).

 

hypothesis_1_3_short
Fig 2. Priority effects were somewhat more commonly observed among parasites of the same type, infection site, or transmission mode (p0.05 when unweighted), lending some support to hypothesis one. In contrast with hypothesis one, priority effects were no more commonly observed when early arriving species had greater or smaller impacts on their hosts, though weighted regression indicates that moderately viulent parasites may exhibit fewer priority effects than low- or highly virulent parasites.

We found strong evidence that later arriving parasites with narrow host breadth (e.g., specialists) more commonly experienced priority effects than parasites with wider host breadth (e.g., generalists; weigthed p < 0.001, unweighted p = 0.007; Fig. 3 – inset). Furthermore, among host specialists, more closely related parasites tended to exhibit more antagonistic interactions (p = 0.003). Together, these results indicate that priority effects among parasites may be less common than expected based on laboratory studies, and that priority effects are rarely consistent with predictions from ecological theory.

 

plot_with_inset_no_annotation
Fig 3. Priority effects were more commonly observed when late-arriving parasites were host specialists, supporting hypothesis two. Among specialists, more closely related parasites tended to experience more competition. Black circles are model estimated means, error bars are 95% confidence intervals; colored points are estimates from each individual pairwise combination of studies, with size corresponding to the number of samples per host individual. Blue indicates significant facilitation, red indicates significant antagonism, and grey indicates insufficient evidence for significant priority effect. The inset shows the model estimated probability of a priority effect on the y-axis as a function of host specificity.

 

References

Barrett, L.G., Kniskern, J.M., Bodenhausen, N., Zhang, W. & Bergelson, J. (2009). Continua of specificity and virulence in plant host–pathogen interactions: causes and consequences. New Phytol., 183, 513–529.

Bates, D., Mächler, M., Bolker, B. & Walker, S. (2014). Fitting Linear Mixed-Effects Models using lme4. J. Stat. Softw., 67, 1–48.

Clay, P.A., Cortez, M.H., Duffy, M.A. & Rudolf, V.H.W. (2019). Priority effects within coinfected hosts can drive unexpected population‐scale patterns of parasite prevalence. Oikos, 128, 571–583.

Ezenwa, V. & Jolles, A. (2015). Opposite effects of anthelmintic treatment on microbial infection at individual versus population scales. Science (80-. ).

Fenton, A., Knowles, S.C.L., Petchey, O.L. & Pedersen, A.B. (2014). The reliability of observational approaches for detecting interspecific parasite interactions: comparison with experimental results. Int. J. Parasitol., 44, 437–45.

Graham, A.L. (2008). Ecological rules governing helminth-microparasite coinfection. Proc. Natl. Acad. Sci. U. S. A., 105, 566–70.

Halliday, F.W., Heckman, R.W., Wilfahrt, P.A. & Mitchell, C.E. (2019). Past is prologue: host community assembly and the risk of infectious disease over time. Ecol. Lett., 22, 138–148.

Halliday, F.W., Umbanhowar, J. & Mitchell, C.E. (2017). Interactions among symbionts operate across scales to influence parasite epidemics. Ecol. Lett., 20, 1285–1294.

Halliday, F.W., Umbanhowar, J. & Mitchell, C.E. (2018). A host immune hormone modifies parasite species interactions and epidemics: insights from a field manipulation. Proc. R. Soc. B Biol. Sci., 285, 20182075.

Heinze, G. & Schemper, M. (2002). A solution to the problem of separation in logistic regression. Stat. Med., 21, 2409–2419.

Hellard, E., Fouchet, D., Vavre, F. & Pontier, D. (2015). Parasite-Parasite Interactions in the Wild: How To Detect Them? Trends Parasitol.

Kuris, A.M., Blaustein, A.R. & Alio, J.J. (1980). Hosts as Islands. Am. Nat., 116, 570–586.

Lafferty, K.D. & Kuris, A.M. (2002). Trophic strategies, animal diversity and body size. Tree, 17, 507–513.

Park, A.W., Farrell, M.J., Schmidt, J.P., Huang, S., Dallas, T.A., Pappalardo, P., et al. (2018). Characterizing the phylogenetic specialism-generalism spectrum of mammal parasites. Proc. R. Soc. B Biol. Sci., 285, 20172613.

Pedersen, A.B. & Fenton, A. (2015). The role of antiparasite treatment experiments in assessing the impact of parasites on wildlife. Trends Parasitol., 31, 200–11.

Ploner, M. & Heinze, G. (2015). coxphf: Cox regression with Firth’s penalized likelihood. R Found. Stat. Comput.

R Core Team. (2015). R: a language and environment for statistical computing | GBIF.ORG.

Rynkiewicz, E.C., Pedersen, A.B. & Fenton, A. (2015). An ecosystem approach to understanding and managing within-host parasite community dynamics. Trends Parasitol., 31, 212–221.

Seabloom, E.W., Borer, E.T., Gross, K., Kendig, A.E., Lacroix, C., Mitchell, C.E., et al. (2015). The community ecology of pathogens: Coinfection, coexistence and community composition. Ecol. Lett., 18, 401–415.

Sousa, W.P. & Zoologist, A. (1992). Interspecific Interactions among Larval Trematode Parasites of Freshwater and Marine Snails Interspecific Interactions Among Larval Trematode Parasites of Freshwater and Marine Snails1. Interactions, 32, 583–592.

Vannette, R.L. & Fukami, T. (2014). Historical contingency in species interactions: Towards niche-based predictions. Ecol. Lett., 17, 115–124.

Table S1. Parasites and their associated traits

Parasite Type Virulence Specialist Route Mode Helminth Intracellular
Anaplasma centrale microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Ascaris lumbricoides macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Anaplasma marginale microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Anaplasma phagocytophilum microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Acanthocephala macroparasite Low Complex Ingestion Helminth Not intracellular
Amblyomma cajennense ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Amblyomma ovale ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Amblyomma parvum ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Apicomplexa microparasite Low Complex Not Helminth Intracellular
Ascaridae macroparasite Low Direct Ingestion Helminth Not intracellular
Ascaridida macroparasite Low Direct Ingestion Helminth Not intracellular
Aspidoderidae macroparasite Low Direct Ingestion Helminth Not intracellular
Australapatemon burti macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Australapatemon minor macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Babesia microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Ballantidium microparasite Low Direct Ingestion Not Helminth Not intracellular
Bartonella microparasite Medium Generalist Complex Flea vectored Not Helminth Intracellular
Brucella abortus microparasite Medium Generalist Direct Ingestion Not Helminth Intracellular
Callistoura macroparasite Low Direct Ingestion Helminth Not intracellular
Capillaria macroparasite Low Complex Ingestion Helminth Not intracellular
Cestoda macroparasite Low Complex Ingestion Helminth Not intracellular
Conoidasida microparasite Low Direct Ingestion Not Helminth Intracellular
Conoidasida microparasite Low Direct Ingestion Not Helminth Intracellular
Colletotrichum cereale microparasite Low Generalist Direct Rain splash Not Helminth Not intracellular
Cooperia macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Cotylurus cornutus macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Cowpox virus microparasite Medium Generalist Direct Direct contact Not Helminth Intracellular
Dicrocoeliidae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Diphyllobothriidae macroparasite Low Complex Ingestion Helminth Not intracellular
Diplostomum pseudospathaceum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Dipylidiidae macroparasite Low Complex Ingestion Helminth Not intracellular
Ehrlichia ruminantium microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Anaplasma microparasite Low Specialist Complex Tick vectored Not Helminth Intracellular
Echinocleus hydrochaeri macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Echinoparyphium aconiatum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Echinoparyphium recurvatum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Echinostoma revolutum macroparasite Medium Generalist Complex Ingestion Helminth Not intracellular
Eimeria boliviensis microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Eimeria hydrochaeri microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Eimeria microparasite Medium Specialist Direct Ingestion Not Helminth Intracellular
Entamoeba microparasite Medium Direct Ingestion Not Helminth Not intracellular
Fasciolidae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Siphonaptera ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Giardia lamblia microparasite Medium Generalist Direct Ingestion Not Helminth Not intracellular
Haemonchus macroparasite Medium Direct Ingestion Helminth Not intracellular
Ancylostomatidae macroparasite Low Specialist Direct Free-living Helminth Not intracellular
Hymenolepis macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Hypoderaeum conoideum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Laelapidae ectoparasite Low Direct Free-living Not Helminth Not intracellular
Lemuricola macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Lemuricola macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Lemurostrongylus macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Leucocytozoon microparasite Low Generalist Complex Blackfly vectored Not Helminth Intracellular
Listrophorus ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Phthiraptera ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Metagonymus macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Hystrichopsyllidae ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Moliniella anceps macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Myobiidae ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Neotrichodectes pallidus ectoparasite Low Specialist Direct Free-living Not Helminth Not intracellular
Notocotylus attenuatus macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Oesophagostomum macroparasite Medium Generalist Direct Ingestion Helminth Not intracellular
Opisthioglyphe ranae macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Opisthorchis macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Oesophagostomum stephanostomum macroparasite Medium Generalist Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Oxyuridae macroparasite Low Direct Ingestion Helminth Not intracellular
Pararhabdonema macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Paryphostomum radiatum macroparasite Low Generalist Complex Ingestion Helminth Not intracellular
Physalopteridae macroparasite Low Complex Ingestion Helminth Not intracellular
Plagiorchis elegans macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Plasmodium microparasite Medium Specialist Complex Mosquito vectored Not Helminth Intracellular
Protozoophaga obesa macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Strongyloides macroparasite Low Generalist Direct Free-living Helminth Not intracellular
Caenorhabditis macroparasite Low Direct Ingestion Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Chromadorea macroparasite Low Helminth Not intracellular
Enterobius macroparasite Low Direct Ingestion Helminth Not intracellular
Panagrellus macroparasite Low Direct Ingestion Helminth Not intracellular
Puccinia coronata microparasite Low Specialist Complex Airborne Not Helminth Not intracellular
Rhizoctonia solani macroparasite Medium Generalist Direct Rain splash Not Helminth Not intracellular
Schistosoma macroparasite Medium Generalist Complex Free-living Helminth Not intracellular
Schistosomatidae macroparasite Medium Generalist Complex Free-living Helminth Not intracellular
Strongyloides fuelleborni macroparasite Low Generalist Direct Ingestion Helminth Not intracellular
Strongiloides macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongyloidae macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Strongiloides chapini macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Subulura macroparasite Low Complex Ingestion Helminth Not intracellular
Subulura macroparasite Low Complex Ingestion Helminth Not intracellular
Theileria mutans microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Theileria parva microparasite High Generalist Complex Tick vectored Not Helminth Intracellular
Theileria sable microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Theileria sp (buffalo) microparasite Medium Generalist Complex Tick vectored Not Helminth Intracellular
Trichuris trichiura macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Theileria velifera microparasite Low Generalist Complex Tick vectored Not Helminth Intracellular
Mycobacterium tuberculosis microparasite Medium Specialist Direct Airborne Not Helminth Intracellular
Thysanotaenia macroparasite Low Specialist Complex Ingestion Helminth Not intracellular
Ixodida ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Trematoda macroparasite Low Complex Helminth Not intracellular
Trichinellidae macroparasite Low Complex Ingestion Helminth Not intracellular
Trichobilharzia szidati macroparasite Low Generalist Complex Free-living Helminth Not intracellular
Trichodectes canis ectoparasite Low Generalist Direct Free-living Not Helminth Not intracellular
Trichostrongyloidea macroparasite Low Direct Ingestion Helminth Not intracellular
Trichuris macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trichuris cutillasae macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trichuris macroparasite Low Specialist Direct Ingestion Helminth Not intracellular
Trypanosoma microparasite Medium Generalist Complex Tsetse fly vectored Not Helminth Intracellular
Trichuris trichiura macroparasite Low Specialist Direct Ingestion Helminth Not intracellular

 

Advice to prospective graduate students

This post was originally published on BioDiverse Perspectives – a research blog aimed at fostering communication about biodiversity.

Getting into grad school is a lot of work. By now, most North American PhD programs in ecology are in the “recruitment phase”. Students have already taken their GRE entrance exams, contacted professors, obtained letters of recommendation, written applications, and waited. Soon they will be visiting prospective universities for the dreaded interview weekend. Below is a list of things (originally published in 2014) that we wish we’d known going into grad-school recruitment. Got others? Share them in the comments section below.

I’ll be the first to admit that I didn’t know much going into graduate school interviews. What I did know was that a commitment to grad school is more than a commitment to a program. I knew that I’d be committing to a relationship with people – especially my potential advisor and lab mates – and that I’d be committing to a relationship with a geographical location – between high school and grad school, I’d never lived in one place for longer than 3 years (thanks in part to an extended stay in junior college), so this was a big deal for me. My eventual recruitment trips reflected this. I asked a lot of questions about grad student – advisor relationships, and I asked a lot of questions about the places I was visiting. Here are some things I didn’t really think about, though:

  1. Try to get to know your potential advisor. A bad relationship with your advisor can poison your grad school experience. And although asking fellow grad students about their relationships can be useful, your relationship with your new advisor is going to be a reflection of your two personalities. No two relationships are the same, so use this opportunity to get to know your potential advisor as best you can.
  2. The same thing goes for getting know a place. One of the things I truly regret not taking advantage of while I was interviewing was the opportunity to travel. If you have time, stay a couple extra days and check out your potential future home.
  3. Take control of your spare time. Recruitment weekends can be really hectic, especially if you end up with a day of back-to-back-to-back interviews. If you need a break, do it. For me, this meant reminding myself that it was okay to stop and use the bathroom between meetings, even if said bathroom breaks weren’t scheduled. I guess an alternative could be to invest in a stadium pal.
  4. Finally ask your hosts about the questions they wish they had asked. In addition to providing some good advice, this can also be a nice window into the hopes and regrets of grad students who have made the exact decision that you’re contemplating. A group of fantastic grad students in the biology department at UNC put together this handy list of things to talk to your potential advisor about before you accept a position in their lab. Who knows, maybe your host has put something like this together, too. -Fletcher Halliday

When I made the decision to start a PhD program, I had the advantage of having already obtained a masters’ degree and taking time off (2.5 years) from grad school. From this perspective I knew something that I think most, particularly young fresh-from-undergrad applicants, don’t take seriously enough when making a final decision on what program to attend. That is, the whole ‘life’ picture – strike a balance between a program and a place that will allow you to achieve balance (when you have time!). Grad school is super intense and stressful. If you end up at an awesome school that is in an awful and unfriendly town your work could ultimately suffer because of it (and vice versa!). While at recruitment take the time to scope out life outside the walls of the university. Definitely ask current students how they feel about the area and what they do for fun. I really feel many don’t think through how their personal-side of life will look during grad school. Don’t assume you can compromise personal happiness, particularly in a program that may last 4-8 years! -Kylla Benes

When you’re visiting a lab, take some time to meet the other grad students.  Whether you become best friends or not, if you join their lab you’ll be spending plenty of time with these people.  Also, these are people who have gone through the same process you are, and have been accepted to a lab and (hopefully) feel happy there.  If you’re visiting for more than a day, go out for dinner and beers with them, and ask them every single question you can think of.  What’s it really like working in their lab?  Is the professor nice or is he or she a jerk?  Do they micromanage or do they expect their students to be independent?  How long do most graduate students take to graduate?  What sorts of jobs do they get afterwards?  How much do grad students make, and how does that compare to the city’s cost of living?  What sorts of things do students do for fun outside of school?  Is there a good social scene (how far away are the bars) or is it a pretty quiet city? Kylla mentioned this already, but I wanted to emphasize how important this question is. You may be surprised with the answers they give, but you’ll definitely be glad you asked. -Nate Johnson

Like Nate, I also encourage you to seek out grad students, ideally as far from campus as possible (I know our institute arranges lunches and/or dinners that are students-only). I’ve found that graduate students are nothing if not candid, especially when it comes time to bitch about their job/lack of pay. So take everything that is said with a grain of salt and ask them straight up “You’ve mentioned a lot of negative things, why are you here?” Also know that their opinion is not the only one: I’ve seen labs where some people absolutely despise their advisor (and labmates), and others in the very same lab get along famously (both with advisors and each other). So don’t get stuck talking to the one disgruntled student, or if you do, know that they are probably using you as an outlet to vent every negative thing they hate about their life.

I also think its important to point out that interviews run both ways: yes, you should be on your best behavior, but so should they. So while it seems like most of the pressure is on you, you hold more of the cards: if they’ve invited you up for the weekend and offered to pay your way, they want you. You have passed muster: your grades are good, your statements were compelling, you demonstrate potential to succeed, and most notably, some faculty has stood up and said “Yes, I will shepherd* them through 3-7 years of intellectual exploration!” (*fund) So relax, take a breath, and enjoy yourself. If you’re more comfortable, you’re also likely to come across better. And don’t be afraid to admit that this advisor/lab/school isn’t for you. I had an interview where I knew pretty much right off the bat that it wasn’t a good fit, on any level. We parted amicably (I hope!) but I wrote the person immediately afterwards and said thank you, I appreciated you taking the time to show me around, but personally I didn’t feel it was a good fit.

Good luck! -Jon Lefcheck

On these trips it’s easy to focus on the faculty and to some extent the grad students in the lab and program. That’s all very valuable, but it’s also a good idea to interact with the other prospectives as well. I definitely DON’T mean being competitive with them, because if you choose to enter that program, some subset of that group will be your cohort, which can be your most valuable asset in getting through grad school. Your peers will help you normalize manuscript rejections, listen to you vent about research frustrations and qualifying exam anxieties. Of course, you don’t know who will and won’t join the program in your interview group, but it’s worthwhile to consider whether they are a group of people you could see yourself spending time with socially (though it’s not necessary) or who you would want to have access to when you need emergency field assistance on the tide flats at 3 am.

And to reiterate what was said above, remember that current grads and faculty can also get burned out by these events. I agree with Jon that everyone should be on their best behavior! But, if they do seem tired or cynical, maybe just remember that it can be an occupational hazard from time to time.

Also, never get intimidated by the process (easier said than done). It’s all about fit, and the timing. These interviews certainly feel like a performance, but you need to be a certain amount of relaxed to be able to take in all the information about the program and people that will help you make a decision about what’s good for you. – Emily Grason

 

Biodiversity Challenge!

This post was originally published on BioDiverse Perspectives – a research blog aimed at fostering communication about biodiversity.

A few months ago, Sharon Baruch-Mordo posted a provocative challenge. She called it the biodiversity challenge and offered it as an opportunity to engage the public – an opportunity to speak to non-scientists about our work and to convince them that they should care about it. Her thesis was that if we’re interested in generating knowledge – any knowledge really – it’s our responsibility to disseminate that knowledge as thoroughly as possible. The biodiversity challenge is a tough one. Not many graduate students are given training in science communication, and when we are, it’s usually targeted towards generating concise scientific prose that can be published in peer-reviewed journals. But the challenge is an important one. If we want to disseminate our knowledge, we have to be able to communicate it effectively.

Here’s the challenge:

Write a 500-word essay for a newspaper or magazine about the importance of your research in the context of biodiversity and conservation. Your target audience is the general public and your goal is to be educational and convincing.

Well, we took the biodiversity challenge. Over the past two weeks we’ve been toiling away at writing about our research interests. Next week we’ll be posting them for everyone to see and we challenge you to join in. You can upload it here or email it to us at info@biodiverseperspectives.com. We will publish them all next week, in full, as we receive them, unedited, in all their glory.

Jon Lefcheck has already taken the challenge. Won’t you?

UPDATE: The Biodiversity Challenges are coming in. Check them out here!

Following #INT13 on Twitter (UPDATED!)

This post was originally published on BioDiverse Perspectives – a research blog aimed at fostering communication about biodiversity.

I was supposed to be doing field work today, but since it was raining, I took the day off to get some writing done for my impending dissertation proposal defense.   Lucky for me (and my tendency for procrastination), the INTECOL meeting is going on right now, and unlike ESA, it looks like there’s enough service in the conference center for people to live-tweet the conference. This being my first ever twitter-only conference attendance, I thought I’d share my observations.

Here’s a little snippet of my own personal meta-live-blog:

11:17EDT. I just started following #INT13! What time is it in London right now, anyway?

11:18EDT. I think I’m in two sessions right now: One on niche-dynamics / species ranges; and one on conservation. According to @Ecopostblog, there are elephants in Mali, and not many people know it:

Dr Canney: many people don’t even know that there are elephants in Mali! #INT13

— EcoPost (@ecopostblog) August 19, 2013

Hopefully, I’ll find out more! 11:20 More on elephants.  Looks like they don’t use corredors much.

11:22. Looks like I’m missing a talk on soil fauna and altitudinal gradients; there was a great workshop; JEcology is having a meet the editor session! Cool! Hopefully someone live-tweets that!

11:23. Just learned that L Seabrook studies Koala poo. I wonder if there was a funny joke about that in the talk.

11:24. More on elephants – someone is developing, has developed, will develop a passage for elephants that’s free of human activities? Not totally sure…

11:25 INNGEcologists are having an early carreer social night. That sounds like fun!

11:27 I’m not sure what happened to the niche-dynamics session…

11:28 Reminder to self: look up Koricheva research on birch clone diversity and leaf mining.

11:29 Lack of implementation of biodiversity plans in South America? That sounds cool!

Andrew knight “lessons can be learned from lack of implementation of biodiversity plans in sa. #int13 — Olivia Richardson (@orichardson12) August 19, 2013

11:30 I have no idea what this is in reference to:

Urban dwellers’ herds is the problem! Outsiders use up the resources of the locals. #INT13 — EcoPost (@ecopostblog) August 19, 2013

Ok, back to work. I’ll keep following along for the rest of the day, and write some general thoughts tonight.

UPDATED:  Ok, It looks like day one of INTECOL is over, or at least the tweets have slowed down.

I should preface my commentary here by saying that I’m fairly new to twitter. I just joined in February. I resisted joining for a long time and am still learning the language. As a result, I probably could have used this:

Don’t forget that we’re giving Twitter tutorials at the BES stand today. Please spread the word! #INT13

— BES (@BritishEcolSoc) August 19, 2013

And I probably missed a lot of great conference material. Feel free to chime in with suggestions for improving my absentee INTECOL experience!

My immediate reaction to a day of conference tweeting is holy cow! That was a lot of information. I’d say that a conference on twitter is pretty incomprehensible, and unless I’m fully devoted to following along, it’s hard to really get a good idea of what any single talk Is about.

Here then, is my summary of day one at INTECOL on twitter:
There were a ton of really great talks on a wide variety of subjects. My favorite talks were this talk linking invasion ecology and climate change (I think):

Meyer: guerrilla strategy in #invasion (long dispersed events of few individuals) enhanced by climate warming #INT13

— Pablo Gonzalez (@pglezmoreno) August 19, 2013

 

#int13 Meyer dispersal key to plant response to climate change, depending on their enemies. . .

— William Gosling (@palaeolim) August 19, 2013

 

Meyer: arguing that diversity effects become stronger over time #INT13

— Carly Ziter (@carlyziter) August 19, 2013

And I really enjoyed Sandra Diaz’s plenary talk on plant functional trait diversity (Full disclosure, this talk took place while I was still sleeping, but I was able to look up the tweets and follow along afterwards).

Functional traits taking over #INT13 by opening plenary speaker Sandra Diaz pic.twitter.com/1AxI0RftHg

BSBNceECEAAnA40

— Rob Salguero-Gomez (@DRobcito) August 19, 2013

 

It sounded like the take home message (thanks @JNGriffy!) was that:

Functional traits hold great promise to mechanistically link biodiversity and ecosystem services #INT13

— John Griffin (@JNGriffy) August 19, 2013

However,

Sandra Diaz: more research needed. ‘We just don’t know enough to understand how functional diversity links to environmental change.’#INT13

— EEB&Flow (@EEB_Flow) August 19, 2013

 

So,

Sandra Diaz is inviting everybody to keep TRYing and upload the forgotten data on plant traits to the TRY database #INT13

— Silvija (@SBudaviciute) August 19, 2013

And for comparison, here’s a summary from Dries Bonte, who was actually there (by the way, check out the Wiley at INTECOL blog. It’s really cool!). It looks like I got the gist of Sandra Diaz’s talk, but I completely missed David Tilman’s talk on biodiversity maintenance. Fortunately, I’m not alone. One of three tweets on that talk:

Could not assist at David Tilman’s conference at #INT13, not enough place in the room for all the fans 😉

— Marine Robuchon (@MarineRobuchon) August 19, 2013

 

I think following the conference at twitter was a good experience, but it could be better. Here’s a list of things that I really enjoyed from day 1:
Humor (most of which comes completely out of context):

“Mathematics is like sex, its ok to talk about it, but not ok to do it in public” #INT13

— Laura Boggeln (@LauraBoggeln) August 19, 2013

 

Walked into @theorecol‘s talk late – I wonder what he’s talking about. Just seems like noise #INT13

— Bob O’Hara (@BobOHara) August 19, 2013

 

Pictures:

“Ants are supercool” from @TomRBishop in his #INT13. He’s not wrong! #ants #ecology pic.twitter.com/PyYexN2thr

BSC0-xuCIAAswsF

— Heather Campbell (@scienceheather) August 19, 2013

 

This is brilliant!! #INT13 #INTECOL #ecology #entomology pic.twitter.com/heHGhXmCwn

BSCjVM0CIAAXFGv

— Fevziye Hasan (@fezidae) August 19, 2013

 

Bits of wisdom:

Hugh Possingham: “Never give people an answer, give them a tool.” #INT13

— Michaela Plein (@michaelaplein) August 19, 2013

 

But there were also some things that I didn’t really like:

First, a common theme was the single tweet about what a talk was going to be about, with no follow up (I’m guilty of doing this at ESA this year, too). I’m sure that there’s some utility to this, but as an e-attendant, it wasn’t very useful. In a sense, I feel like I’d be better off using this great website to read through the abstracts myself.

But most frustrating was that I saw very little communication between people tweeting the conference. It was almost as though there were hundreds of people talking over / past each other.  I first joined twitter as a necessity for getting the most out of the 2013 Science Online Conference, and I loved how people asked questions and commented during discussions.  Here’s a great example from a session on imposter syndrome. I’m hoping that this is the future of tweeting scientific conferences.

If it’s still raining tomorrow, maybe I’ll try again, but overall, this tweet pretty well sums up my experience:

#int13 message of the day. Online no substitute 4 Face2face contact in the field to get people excited about ecology @BESroadies @palaeolim

— Lesley Batty (@LesleyBatty) August 19, 2013