Technician and lab manager positions available in the Duffy Lab at Michigan!

In addition to having two postdoctoral positions available (more info here, review of postdoc applications begins on March 1st), the Duffy Lab at the University of Michigan is searching for a technician and a lab manager. The technician and lab manager position applications will be available until Feb 26 2021. The projected start date for both positions is June 2021 in Ann Arbor.

Technician position:
The responsibilities for this position include maintaining Daphnia, algae, and parasite cultures, field sampling of lakes in the Ann Arbor area, carrying out lab experiments, helping to develop and test protocols, assisting with general lab maintenance and upkeep, and organizing and maintaining protocols. This position requires an Associate’s degree or higher; a Bachelor’s in biology, ecology, microbiology, or environmental science is preferred.

More information, including on how to apply, is available here. Any questions can be directed to Meghan Duffy (

Lab manager position:
The responsibilities for this position include many of the same responsibilities as those for the technician position, but also include working independently to analyze data, coordinating the field sampling and lab personnel, supervising hourly employees, and leading development and testing of protocols and equipment. This position requires a Bachelors degree in science with 1-3 years of experience; a Master’s degrees in biology, ecology, microbiology, or environmental science is preferred.

More information, including on how to apply, is available here. Any questions can be directed to Meghan Duffy (

A flat bottomed rowboat loaded with oars, pfds, and sampling equipment, on the edge of a large lake on a calm day. Big puffy clouds are visible in the sky and reflected in the lake's surface
Photo credit: Bruce O’Brien

Why do so many ecologists overestimate how informative small meta-analyses are about the mean effect size?

Recently, I showed poll results and other data tentatively making three points:

  1. The typical ecological meta-analysis includes 50ish effect sizes
  2. Many ecologists think that an ecological meta-analysis only needs 50ish effect sizes in order to provide a stable estimate of the mean effect size
  3. In fact, ecological meta-analyses typically need 250-500 effect sizes, or maybe even more, to provide a stable estimate of the mean effect size

In other words, many ecologists overestimate how informative most meta-analyses are about the mean effect size. Why?

I don’t think it’s because most ecologists haven’t ever thought about this issue (is it?). I don’t think most ecologists just subconsciously assume that however big a typical ecological meta-analysis is, that’s surely big enough (is it?). And I don’t think it’s because most ecologists are unaware of the assumptions and statistical properties of the hierarchical random effects models used for most ecological meta-analyses (is it?). So what’s the thinking? Here are the lines of thinking I’ve heard, in our comment threads and in correspondence with colleagues, and my responses to them.

tl;dr: I don’t come to bury Caesar meta-analyses. Meta-analyses can be useful! They’re a good tool for ecologists to have in our toolbox! I’m not one of those rare-but-annoying ecologists who think that meta-analyses and the ecologists who do them are somehow Bad. I just think that, for certain purposes, meta-analyses in ecology aren’t as useful as many ecologists think they are. I think it would be healthy to have a clear-eyed discussion about that. Maybe there are ways to do meta-analyses better. Or better ways to accomplish some of the goals we’re trying to accomplish with meta-analyses.

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Postdoc positions available in the Duffy Lab at Michigan!

Two postdoctoral positions focusing on host-symbiont interactions in inland lakes are available in the laboratory of Dr. Meghan Duffy in the Department of Ecology & Evolutionary Biology at the University of Michigan. The Duffy Lab studies the ecology and evolutionary biology of host-parasite interactions, using the aquatic crustacean Daphnia and their microparasites as a model system. The successful candidates will have access to a vibrant intellectual community and state-of-the-art facilities in the new Biological Sciences Building at Michigan.

In addition to being super cute, Daphnia are a great system for studying the ecology and evolution of infectious diseases at scales ranging from genes through ecosystems!

There is a lot of flexibility in terms of what successful applicants can work on, and postdocs will be encouraged to develop projects that are well-suited to their strengths and interests. Some themes of ongoing work in the lab include:

  • characterizing the diversity of symbionts in zooplankton in inland lakes
  • understanding the drivers of shifts between mutualism & parasitism
  • investigating the ecosystem-level impacts of shifts along a mutualism-parasitism gradient
  • discovery of the factors that allow parasites to move between host species
  • characterization of the distribution of parasites in the water column of lakes
  • interactions between symbionts (including parasites) within host individuals and at the population level
  • how host diversity influences parasitism
  • impacts of predators on host-parasite interactions
  • how symbionts alter Daphnia interactions with phytoplankton, and how phytoplankton influence Daphnia-symbiont interactions.
Mill Lake, one of our beautiful study sites in Southeastern Michigan!

The successful candidates for these positions will be expected to carry out independent research relating to aquatic symbiosis, using Daphnia and their symbionts (especially their microparasites) as a model system. Projects will be developed based on the strengths, interests, and expertise of the successful candidates. The projects will likely involve field and lab work. Depending on interest and abilities, postdocs will also have the ability to work on mathematical modeling of disease.

These positions will also involve mentoring of undergraduate researchers in the lab.

How to Apply
Interested individuals should send a CV, a brief description of research accomplishments and future goals, and the names and contact information for 3 references to Meghan Duffy by e-mail ( Review of applications will start on March 1, 2021 and will continue until the position is filled. The University of Michigan is an equal opportunity / affirmative action employer.

Required Qualifications
PhD (by start date) with experience in aquatic ecology, disease ecology, community ecology, eco-evolutionary dynamics, evolutionary ecology, microbiology, protistology, mycology, or a related field.

Desired Qualifications
Experience working with Daphnia and/or isolating parasites from the field would be beneficial, but is not required.

Other information
Preference will be given to applicants who can start by mid-summer 2021, though start dates as late as Fall 2021 are possible. Funding is available for each postdoc for at least two years, but is contingent on satisfactory progress in year one. The anticipated starting salary for the positions is $48,500 per year plus benefits.

The University of Michigan is an equal opportunity / affirmative action employer.

How many effect sizes estimates are “enough” for an ecological meta-analysis? Way more than many ecologists think!

Recently I polled y’all on how many effect size estimates an ecological meta-analysis needs to include to be “big enough”. That is, how many it needs to have for you to be reasonably confident that the estimated mean effect size won’t change too much as more studies are published in future (I’m summarizing the poll question; follow that last link if you want to see the exact poll question.)

Here are the poll responses to the question, along with an answer drawn from my pretty-comprehensive database of 476 ecological meta-analyses.

tl;dr: Ecological meta-analyses need to be waaaaaaaaaay bigger than most respondents think they need to be. They also need to be waaaaaaaaaay bigger than most of them actually are! At least, that’s sure how it looks to me, but have a look yourself and tell me if I’m wrong!

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PhD and postdoc openings in McGill lab at UMaine and postdoc opening in Niles and Gotelli labs at UVM

First the ad for a PhD and Postdoc at UMaine

Two positions are open to work in Brian McGill’s lab at the University of Maine as part of a larger group of eight faculty in Maine and Vermont on a large grant: Barraccuda (Biodiversity and RuRal communities Adapting to Climate Change Using Data Analysis) (OK the acronym is a stretch and misspelled, but it gets the main ideas across!). We are a team of ecologists, social scientists and spatiotemporal data scientists. Goals include: 1) modelling the response to climate change in birds, trees, certain crops, and zoonotic hosts of certain diseases, 2) understanding how rural communities will adapt to the changing environment, 3) improving the toolset for ecologists and social scientists working with spatiotemporal data, and 4) learning how to better communicate these complex results to stakeholders (especially in agriculture). Our subteams are organized around these four themes.

  • The PhD position will have as its primary focus working on theme #1 modelling responses of organisms to climate change. Funding is for one year with extensions possible for two more years depending on satisfactory performance and funds availability. There would also be teaching assistantships and the possibility for other grants to fill in up to 5 years of funding total. Requirements including a bachelors in ecology or related field and either existing skills or a strong desire to learn more data science. Opportunities to work and learn in other areas of the project also exist. A MSc is beneficial but not required. Stipend is $26K/year with annual cost of living increases plus coverage of tuition and health care. Start date is summer 2021.
  • The postdoc position will be more integrative and would work across all four themes of the project (ecological modelling, social sciences, stakeholder engagement and data science). This position would also work closely with the Waring lab at UMaine. Again funding would be for one year with extensions possible for two more years depending on performance and funds availability. Requirements include a PhD in a relevant field (e.g. ecology or related, social sciences related to rural communities, or data science or related) and a strong desire to learn and work in the other two areas. The ability to work both collaboratively and independently is also essential. Salary is $48-55K/year commensurate with experience and with annual cost of living increases plus a strong package of benefits according to the UMPSA agreement including healthcare and retirement plan contributions. Desired start date is summer 2021 (although earlier or slightly later is negotiable).

The University of Maine is located in Orono, ME which has a low cost of living, supports a walkable/bikable lifestyle, and has exceptional access to the outdoors ranging from a river, a lake and a trail network in town to national parks and wilderness not far away. Being part of the Bangor Metropolitan area and a university also results in good access to cultural events and services like health care, restaurants and shopping. We have an airport with direct connections to most East Coast cities and are a four hour car or bus ride away from Boston. We also have great K-12 schools if you are at a life stage where that matters. If you’re looking for clubbing until 2AM and eating in a different restaurant every night of the week, it might not be a fit, but most everybody else finds the quality of life excellent here (it’s pretty cute what they consider to be a “traffic jam” here).

The University of Maine is an equal opportunity employer and members of underrepresented minorities are encouraged to apply. To apply please submit a cover letter explaining fit to and interest in the project as well as a CV as a single PDF to Graduate student applicants should also include a transcript (GRE scores are optional but may be submitted if the student wishes and the same for TOEFL scores if not a native English speaker). Please note that if selected, the graduate student applicant will also need to apply to either the School of Biology and Ecology or the Ecology and Environmental Studies PhD program, but this can be done later. Review of applications will start February 19th and continue until the positions are filled. Please contact Brian McGill at with questions.

And the ad for a postdoc at UVM (University of Vermont)

Post-Doctoral Position- Species Distribution Modeling of Biodiversity and Adaptation of Farmers and Rural Communities To Climate Change

The University of Vermont is seeking qualified applicants for a two-year post-doctorate position, with potential for renewal for another two years, to use species distribution modeling to understand how biodiversity, farmers and rural communities adapt to the challenges of climate change.  The project includes the aggregation and development of largescale datasets of biodiversity, farmer behavior and perceptions across US states, construction of mechanistic, spatially explicit models of range shifts with climate adaptation, and application of these models to farmer and rural community responses to climate change.


Funded through a National Science Foundation grant, the research project with collaborators at University of Vermont (Dr. Meredith Niles, Dr. Nicholas Gotelli, Dr. Laurent Hébert-Dufresne) and University of Maine (Dr. Tim Waring, Dr. Brian McGill, Dr. Kati Corlew, Dr. Matthew Dube), seeks to understand how both rural human communities and species populations will respond to challenges posed by climate change [1]. The project will synthesize large amounts of data and develop new species distribution models to predict climate-driven shifts in species ranges as well as the responses and cultural adaptations of human communities. The project will also work with farmers and rural communities to understand their perspectives of the projected outcomes and responses. A successful applicant will work with a multidisciplinary team of biologists, social scientists and complexity researchers in Maine and Vermont.


The two main aims of this position are 1) to develop mechanistic, spatially explicit models of species range shifts, and 2) to develop a better understanding of the interaction of humans with biodiversity change and the ability of farmers and rural communities to adapt to climate change. This requires the assembly and analysis of species occurrence data (birds, trees, crops, and diseases), and datasets related to land use and farmer behavior.  Tasks include the identification of existing public datasets, the curation, aggregation, and synthesis of multiple data types, and the generation of novel species distribution models and indicators of climate adaptation and associated behaviors.  In addition, the post-doctorate will  help to integrate these data with evolutionary models of cultural adaptation to climate change and engage with agricultural and rural communities, including in presentation of results to diverse stakeholders and policy makers.


The position is one of five new hires that form the core of the four-year research project funded by the National Science Foundation. The post-doctorate will be co-advised at the University of Vermont by Dr. Meredith Niles ( in the Food Systems Program of the Department of Nutrition and Food Sciences, and Dr. Nicholas J. Gotelli ( in the Department of Biology. The Niles and Gotelli labs have a strong commitment to interdisciplinary research, biodiversity modeling, food systems science, and open access principles.  Salary range will be $48,000-$52,000), depending on experience.  There are a number of generous benefits associated with the position, which can be found at: .  The post-doctorate will also have opportunities for professional development and travel associated with the project, as relevant, as well as engagement with other professors on the project, especially Dr. Tim Waring, Dr. Laurent Hébert-Dufresne, and Dr. Kati Corlew.



  • Successful completion of a PhD in a relevant field of biology, social science, or data science
  • Demonstrated research and academic excellence evidenced by existing publications in relevant topics
  • Experience constructing, fitting, testing, and comparing species distribution models with species occurrence data
  • Excellent data science and social science quantitative skills
  • Experience with data aggregation and curation, especially across diverse types of datasets
  • Significant experience with Python and familiarity with other languages such as R, SQL, Stata, etc.
  • Excellent communication skills and ability to work with an interdisciplinary team across multiple institutions
  • Self-directed and ability to lead projects and learn new skills
  • Mature, organized, professional and courteous


  • Experience in interdisciplinary approaches to human behavior, especially in social-ecological systems
  • Experience working with farmers or rural communities
  • Strong interest and experience in data visualizations
  • Understanding, or interest, in stakeholder engagement
  • Understanding, or interest, in qualitative methods, including focus groups
  • Enthusiasm for open data and science practices


Please address questions and completed applications electronically to Dr. Meredith Niles ( and Dr. Nicholas Gotelli ( Applications should include:

  1. A cover letter detailing your interest in the position, how you meet the essential and desired requirements, and details of past research projects
  2. A CV or resume, including three references (with name, phone, email).

Review of materials will begin February 15th 2021 and continue until the position is filled.

How big does an ecological meta-analysis have to be to be “big enough”? Take our poll!

Meta-analysis has become a standard way to summarize the ecological literature. One of the most basic outputs of any meta-analysis is an estimate of weighted mean effect size, with effect size estimates typically being weighted by the inverses of their sampling variances so that more precise estimates contribute more to the average. Ok, there’s usually also considerable interest in explaining variation around the mean (e.g., via moderator variables). But presumably, if meta-analysis authors didn’t think the mean effect size was a scientifically meaningful quantity, they wouldn’t report it.

One question every meta-analysis author has to answer, if only implicitly, is how many effect size estimates are enough to make it worth publishing a meta-analysis. The answer matters, because the mean effect size in a meta-analysis will bounce around as additional effect size estimates are published. Just like how any sample mean will change as you incorporate additional observations into the sample. In most circumstances (not all), we’d expect that eventually the sample size would get big enough that the sample mean stabilizes. The same is true for meta-analyses: at some point, presumably there are enough effect size estimates that one can be reasonably confident that the mean effect size wouldn’t change too much if additional effect size estimates were published. You might decide it’s too early to publish a meta-analysis, if you don’t think there are enough published effect size estimates to be reasonably confident that the mean effect size has stabilized. (Or, you might decide to publish a meta-analysis anyway; there are various reasons one might publish a meta-analysis.)

So, how many effect size estimates do you think a typical ecological meta-analysis needs in order to be reasonably confident that the estimated mean effect size has stabilized? That it’s unlikely to change massively in future, if additional effect size estimates are published? Put more crudely, how many effect size estimates does an ecological meta-analysis need to include for you to say, “We now have a pretty good handle on the typical sign and magnitude of this effect”? Take our poll below!

For reference, in my nearly comprehensive database of 476 ecological meta-analyses, the average ecological meta-analysis includes 191 effect sizes. But the distribution is very skewed: the median is 60 effect sizes. The max is 2,922 effect sizes.

I do have data that speaks to the answer to this question, which I may reveal in a future post. But before I answer the question with data, I want to know what ecologists think the answer is!

(UPDATE: Come on, you chickens! Don’t everyone say “it depends!” If you think the answer depends on whether there are moderator variables, you can interpret my question as “How many effect sizes do you need in order to be reasonably confident in the estimated mean effect size, for a given level of the moderator variable(s)?”)