by Coralie Williams
​

Subgroup analysis, moderator analysis, meta-regression
Sometimes we come across terms that sound different but actually mean similar things. Moderator analysis is a broad term for any method that looks at whether moderators (also called predictors, or independent variables) help explain differences in the effect sizes being analysed. One type of such methods is subgroup analysis, where studies are grouped based on a categorical variable and effect sizes are compared across these groups (e.g.  treated vs control). This method is useful to answer many questions, but it is limited to categorical variables. Meta-regression takes things a step further by using a regression model to look at how one or more moderators are linked to variation in effect sizes. These moderators can be categorical, continuous, or both. So, subgroup analysis is really just a simpler case of meta-regression, and they are both types of moderator analysis used in meta-analysis.

Fixed-effect vs fixed-effects models
Fixed-effect (singular noun) vs fixed-effects (plural noun) are sometimes used interchangeably in the literature to describe meta-analysis models, despite referring to different statistical assumptions (Borenstein et al., 2010; Viechtbauer, 2010). The fixed-effect (singular) model assumes that all studies in the meta-analysis estimate a common true effect size. Whereas, the fixed-effects (plural) model assumes that each study has its own true effect, but these are treated as fixed quantities and not drawn from a distribution. This makes it suitable for cases where we believe heterogeneity exists but want to restrict inference to the studies at hand (which is actually quite rare). In statistical modelling, "fixed effect" usually refers to a non-random coefficient in a regression model, for example species traits. But in meta-analysis, the label “fixed effect” can refer to a model. Confusing? Yep. And that's why some recommend renaming the fixed-effect model to the common-effect or equal-effects model for clarity.

Multivariate in meta-analysis
Another tricky term is multivariate. In a statistical sense multivariate refers to multiple response (outcome) variables. However, in meta-analytical modelling, this term can have several meanings. I recommended reading a great post by James Pustejovsky here who elaborates on this (with some humour) and explains its various meanings which has help me a lot to understand this term in the context of meta-analysis methodology.
These kinds of nuances in terminology can make it hard to get a clear conceptual footing, especially when new to the field, but hopefully it doesn’t scare you away from the wonderful (really it is) world of meta-analysis!
 
References

• Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97–111. https://doi.org/10.1002/jrsm.12
• Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(3). https://doi.org/10.18637/jss.v036.i03

by Shinichi Nakagawa

When I was in Sydney this February, my old colleague Rob Brooks (from UNSW) told me I should go and see an NHL game, specifically an Oilers game, so finally, I did! I went with Totoro (my older son), and it was our first time at Rogers Place. It was such an exciting night.

Before the game, my new colleague Kim Mathot kindly lent us two Oilers T-shirts. They were very orange and very cool; we looked like real fans. The whole place was full of people wearing Oilers fancy jerseys, shouting and cheering even before and throughout the game.

The game was close, 2-2 until the third period. Then suddenly, the Oilers scored again and again! Every goal made us elate and the stadium shake. Everyone was yelling, jumping, clapping. There was so much energy, and the final score was 4–2.

It was very Canadian, at least that is what I thought, with cold drinks, loud music, and parachuting pizzas. Maybe not always polite, yet very polite.

I still don’t understand the rules very well (icing?), but it was a lot of fun. Totoro and I had a great time and bonded via Oilers, which I first thought was “Eulers” after the famous mathematician Euler [pronounced: oy-lr]!?!? – how wrong I was!

(photos by Shinichi Nakagawa)

by Malgorzata (Losia) Lagisz

I've been living in Sydney for ten years now, yet somehow, I never made it to the famous Mardi Gras Parade—even though it happens  every year just a few kilometers from the UNSW campus. With this being my final year in Sydney (well, technically, just half a year), I realised it was my last chance to experience it before moving to Canada.

My younger son decided to join me (how had he never heard about the Mardi Gras before?). We easily found a free viewing spot near the end of the parade route in Moore Park. Since I was still recovering from a fever, we stayed for less than an hour, but it was enough to see nearly 50 parade floats and groups—and to soak up the incredible atmosphere.

The event was colorful, energetic, and wonderfully diverse. The crowd included everyone from babies in strollers to centenarians on mobility scooters, with people of all backgrounds, body shapes, and abilities. The floats were just as varied, featuring everything from the Childless Cat Ladies to the City Mayor, with a strong presence of Aboriginal and Torres Strait Islander people leading the march. Costumes ranged from minimalistic to absolutely extravagant, and no matter how people dressed, the energy was infectious—everyone was having a blast.

It was a cool event to witness—so joyful and uplifting. Unfortunately, I didn’t have a great camera, and my phone isn’t the best for night photography, but I still managed to capture a few shots worth sharing.

by Yefeng Yang

​Today, my topic is about what we often overlook in meta-analytic practice—things that could make a big difference to the reliability of our results.

Meta-analysis is everywhere now. With the rise of user-friendly statistical software, conducting one has never been easier. This accessibility is a double-edged sword. On the bright side, researchers with little to no statistical background can run meta-analyses and produce what is often considered more reliable evidence than any single study (thanks to increased statistical power). But on the flip side, it has become so accessible that many forget the statistical complexity behind it.

I’ve seen many researchers grab example code from somewhere (yes, open science – the soul of our lab!) and tweak it for their own data without really thinking about whether the approach they’re borrowing or the way they’re interpreting their results is actually valid. The problem? This can lead to misleading evidence, which, when applied to conservation, health, or policy-relevant topics, may have serious real-world consequences. So today, I want to highlight two critical but often ignored steps in meta-analysis:

1. Checking model assumptions
2. Assessing model fit
 
Are your assumptions holding up?
Every statistical model relies on assumptions, and meta-analysis models are no exception. But let’s be honest—how often do we actually check them? For example, most meta-analysis models assume that effect size estimates come from normal sampling distributions (note: this doesn’t mean the effect sizes themselves have to be normally distributed). Yet, in practice, few people ever check this assumption. It’s easy to do—just simulate the sampling distribution of the effect size you have chosen, plot a histogram, or use a normal quantile-quantile (Q-Q) plot to see if things look off:

Another assumption that gets ignored is that effect size estimates are unbiased estimates of the true effect. This one might surprise you: the commonly used log-response ratio log(X₁/X₂) is actually a biased estimator because of Jensen’s inequality. There’s a simple bias-correction factor based on a Taylor expansion, but hardly anyone applies it.
 
Is your model a good fit for your data?
When you fit a meta-analysis model, you are making an implicit assumption that the model accurately represents the data-generating process. But in reality, the true process is almost always more complex than any model one can come up with. This is why checking model fit is essential, yet it’s something we rarely do.
 
One simple way to test for model misspecification is by looking at standardized (deleted) residuals—if they’re not randomly scattered, that’s a red flag. Similarly, we often assume that true effects vary within and across studies (heterogeneity), but how many of us actually test this using, for example, a Q-test (not that this is different the often reported heterogeneity index I2)? We also assume that both within- and between-study random effects follow a normal distribution, yet we almost never run statistical tests to confirm this.
 
And when we use the restricted maximum likelihood (REML) method, we assume it has successfully found the optimal parameter estimates. But without checking the likelihood profile, how do we know if it actually did? Most of us don’t bother to check—and that’s a problem.
 
So, what we should do?
My answer: don’t assume—verify! Some might say I’m overthinking this. Sure, some assumption violations—like non-normality—may not always impact results that much, according to simulations. But here’s the thing: you never know if that’s true for your specific dataset. The best way forward is to check. If you find assumption violations or model misspecifications, be transparent—report them, and interpret your results with caution. That said, I understand that checking every assumption and model fit metric manually can be tedious. This is where methodologists and software developers could step in—by creating pipelines that automate these essential checks.
 
Meta-analyses shape scientific understanding, policy, and real-world decisions. If we want them to provide truly reliable evidence, we need to stop mechanically clicking and pointing in a GUI software or running a couple of lines of R code without critical thinking. Because in the end, a meta-analysis is only as good as the care put into it.

by Shinichi Nakagawa

As 2024 draws to a close, I want to take a moment to reflect on another wonderful year for I-deel.
 
This June, I moved to Canada, where I joined the University of Alberta. It was a slow start with learning new things such as new acronyms, ticketing systems and hidden rules. While I settled into life at the University of Alberta  (see pics below for my new workplace with some exotic beasts), most I-deel members who stayed in Sydney have been doing a lot of great jobs in my physical absence (albeit moral presence).

I have been trying to find a way for many years how we could create a lab where people are comfortable expressing opinions and they can also disagree freely and they can resolve such disagreements - this is how science should work and this is how good scientists are made. This year, I felt our members are taking a lot of initiatives to be more independent and inter-dependent. This sounds like an oxymoron but it is not. Many members are leading projects. And also they are working with others to get help for where they can work together or where they can create synergy. I have been extremely lucky with my lab members in the past, but the current bunch is pretty incredible.

Not only they are a nice bunch of people working together. They are very productive and I highlight some of these successes this year:

1. Our three PhD students, Coralie, Lorenzo, and Kyle all published their amazing work in top journals (Methods in Ecology and Evolution, Environment International, Chemosphere and Environmental Pollution; note Lorenzo get an award for the best paper for his work on a meta-analysis on PFAS transfer in birds).
2. Speaking of the best paper awards, Losia led an international collaboration on this very topic into PLoS Biology.
3. Szymek published his simulation work on the sample size required to study both sexes in PLoS Biology.
4. Yefeng published his in-silico replication work in Nature Ecology & Evolution.
5. Patrice published a collaborative paper from our lab meetings on how to best write an abstract in Proceedings B.
6. Pietro got his masterpiece of a systematic map of meta-analyses on sexual selection into Biological Reviews.
7. Ayumi got her first meta-analysis (the function of eye patterns in butterflies) into eLife.

 
There are more amazing publications including many blog posts on various topics (thank you!), presentations and personal achievements from I-deel members this year. These are underlined by dedication and teamwork, for which I thank all I-deel members. Soon, in Feb 2025, I will be visiting Sydney to celebrate all these milestones with awesome I-deel members. In 2025, also I have a challenging job of replicating this awesome team here at the University of Alberta - the Centre of Open Science and Synthesis in Ecology and Evolution - a very exciting year ahead!

Completing a PhD takes a lot of effort and many years. By the end of your PhD, you have already acquired a very diverse range of skills, and I believe you are already an amazing scientist who should be easily employable. However, the reality is often different. If you would like to continue on the academic path, job opportunities are sadly quite scarce. This creates a large bottleneck, where most people with PhDs do not end up with a continuing academic position. However, there are some job opportunities tailored for different career stages, in a variety of countries, so there is a chance you may land the postdoc of your dreams if you apply! You’ll never get the job you don’t apply for, but you may get one out of the dozens of applications you put up. Perseverance can go a long way!

Here are a few tips and resources I have gathered that could be helpful.

1. Recognise the diversity of skills you have
The skills you have acquired during your PhD are unique, diverse, and not restricted to your specific research topic. You have acquired a wide range of transferable skills such leadership, coding, data management, writing, public speaking, or time, people, and budget management. These skills are sought out and valued by many! You have also demonstrated that you are an excellent and fast learner. Job ads often list a wide range of skills, and nobody ticks all the boxes – don’t be afraid to apply even if you’re missing some of the required skills, you just have to convince them that you can learn and master those skills quickly.   

2. Apply to as many jobs as you can
You’ll never get the job you don’t apply for, but you may get one out of the few applications you put out! It certainly takes time to apply for jobs, but this time is not always lost. It helps you think about the next big questions you would like to work on, you get experience with interviews, and you become a more efficient writer. You may also make important connections!

3. Email the people you would love to work with.
If you are a big fan of someone’s research, it might be a good idea to contact them and ask if they would like to collaborate with you. They may have some funding available or invite you to apply for a grant or fellowship. Even if they do not have a position available at the time, they may contact you in the future when a new opportunity becomes available. Just remember to contact previous and current lab members to make sure the atmosphere in the group is what you are looking for. In my view, working with people you get along with, and whose values and work ethics are aligning with yours is key to a happy and productive working environment!

4. Identify the people who have received funding recently
Often, the issue is not finding someone to collaborate with but finding someone who has available funding to employ you. However, if you consult grant reports and identify who has been awarded funding recently, there is a chance they have funding available for a postdoc! Of course, only contact the people you would like to work with, but that is an option I have heard worked for many.

5. Apply for fellowships
The issue with applying with already-funded postdocs is that you may not have complete freedom with the research topic, and you may be more constrained than during your PhD. However, with fellowships, you are free to design your own research project, which is both an interesting exercise and an incredibly exciting thing to look forward to! Fellowships are very competitive, but there are lots of them worldwide for different career stages, and some give you a lot of freedom (see below for some useful resources)!

6. Reuse your research proposals for multiple fellowships
Putting your first fellowship proposal together takes a huge amount of time. Do not underestimate fellowship applications - it’s best to start preparing your proposal months before the deadline! However, once you have put one proposal together, you can reuse the ideas and the structure for other fellowship applications. Note though that you often have to adjust many components of the fellowship, so it is still a time-consuming process. You should also make sure there is a good match between your research project, your supervisor, and the host institution. If you apply for a fellowship with a different supervisor, you’ll likely have to write a new proposal so that the project matches all the people involved. 

7. Apply for permanent positions
You may think you are not competitive for permanent job positions, but it can be worth a shot! It’s also a good opportunity to identify which skills and experience you currently lack to be competitive, and direct you to opportunities where you can broaden your skillset. For instance, may academic jobs require extensive teaching experience. If you have not done much teaching, perhaps it is a good way to realise you need to get more involved in teaching or even coordinate your own lecture! Applying for permanent positions might also be a good exercise to understand how job applications are structured, so you are ready when your dream job position opens up!

8. Seek out online platforms and resources
A lot of job advertising is happening on social media platforms such as Twitter/X, BlueSky, or LinkedIn. Feel free to let the online community that you are on the hunt for a postdoc on your profile – someone might reach out to you! Some academics have also compiled very useful lists of jobs and fellowships in ecology and evolution. For instance, Dieter Lukas has made this large list of independent postdoc fellowships here; or you can consult this one from Allison Barner here; Corrie Morreau has also put together a list of faculty positions and postdocs in ecology and evolutionary biology here; ERC Central is also a great website listing a lot of funding opportunities for ECRs; and many opportunities in Europe are posted on EURAXESS. There are also some interesting mailing lists that regularly post job opportunities such as Evoldir or ECOLOG-L.

9. Don’t take rejection personally
All people applying for positions are fantastic researchers, and not getting a job or fellowship does not mean you do not have the skills and expertise to carry out exciting research! There are too many people applying, and not enough jobs for everyone. Decisions are also highly subjective, so don’t take those rejections personally. You are amazing, whether you get the job or not! Just make sure to seek out feedback on your application once you have digested the outcome. You might receive some useful advice to make your application stronger next time!

10. Extra notes
There are of course, many additional challenges to finding a job. The common expectation for ECRs to move internationally for short contracts can be extremely difficult financially and mentally, so these are not viable options for everyone. This is even harder if you have to relocate a whole family, have mobility or health issues, or ties to a specific area.

I think it’s important to seek jobs in places you know you will likely enjoy living in, and if moving is not something you wish to do, then perhaps it’s best for you not to! This will make the job hunt a more challenging, but compromising on mental health and quality of life may not always be a great idea. Note that some postdoctoral fellowships do not have geographic restrictions (e.g., the AXA fellowships support research at any institution). These may interesting options if you are restricted geographically!

I have also focused this post on academic jobs, but there are many other amazing jobs outside of academia! As I said earlier, you have already acquired a diversity of skills during your PhD, and these will be valued beyond academia. I’m not very knowledgeable about non-academic jobs, but they are certainly worth exploring! 

5by Losia Lagisz

SORTEE (the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology) annual conference was held on 15-16th of October.  ⭐⭐⭐⭐⭐

By now, I hope, most of our readers should know about SORTEE (it's been around for 4 years). A few of our lab members participated in its creation, and many others joined the team of volunteers who help run the society and participate in its many activities, including the conference. This year was no different, with our lab members facilitating several sessions, especially unconferences and hackathons (the conference program is still available here). 💫💫💫💫💫

All the events were run virtually over 24 hours to include all timezones across the globe. The registered participants came from 35 countries (see the map above), representing higher geographic diversity than ever before (and it was visible during the sessions too!). The conference registration was free for SORTEE members and very cheap for non-members (or free, as needed - no questions asked). The two great plenary talks were recorded, as well as introductory and concluding talks by SORTEE President Rose O'Dea (all recordings, including from the earlier years, are available here). The conference itself went extremely well, with no hiccups, thanks to the fantastic effort and strategic skills of the organising committee, which I cannot praise enough. So thanks again everybody! 💜💜💜💜💜

SORTEE is a non-profit organisation, which strives to be inclusive and serve the scientific community. It relies exclusively on the volunteers. So, if you haven't volunteered in one of the many roles available yet, or wish to do more, you can still nominate yourself (and encourage others to do so) - nominations close October 30th via an online form available here. Have fun, learn new skills, network, and help making science more open! 🧚🧚🧚🧚🧚

I am looking forward for another SORTEE conference, in 2025, and all the different events and activities that will come before that (if you are not a member yet, join SORTEE to participate!). ✨✨✨✨✨✨

by Lorenzo Ricolfi

As a PhD student working on a computer-based project, I spend countless hours sitting at my desk. It’s a common trap: we think that the more time we dedicate to our research, the more progress we make. But in reality, extended periods of physical inactivity can reduce productivity and harm both mental and physical health.
Physical activity isn’t just about staying in shape; it’s a crucial part of maintaining mental sharpness and energy levels. Regular exercise triggers the release of endorphins, serotonin, and dopamine, key hormones that boost mood and cognitive function. Physical activity combats stress, reducing the likelihood of burnout, which is all too common in long academic journeys. On top of that, [a study from north american universities](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041121/#:~:text=Exercise training increased hippocampal volume,neurogenesis in the dentate gyrus.) and published on the journal PNAS showed that physical activity increases hippocampal volume, a part of the brain responsible for memory and learning.

An older study from the University of Bristol published several years ago found that employees who exercised before work or during lunch breaks reported an increase in performance. Positive changes in performance outcomes were almost exclusively linked to changes in mood. For those of us in academia, where problem-solving and creativity are essential, these benefits can't be overlooked.
PhD life in Australia, typically spanning 3.5 years, is a marathon, not a sprint. Like any long-term challenge, pacing is essential. Yes, a PhD is demanding, and there’s always more to do. But pushing personal boundaries on how many hours you can sit at a desk without moving isn’t productive. Studies show that sitting for prolonged periods increases the risk of heart disease, diabetes, and even reduced life expectancy. Even worse, extended desk time leads to a decline in focus and problem-solving abilities, defeating the purpose of staying glued to the chair in the first place.
Anyway, if you really don't want to do medium-intense physical activity, you should at least make time for a walk. In my journey, I’ve learned that stepping away from the desk, whether it’s for a short walk, a workout, or even a stretch, can reignite productivity. You return with fresh eyes, a sharper mind, and often the solution to a problem that had you stumped for hours.
Finding a balance between work and personal health isn’t easy, but it’s crucial. I’m now in the early stages of my final year of PhD, and throughout this journey, regular exercise has been a constant. I currently work out three times a week and make time for at least one long walk on the weekends. The toughest periods in my PhD were when I neglected exercise. I became fully absorbed in research a couple of times, pushing aside essential activities outside academia, and those were the hardest times. Life outside academia matters too, and the body and mind function best when we respect their limits. For those of us navigating the academic world, taking care of our physical health is an investment in both our current well-being and our future success. So, the next time you feel like you’re stuck in the PhD grind, remember that sometimes the best thing you can do is step away, get moving, and let your body, and your mind, recharge.

by Coralie Williams

Last month, I attended the International Statistical Ecology Conference (ISEC) in Swansea, UK. This was my first time attending ISEC, and I was excited as I had heard it was a friendly environment for networking with ecological statisticians and the R developer community.
​
To start off, optional pre-conference workshops were offered. These workshops provided a great way to overcome jetlag, get into the stats/coding rhythm, and meet people in a smaller setting before all the attendees arrived. I often find that the first day of any conference can be overwhelming, so this was particularly beneficial. I attended the workshop on RTMB, a new R package designed to run flexible models with TMB (Template Model Builder) directly in plain R code. We received valuable tips from Anders Nielsen, one of the TMB maintainers, and Ben Bolker, the GLMM guru.

The conference itself was packed with a diverse range of topics, from software development to sophisticated multi-species models. While most presentations focused on animal taxa applications, there were also interesting topics on the modelling fungi distribution with spatially explicit capture-recapture models and models to predict different apple species flowering time. During the poster sessions, I presented my first PhD chapter, which is a meta-research project proposing ways to improve the reporting of simulation studies evaluating statistical methods. I was very pleased with the interest it generated.