by Lorenzo Ricolfi

I'm sitting here at the airport in Edmonton after five weeks in Canada, reflecting on everything I experienced and I felt the need to write it down. So here we are.
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My first time in Canada went by in the blink of an eye. I spent the first three weeks working on my thesis and ongoing projects at the University of Alberta, where Shinichi and his new team kindly welcomed me. I had the chance to grab a couple of beers with Santi and Erick, and we ended up having some deep, fascinating conversations about the future of AI in academia and beyond, mixed in with the occasional lighter banter. Time was limited, but enough to realise they’re both great guys and brilliant scientists. Their minds are open and sharp, the way a scientist’s mind should be.

Edmonton is ok. It reminded me a lot of an average U.S. city: big parking lots, fast food chains everywhere, and footpaths clearly not designed for pedestrians. Still, there were highlights. One day, Shinichi, Yefeng, Toto, and I went for a hike in Elk Island, where we saw a couple of bison from a distance. Man, their heads are massive!
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​​I also learned a lot about Edmonton thanks to a memorable chat with my Uber driver, Nwabueze. He’s a Nigerian guy, a little older than me, who picked me up from the airport. After the usual chit-chat, he told me how he left Nigeria six years ago, not because things were bad, but because he wanted to open his mind and challenge himself. Among many interesting stories, he explained to me how incredibly cold it gets here in winter, how diesel fuel requires antifreeze additives during the colder months, and why so many windshields are cracked (spoiler: they put rocks on the roads to improve traction on ice). “It’s tough during winter,” he said. I believe him.

The University of Alberta was good, although very quiet. After those three weeks of work, my girlfriend and I set off on a road trip from Edmonton to the Rockies (Jasper and Banff NPs), down to Vancouver and Vancouver Island, and then back. About 4,500 km through the wilderness of western Canada.

The Rockies are absolutely stunning. Nature, landscapes, and alpine lakes that really take your breath away. Sadly, a large portion of Jasper burned last summer in a devastating wildfire. Driving and walking through the scorched land, where everything felt lifeless and silent, was surreal. A local in Jasper told us the cause of the fire is still uncertain, possibly a cigarette, lightning, or a mix of both. We spent three days there and hiked a couple of beautiful trails in the areas that hadn't burned. We saw squirrels, chipmunks, and even wild goats.

From Jasper, we took Highway 93 south toward Banff. That drive alone is worth the trip. The beauty is hard to put into words, so here’s a picture to help you imagine it.
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We were lucky enough to spot a couple of American black bears along the way. It was amazing to see them roaming freely in their natural environment, minding their own business. In Banff, we stayed five days and visited the iconic Lake Louise and Moraine Lake. We also ventured into the nearby Yoho and Glacier National Parks. There, we saw elk and what I think was a marmot (but don’t quote me on that).

We did several incredible hikes, always carrying a bell to make noise (so grizzly bears know you're around) and a can of bear spray, which is mandatory for many trails. Unfortunately, or maybe fortunately, we didn’t see any grizzlies. I guess the bell worked.
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After immersing ourselves in the mountains, we headed to Vancouver and Vancouver Island. We had a great impression of Vancouver: good vibes, tasty food, pretty skylines, and hidden gems tucked around the city. To reach Vancouver Island, we took a car ferry that crosses over in a couple of hours. Tofino was the highlight, a little village full of personality.

If I had to point out one downside to the trip, it would be the food. In Canada, decent quality food, what you'd consider average elsewhere, comes at a steep price. Many people resort to fast food a few times a week, and overall, Canada doesn’t really shine in the culinary department. Not that it was a surprise, considering some of the national staples are ketchup Lay’s chips and poutine (French fries with cheese curds and gravy).
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So now it’s time to fly back to Sydney, slightly tired, definitely inspired, and maybe still craving real food. Canada surprised me in many ways, some good, some weird, and all worth it. I’m already looking forward to my next adventure (New Zealand in about 20 days!), but for now, I’ll just sit here with my Tim Hortons coffee and say: thanks, Canada. It was a wild ride.
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by Malgorzata (Losia) Lagisz

This post is inspired by Coralie’s recent blog post, “Meta-analysis terminology can be confusing”, in which she untangles a range of commonly used, misused, and confused terms in meta-analysis—such as subgroup analysis, moderator analysis, meta-regression, fixed-effect vs fixed-effects models, and multivariate.

These certainly warrant clarification. But what about the terminology for the underlying data—could that be just as confusing?
 
What is “meta-data”?

There are many definitions of meta-data (or metadata), but most describe it as “the information that defines and describes data” (ABS). Since information is also a form of data, meta-data itself can have meta-data… which can have more meta-data… and so on. Conversely, a dataset can include meta-data, which itself may include even deeper layers of meta-data. This creates a kind of conceptual circularity that adds to the confusion—especially in the context of meta-analysis (and systematic reviews of all sorts).
 
Does meta-analysis use meta-data?

Yes—but not always in the way people expect.

It is common to assume that meta-data simply refers to the dataset compiled and analyzed in a meta-analysis, especially since both terms contain the prefix “meta” and deal with data from primary studies. As a result, when researchers are asked to share both their data and meta-data, they often upload only the dataset itself. However, in this context, meta-data refers specifically to the description of the dataset: a detailed explanation of the variables, their definitions, units, data structure, etc. But this may also contain some information that can be considered meta-data, contributing to the confusion.

Visualising layers of meta-data in meta-analyses

What counts as “data” or “meta-data” depends on the context (see my diagram above). In a primary empirical study, the data might consist of field or lab measurements of things, humans, or systems, while the meta-data includes descriptions of the variables in that spreadsheet (black parts of the diagram above). 

But once a primary study is published (or shared), it gains another layer of meta-data: title, abstract, publication date, author names, affiliations, etc. This is the meta-data librarians and other information specialists work with (green parts of the diagram above).

In a secondary study, such as a meta-analysis or systematic review, you typically compile not only data of primary studies (selected results and their descriptors), but also some of their meta-data (e.g., study-level characteristics such as study reference, title, authors, journal, DOI, etc.), and then also generate new data for your synthesis (e.g., recalculated effect sizes). The resulting dataset is a layered mix of data and meta-data from different sources and levels.

What to do in practice

In practice, for a meta-analysis (and systematic reviews or other secondary studies) use terminology consistently in the context of your study: call your dataset "data", and description of your dataset "meta-data" (purple/plum, NOT pink, parts of the diagram above). You can still acknowledge that your data contains some meta-data from underlying primary studies (e.g. information describing the publications).
 
Why it matters

Conceptual complexity—and the commonly inconsistent use of terminology—may partially explain why appropriate meta-data is often missing or poorly documented in shared datasets from meta-analyses (and various types of systematic reviews). When people are asked to share meta-data--but they think this is just their dataset (data)--they only share the dataset, without description of all variables (meta-data). But without complete and well-structured meta-data (the descriptions of data), it becomes difficult to interpret the dataset (the data), let alone reuse it or reproduce the analyses. Transparent and clear meta-data (descriptions of data = dataset) is crucial for making meta-analyses truly open and reusable.
 
NOTE:
You can find earlier blog posts from my “Around meta-analysis” series archived on my personal website.

by Coralie Williams
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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!). ✨✨✨✨✨✨