Why I Reach for Mixed Effects Before Anything Else
A working note on modeling genotype-linked monitoring data without overpromising what the data can tell you.
Most of the modeling questions that land on my desk look something like this. There are sites. Within sites there are individuals, or colonies, or quadrats. Within those there are repeated measurements over time. Someone wants to know whether a treatment, or a genotype, or an environmental variable, is doing something interesting. The dataset is not huge. The structure is hierarchical. The measurements are correlated within unit. Welcome to almost every monitoring program I have ever worked with.
When this is the shape of the problem, I almost always reach for a mixed-effects model first. Not because it is the most sophisticated tool available, but because it handles the structure of the data honestly, and honesty is the entire point.
A common failure mode I see, particularly from analysts who came up through machine learning rather than ecology or biostatistics, is treating monitoring data as if every row is independent. Stack the rows, throw them into a regression, report a p-value. The model converges, the effect is significant, everyone is happy until you realize that significant was driven entirely by the fact that one site contributed half the rows. Repeated measurements within a colony are not independent. Multiple colonies within a site are not independent. Multiple sites within a region are not independent. If you ignore that nesting, your standard errors are wrong, your confidence intervals are wrong, and you will overstate effects that are really just structural correlation. This is not a controversial point. It has been the standard view in biostatistics for a long time. It just gets ignored a lot in applied work because it is inconvenient. A mixed-effects model lets you write the structure into the equation. Fixed effects for the things you care about, random effects for the levels of nesting you have to account for. That is it. The math is doing real work for you, and the work it is doing is exactly the work that needs doing.
Where this gets concrete for me is genotype-linked monitoring. In coral restoration, you often have multiple genotypes outplanted across multiple sites, measured repeatedly over time. The question is something like, do certain genotypes perform better than others, and is that performance consistent across sites. That question has a specific structure. Genotype is a fixed effect, because you care about its effect specifically. Site is a random effect, because you are not interested in these particular sites, you are interested in generalizing across sites. Time is fixed if you care about a temporal trend, often modeled as a smooth term if the trend is nonlinear. The colony itself is a random effect nested within site and crossed with time. This is not a clever model. It is the simplest model that respects the structure. And in my experience, when you fit it carefully, it gives you exactly the kind of answer the program actually needs. Which genotypes are doing well on average, how much that varies across sites, and how much of the total variation is just noise within colonies. The estimates are usually less dramatic than the estimates from a naive analysis. That is a feature, not a bug. The naive analysis was overconfident. The mixed-effects analysis is showing you what you actually know.
There are checks I run every single time, and I think the field would be in a better place if more analysts ran them out loud, in writing, in their reports. I always look at the random effect variances. If the site-level variance is huge relative to the residual variance, that is a story about the data, and it deserves attention before I report any fixed effects. If the colony-level variance is near zero, the model is telling me that the within-site, between-colony differences are not really there, which has biological implications worth thinking about. I always plot the residuals against fitted values, and against time, and against site. Patterns there usually mean I have missed something structural, like a nonlinear trend or a heteroscedastic error. I always fit at least one alternative model and compare. If the conclusions are robust to reasonable alternative specifications, I am more comfortable. If they flip when I change the random effects structure, I do not trust the result and I say so. I look at convergence warnings carefully. Mixed models in lme4 or glmmTMB will sometimes converge with warnings that look minor and are not. A singular fit usually means you have asked the model to estimate a variance component that the data cannot really estimate, and the right response is to simplify the model, not to ignore the warning.
For anything where the dataset is small, the structure is messy, or the stakeholders need an interpretable statement of uncertainty, I will often refit the model in a Bayesian framework, usually with brms or rstanarm. The reason is not that Bayesian is fancier. It is that the output, posterior distributions for every parameter, is much easier to translate into the kinds of statements that program managers and grant agencies actually want. Saying that there is an 88 percent posterior probability that genotype A outperforms genotype B at the average site is a defensible, useful statement. Saying that the p-value was 0.04 is technically correct and almost never useful. The Bayesian fit is more work, and the priors are a real choice that needs to be defended, but the communication payoff is large.
The thing I want field scientists and program managers to know is this. A mixed-effects model is not a flex. It is the floor. It is what respecting your own data structure looks like. If someone is analyzing your monitoring data and they have not at least considered the nested structure, the analysis is not done yet. You are entitled to ask. That single question, what is the random effects structure, will tell you most of what you need to know about whether the analysis is going to hold up. The good ones welcome it. The bad ones change the subject.
