Tiago Marques, a researcher at Ciências, is part of an international study that proposes rethinking the teaching of statistics to ecologists

Ecological research today produces unprecedented quantities and types of data, given the new technologies used, such as drones, GPS, sensors, cameras and genetic sequencing. But is the training of future ecologists prepared for this new reality?

A new article published in the journal Methods in Ecology and Evolution argues that it is urgent to rethink the teaching of statistics and quantitative methods in Ecology, prioritising the understanding of fundamental principles rather than the learning of specific procedures.

Among the authors of the paper is Tiago Marques, a researcher and lecturer at Ciências ULisboa, who has been promoting this discussion within the international community for several years.

What is the main question this study seeks to answer, and why is it important to rethink how statistics and quantitative methods are taught to ecologists?

There is broad consensus on the need to reshape how statistics is taught to ecologists, and this notion is surprisingly old. The main question we address is not only to draw attention to the issue once again, but to identify concretely the main reasons why, despite the problem being recognised, it remains unresolved. And there is not just one reason for the lack of progress, many of the reasons are transversal, and we cannot attempt to tackle the problem without considering this diversity. But the need to change things is obvious. The modern ecologist requires a set of quantitative skills to carry out their work, skills that are not provided during their training in the vast majority of university programmes. If this was already true 100 years ago, today this need is pressing. The rapid development of new technologies, such as GPS, drones, hydrophones, microphones, video cameras, genetic sequencing, satellite imagery, among many others, used to measure, record and assess ecological parameters, and which generate a potentially overwhelming quantity and variety of data, has contributed to making many traditional data‑processing and analytical methodologies obsolete. And this is a global reality. This article aims to serve as a new starting point, a global wake‑up call - hence its publication in what is possibly the most important journal on methodologies in Ecology and Evolution - by identifying the factors involved and providing concrete tools for lecturers working in these areas to begin changing the landscape.

What are the main limitations of the current training model identified by the study, and what should change in the preparation of future ecologists?

I believe that, beyond the inability of current approaches and methodologies to deal with the quantity and diversity of data I mentioned above, the main limitation of the current teaching model is its excessive focus on teaching specific procedures and how to implement them, whether a t‑test, an analysis of variance or a regression analysis, instead of addressing general principles and how to use them to solve new, less conventional or less traditional situations. These basic principles include variability, randomness, replication, confounding, the difference between correlation and causation, the difference between statistical significance and ecological relevance, among others. This is because, in fact, the world has changed and continues to change so quickly that it is very difficult to teach students to deal with concrete, predefined cases; we must help them handle new situations that may arise. Nowadays, analytical problems are rarely textbook cases, and researchers must think about the question at hand, its specificities, and develop dedicated approaches. For that, a deep understanding of basic principles is essential. Knowing how to execute a set, even a broad one, of methodologies for situations that exist only in textbooks is not enough. It is necessary to have a versatile set of tools and a solid grasp of basic principles in order to develop the required solutions in new situations.

The study involves researchers from different countries and institutions. What was your contribution to this work, and how did your participation in the project come about?

In reality, this is the culmination of a project to which I have devoted considerable time, and which began spontaneously almost at the same time I started teaching statistics to biologists/ecologists at Ciências nearly a decade ago. From the outset, it was clear that what I taught, or could teach, was far from what would be important to do, but that introducing such change would not be easy. I began promoting this discussion within the international ecological statistics community, with successive round tables dedicated to the topic at ISEC (International Statistical Ecology Conference). Finally, in Swansea (2024), the first author of the article, William Kay (Cardiff University), took part in the discussion and brought a profound shift to it. For the first time, we had someone among us who proposed to make this issue the object of his research, and not merely a latent concern. And that is how this article was born, with several other spinoffs currently in development, following an earlier one from 2024, “Teaching Statistics‑Savvy Ecologists”, which, due to its importance, received an award from The Wildlife Society in the United States. It is paradigmatic that an article on the importance of teaching quantitative aspects to ecologists, and not something about ecology itself, was chosen as the best contributed article of last year in The Wildlife Professional. It is a recognition, by those to whom this discussion ultimately concerns, of how this debate is more important than ever.

What impact could this shift in training in quantitative methods have on the way ecological research is carried out and on the decisions that result from it?

The impact of this change, if we manage to implement it, has the potential to be absolutely transformative. In ecological research, data processing, handling and analysis are often critical factors that determine the ability to obtain and publish results, in a way that is far more fundamental than the data collection itself. There are still many cases like those Fisher immortalised with his, loosely translated from a famous phrase that has undergone countless variations - “Often, when they come to me for a diagnosis after the data have been collected, I can do no more than issue a death certificate.” I have encountered many such situations: studies that could have been very interesting if there had been, sometimes, just a small difference in how the data were collected. This would have been the case had the fundamental concepts mentioned above been taken into account, if the concepts had been the foundations, instead of what we currently have, which tends to be little more than a dumping of methodologies that can be applied if conditions X, Y and Z are met, based on their narrow assumptions. The problem is that in real life we rarely find ourselves in those ideal examples; we are almost always dealing with special cases for which traditional recipes do not work. If we change the way we teach statistics to ecologists, giving them the general principles, the fundamental concepts, and how to use those concepts to approach new questions, they will be far better prepared for the challenges they will face in their future careers.

If you had to summarise the main message of this work for a Biology student who is just beginning to learn statistics, what would it be?

The main message of this work for someone who is just beginning to learn statistics may be somewhat discouraging, because it recognises that what we teach them is not truly what they will need later on. A working group has been created in the Department of Biology, in coordination with colleagues from the Department of Mathematical Sciences, to take stock of what we currently have at Ciências and to try to integrate, coordinate and improve the teaching of quantitative content to our students. In that respect, I believe Ciências is well positioned even within a demanding international landscape, although we are no longer in time to directly help those who are currently taking the degree. On the other hand, it can also be a warning message, a call to action, a suggestion that there is indeed a huge opportunity here for those who wish to invest in this area beyond what is taught in the classroom. Statistics will almost certainly be fundamental in their future work, and as such, the course units that address quantitative components, such as Mathematics for Biologists, Introduction to Data Handling, Biostatistics and Numerical Ecology, will very likely be essential for their future paths, even if that is not obvious to them when they are taking those units. They should therefore be approached as seriously as, if not more seriously than, the course units in the specific domains of biology and ecology. Going further into the topics covered by these units will make the students who do so highly sought‑after in terms of future opportunities, whether to work in a research group, pursue a master’s degree, a PhD, or even leave academia and enter the job market. Statistics is a highly transferable skill, transversal across knowledge, increasingly present in all kinds of professions in our data and algorithm‑driven society. The growing diversification of data types and the analyses required to deal with them mean that, in most careers, and in ecology in particular, mastering quantitative aspects is still rare enough that students who achieve it will have a decisive competitive advantage over their peers in terms of the opportunities they will be able to seize.