View from the editor's desk: From code to credit
Citing software and crediting original methods in the age of install.packages()
By Pedro Peres-Neto
Concordia University, Canada
Editor-in-Chief, Oikos
The gist
In ecology, citing software packages has become routine; and that’s a good thing. But too often, those citations stop at the package level, with little or no acknowledgment of the original scientific work that motivated, developed, or formalized the methods implemented in those tools. This is not about citing the original t-test, ANOVA, or PCA every time we use them. It’s about good knowledge governance: giving proper credit to genuine intellectual and creative contributions and providing readers with enough context to understand what a tool actually does.
The solution is simple. Users should better understand the distinction between a tool and the ideas it implements and take responsibility for citing both appropriately. For good measure, when a function is used, it could print a short message identifying the key references, essentially saying: “cite the package and cite these original papers.”
result <- fancyModel(y ~ x + z, data = mydata) “You are using fancyModel() from the ecoFancy package. Please cite: - Doe & Smith (2018): foundational description of the methodological framework – Roe et al. (2021): extension of the framework implemented in this function – ecoFancy package (v1.4.2) for the software. Thank you for supporting good scientific credit.
The longer answer
Let me start by saying this clearly: citing packages is essential! Package authors invest enormous time and expertise into building, maintaining, documenting, and supporting tools that the entire community relies on. Package citations are one of the few ways these contributions receive formal recognition, and they absolutely matter.
But there is a growing pattern that makes me uneasy. In many ecological papers, methods are now described almost entirely as a list of packages and functions within them without any reference to the original methodological papers that motivated, developed, or justified the approach in the first place. In some cases, readers are left with no real explanation of what the method does conceptually, only which function was called.
This isn’t about nostalgia from the times before packages, and it’s not about gatekeeping citations. It’s about how we treat methods as scientific knowledge and recognizing the intellectual work and creativity of the researchers who created them.
I should also say that I don’t come to this from a neutral position. These reflections sit at the intersection of my quantitative work, my involvement in developing packages, and my editorial experience. And, like many of us, I’m not perfect; I sometimes miss references that deserved to be cited.
Packages are implementations, not origins
Most packages are not just collections of code; they are implementations of ideas that emerged through earlier theoretical, statistical, or conceptual work. Often, those ideas were developed, tested, debated, and refined in the peer reviewed literature long before they were wrapped into a convenient function and related packages. Many package developers are not the researchers that introduced the original methodological developments the package implements.
When we cite only the package, a lot of intellectual history gets compressed into a single line of software citation. What started as a careful idea designed to solve a specific problem can end up looking like it simply appeared as a function. Over time, that makes the original work less visible and muddies the difference between developing an idea and coding it. That not only makes the original work less visible and undervalues its contribution but also misses a chance to encourage users to engage more deeply with the method itself.
Again, I’m not arguing that every use of a method requires tracing its full historical genealogy. We do not and should not cite Student every time we run a t-test or Pearson for Principal Component Analysis (PCA) and chi-square. Many tools are so foundational that they have become part of the shared statistical language.
But many methods used in modern ecology and other fields are not at that stage. They are relatively recent (< 40 years), actively evolving, and often tightly linked to specific assumptions, data structures, or inferential goals. When methods are treated primarily as black-box software rather than as research contributions, both authors and readers lose important context.
Description matters as much as citation
There is a second, related issue: minimal methodological description. Increasingly, methods sections read like this: “We analyzed the data using package X with function Y.” That tells us very little.
What assumptions does the method make? What is it estimating? What trade-offs does it involve? Why is it appropriate for this question and this dataset? These are scientific questions, not software questions, and they deserve to be addressed in the manuscript.
When methodological description is replaced by software citation alone, the method becomes opaque. Reviewers struggle to evaluate it, readers struggle to interpret results, and the work becomes harder to build on. Reproducibility could be thought as not just about rerunning code; it’s about understanding what the code is doing.
This is not an argument against open tools
To be absolutely clear, none of this is a critique of open-source software or the move toward reproducible workflows. Quite the opposite. Open tools have transformed ecology for the better, and package authors deserve substantial credit for that. But openness also comes with responsibility. A healthy knowledge ecosystem distinguishes between ideas, implementations, and applications, giving each appropriate recognition.
Why this matters for the community
Credit shapes careers. Citations influence hiring, promotion, and funding decisions. When original methodological work is systematically under-cited because its ideas are now “hidden” inside packages, we quietly reshape incentives away from conceptual and theoretical development; and may also reduce incentives for researchers to invest time in developing new methods in the first place.
It also shapes how methods evolve. If ideas are no longer visible as ideas and only as code, they become harder to critique, adapt, or extend. Ecology risks drifting toward a toolbox culture where methods are chosen because they exist, not because they are understood.
So, what’s a reasonable way forward?
This doesn’t require rigid rules. It requires judgment.
When a method is recent, specialized, or conceptually non-trivial, cite both the package and the key paper(s) that introduced or formalized the approach. When a method is central to the paper’s contribution, explain it in words, not just in code. When in doubt, ask: would a reader unfamiliar with this tool understand what we did and why?
Good governance of knowledge is not about inflating reference lists. It’s about maintaining a clear link between ideas, tools, and evidence. Packages are powerful tools for research, but they shouldn’t become substitutes for credit or explanation.
As with many things in publishing, this is less about enforcement and more about common sense and some norms. If we want a community that values both open tools and intellectual contributions, we need to be deliberate about how we acknowledge both.
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