Post provided by: Brian Buma
In this post, Brian Buma discusses a unifying framework for studying disturbance ecology, from largescale wildfires to bacterial colonies, as proposed in the new paper “Disturbance ecology and the problem of n = 1: A proposed framework for unifying disturbance ecology studies to address theory across multiple ecological systems” recently published in Methods in Ecology and Evolution.
Disturbances are quite common in the world and integral parts of most ecological systems. Wildfires, hurricanes, tornados, avalanches, and other extreme weather events are all naturally occurring phenomena that create habitats, recycle nutrients, and maintain ecosystems. These events happen at non-human scales too; a boot print can be a major disturbance for a desert biocrust community and, at the bacterial level, heat and chemical changes cause very similar ecosystem dynamics. These disturbances not only serve as important ecosystem events in and of themselves but can also trigger important long-term changes, like species migration in our changing climate. Of course, we are rapidly changing the frequency and intensity of disturbances around the world via anthropogenic climate change. Wildfires in the US West, heat waves in Europe, and other highly visible events are obvious examples, but many broad-scale studies have found statistical evidence of changes in disturbance frequencies, magnitudes, and impacts.
No two disturbances are the same
The study of disturbances is critical for anticipating the impacts of these events on ecosystems and people – from answering questions about resilience and recovery, to how species might use disturbed areas to hopscotch into cooler climates as an adaptation strategy. This field of study has been around for decades, but really grew in the late 1980’s with events like the Yellowstone Fires (in 1988) and a growing recognition of the role of disturbances in landscape dynamics. From there, it has expanded to encompass new disciplines such as paleoecology and incorporate diverse techniques including bacterial microcosm studies. As new disturbance types emerge, or old disturbances appear in new places, having a generalized theory of disturbance ecology – a framework by which we have a starting point for expectations regardless of system – is more important than ever.
But there has always been a fundamental challenge to generalizing the lessons from disturbance studies. One could think of it as a problem of sample size. Most studies are limited to single events, especially studies of human-scaled disturbances like fires or hurricanes or environmental accidents. Disturbances, by their nature, are idiosyncratic and replication is not only difficult, it is often impossible. One fire is not necessarily comparable to the last and things like differences in weather conditions, topography, and time since last disturbance all vary continuously over landscapes. No hurricane can hit the same coastline twice.
This means that, statistically speaking, most studies that we write up are more akin to case studies at the disturbance-scale than independently replicated work. This makes finding and testing fundamental patterns difficult as too many things are uncontrolled by necessity.
Frameworks and methods for studying disturbances
That hasn’t stopped us from developing conceptual frameworks and excellent work has been produced in an effort to generalize disturbance ecology. Good examples include broad frameworks and concise, equation-based formulations (and more before and since). But cross system comparisons are difficult, because every group of scientists (e.g. coral researchers vs. grassland researchers vs. urban researchers) tend to use their own unique terminologies.
Remote sensing offers an amazing view of multiple events in a replicable way, like Landsat monitoring of fires, but we’re limited in the questions we can ask remotely by scale and viewpoint and work still needs to be tied to the ground measurements. Standardized datasets that are collected at very broad scales, like the US Forest Service’s Forest Inventory and Analysis program, can be used but they are very limited worldwide and some avoid disturbed areas. Community driven collaborations are extremely valuable and are becoming more common, with calls for contributed datasets and a subsequent broad-scale analysis of all the data. But those are limited to questions with fairly common datasets, often in areas that are well studied (that’s why there is data there!), and there is always the underlying challenge of uniting data collected for differing purposes. Paleoecology can study multiple events in a single spot, but over deep time, with all its variation in climate and imprecision with proxies.
Unifying disturbance ecology
In the new paper “Disturbance ecology and the problem of n = 1: A proposed framework for unifying disturbance ecology studies to address theory across multiple ecological systems“, I want to open a conversation about broadening the scale of our focus from disturbance studies to the field of disturbance ecology as a whole. The idea is to intentionality coordinate cross-disciplinary efforts that proactively address theoretical issues ahead of time. The goal is theory-based data conversations across disparate systems, from forests to coral reefs and bacterial communities.
Using prior formulations of theory, I propose a few minimal descriptive criteria – the baseline that would need to be written down for each and every study, regardless of system – criteria which stick different systems together into a common framework. These are simple, non-onerous measures based in current theory, because all studies will at their core be unique and obviously any add-on at the study level must be non-intrusive to be adopted. They are also relativized, meaning one can directly compare each criterion across systems, from wildfires to bacterial colonies. The focus of these is not answering any particular question per se, but to create a coordinated set of data that will allow future researchers to design good cross-discipline questions and have the data with which to test theoretical expectations of disturbance ecology directly.
At its core, this is a call to the discipline to think bigger. Another case study of forest regeneration after the latest fire may be useful, of course. Another experiment into bacterial community response to mortality can be useful. But if we think at a higher level, at the level of dynamics and dynamism, we can do more with those studies. Here, I simply propose to do that with intentionality. The metrics here may not be the best or optimal (though that was of course the hope!), after all, this is really a discipline-level task. Some may argue that generalization is not necessary, or plain impossible. That’s fine– this paper is meant to spur that conversation, to get disturbance ecologists thinking about what unites very different ecological systems in terms of their disturbance-driven dynamics, and how we might attack those questions directly and with intentionality.
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