Extending the climatological concept of ‘Detection and Attribution’ to global change ecology in the Anthropocene

Abstract

Research into global change ecology is motivated by the need to understand the role of humans in changing biotic systems. Mechanistic understanding of ecological responses requires the separation of different climatic parameters and processes that often operate on diverse spatiotemporal scales. Yet most environmental studies do not distinguish the effects of internal climate variability from those caused by external, natural (e.g. volcanic, solar, orbital) or anthropogenic (e.g. greenhouse gases, ozone, aerosols, land‐use) forcing factors. We suggest extending the climatological concept of ‘Detection and Attribution’ (DA) to unravel abiotic drivers of ecological dynamics in the Anthropocene. We therefore apply DA to quantify the relative roles of natural versus industrial temperature change on elevational shifts in the outbreak epicentres of the larch budmoth (LBM; Zeiraphera diniana or griseana Gn.); the classic example of a cyclic forest defoliating insect. Our case study shows that anthropogenic warming shifts the epicentre of travelling LBM waves upward, which disrupts the intensity of population outbreaks that occurred regularly over the past millennium in the European Alps. Our findings demonstrate the ability of DA to detect ecological responses beyond internal system variability, to attribute them to specific external climate forcing factors and to identify climate‐induced ecological tipping points. In order to implement the climatological concept of ‘Detection and Attribution’ successfully into modern global change ecology, future studies should combine high‐resolution paleoenvironmental reconstructions and state‐of‐the‐art climate model simulations to inform inference‐based ecosystem models. A free Plain Language Summary can be found within the Supporting Information of this article.

Plants in the UK flower a month earlier under recent warming

“…While the impact of rising mean temperatures and associated climatic extremes can be manifest in distinct environmental responses and societal consequences [3,4], the effects of long-term climate change on the functioning and productivity of biological, ecological and agricultural systems are often subtle, thus difficult to recognize and quantify [5]. This is particularly true in ecological research where the climatological concept of ‘detection and attribution’ has only recently been applied [6,7], as well as for farmers, policymakers and the wider public since statistical significance alone often remains an abstract dimension.…”

A case for associational resistance: Apparent support for the stress gradient hypothesis varies with study system

“…In the context of environmental stress such as climate change, shifting organismal interactions have a greater role in population declines and extinctions than physiological climatic tolerances (Cahill et al, 2013; Ockendon et al, 2014). Understanding the nature of how organismal interactions shift is critically important for successful conservation and ecological management, especially because global stress is accelerating at this stage in the Anthropocene (Allen et al, 2015; Büntgen et al, 2020; Hodson & Marvin, 2010; Newman, 2019; Schimel et al, 2013).…”

A general framework to quantify and compare ecological impacts under temporal dynamics

“…This is likely a common situation as anthropogenic stressors such as climate change, habitat loss, pollution and alien species frequently co-occur (Geary et al, 2019). In such situations, snapshot comparisons may be misleading (Büntgen et al, 2020;Didham et al, 2020;McCain et al, 2016;White, 2019): if the population was already decreasing without the new driver, its negative impact would be overestimated (Figure 1b). If the population was increasing, the new driver might be wrongly inferred as having a positive impact (Figure 1c,d); likewise, a positive impact caused by the implementation of a driver mitigation measure could be wrongly inferred as negative if the population was decreasing (Figure 1e).…”

“…Importantly, impact quantification must account for the temporal dynamics of the system (e.g. natural variability and long-term temporal trajectories) to accurately quantify impacts and avoid biases (Büntgen et al, 2020;Christie et al, 2019;Dee et al, 2023;McCain et al, 2016;Wauchope et al, 2021) and to capture temporal variation in impacts (Jackson et al, 2021;Ryo et al, 2019). However, due to limited resources and the need for rapid assessments, many ecological impacts are still measured by comparing static snapshots of biodiversity either between impacted and unimpacted situations or before/ after the driver had an effect.…”