Abstract
Estimation of the impacts of atmospheric nitrogen (N) deposition on ecosystems and biodiversity is a research imperative. Analyses of large-scale spatial gradients, where an observed response is correlated with measured or modelled deposition, have been an important source of evidence. A number of problems beset this approach. For example, if responses are spatially aggregated then treating each location as statistically independent can lead to biased confidence intervals and a greater probably of false positive results. Using methods that account for residual spatial autocorrelation, Pescott & Jitlal (2020) re-analysed two large-scale spatial gradient datasets from Britain where modelled N deposition at 5 × 5 km resolution had been previously correlated with species richness in small quadrats. They found that N deposition effects were weaker than previously demonstrated leading them to conclude that “previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated”. We use a simulation study to show that their conclusion is unreliable despite them recognising that an influential fraction of the residual spatially structured variation could itself be attributable to N deposition. This arises because the covariate used was modelled N deposition at 5 × 5 km resolution leaving open the possibility that measured or modelled N deposition at finer resolutions could explain more variance in the response. Explicitly treating this as spatially auto-correlated error ignores this possibility and leads directly to their unreliable conclusion. We further demonstrate the plausibility of this scenario by showing that significant variation in N deposition at the 1 km square resolution is indeed averaged at 5 × 5 km resolution. Further analyses are required to explore whether estimation of the size of the N deposition effect on plant species richness and other measures of biodiversity is indeed dependent on the accuracy and hence measurement error of the N deposition covariate. Until then the conclusions of Pescott & Jitlal (2020) should be considered premature.
Highlights
Atmospheric nitrogen deposition is one of a number of chronic pressures that arise from human activity (Ackerman, Millet & Chen, 2018; Sala et al, 2000)
The flaw we highlight in P&J20’s reasoning becomes more plausible if N deposition estimates at the 5×5 km square resolution average out significant variation at finer resolutions across Britain. We show that this is the case by analysing the differences in variance of modelled N deposition estimates at the 1 × 1 km versus 5 × 5 km resolutions
As P&J20 point out this is especially important when evidence has a major influence on policy and public expenditure on research. The conclusions from their analyses are undermined by not exploring the effect of measurement error within their model specification
Summary
Atmospheric nitrogen deposition is one of a number of chronic pressures that arise from human activity (Ackerman, Millet & Chen, 2018; Sala et al, 2000). Since nitrogen is an essential macronutrient that is limiting in many ecosystems, unnaturally high levels of deposited N are expected to cause a range of ecological effects (Stevens et al, 2011; RoTAP, 2012; Phoenix et al, 2012). These effects are modified by factors such as livestock grazing (Van der Wal et al, 2003), historical sulphur deposition (RoTAP, 2012; Rose et al, 2016), soil pH (Van Den Berg et al, 2011; Van Den Berg et al, 2005), P limitation (Rowe, Smart & Emmett, 2014) and species identity (Van Den Berg et al, 2005; Sheppard et al, 2014). A strength of these spatial gradient studies is their realism but at the cost of uncertainty in attributing cause to effect (Smart et al, 2012)
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