Development of soil health benchmarks for managed and semi-natural landscapes.

Efforts to improve soil health require that target values of key soil properties are established. No agreed targets exist but providing population data as benchmarks is a useful step to standardise soil health comparison between landscapes. We exploited nationally representative topsoil (0-15 cm) measurements to derive soil health benchmarks for managed and semi-natural environments across Great Britain. In total, 4587 soil organic matter (SOM), 3860 pH, 2908 bulk density (BD), and 465 earthworm abundance (EA) datapoints were used. As soil properties are sensitive to site-specific characteristics, data were stratified by habitat, soil type, and mean annual precipitation, with benchmarks defined as the middle 80 % of values in each distribution - yielding 135 benchmarks. BD and pH decreased with land management intensity (agriculture > semi-natural grasslands > woodlands > heathlands > wetlands), and vice versa for SOM and EA. Normalising benchmark ranges by medians revealed soil health indicator benchmark widths increased in the order: pH < BD < SOM < EA, while width increased with decreasing land management intensity. Arable and horticulture and improved grassland exhibited narrow benchmarks for SOM, pH and BD, yet the widest EA benchmark, suggesting additional drivers impact EA patterns. Upland wetlands had the widest BD benchmarks, important when determining carbon stocks. East Anglia currently possesses the largest proportions of atypical soils, including below typical SOM (19.2 %), above typical BD (17.4 %) and pH (39.1 %), and the smallest proportions of above typical SOM (2.4 %), and below typical BD (5.8 %) and pH (2.3 %). This is found even after land use, soil type and rainfall have been considered, underscoring how urgently soil health should be addressed here. Our benchmarking framework allows landowners to compare where their measured soil health indicators fall within expected ranges and is applicable to other biomes, national and multinational contexts.

Comment on Pescott & Jitlal 2020: Failure to account for measurement error undermines their conclusion of a weak impact of nitrogen deposition on plant species richness.

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.

Biotic homogenization and changes in species diversity across human-modified ecosystems.

Changing land use and the spread of 'winning' native or exotic plants are expected to lead to biotic homogenization (BH), in which previously distinct plant communities become progressively more similar. In parallel, many ecosystems have recently seen increases in local species (alpha-) diversity, yet gamma-diversity has continued to decline at larger scales. Using national ecological surveillance data for Great Britain, we quantify relationships between change in alpha-diversity and between-habitat homogenizations at two levels of organization: species composition and plant functional traits. Across Britain both increases and decreases in alpha-diversity were observed in small random sampling plots (10-200m2) located within a national random sample of 1km square regions. As alpha-diversity declined (spatially in 1978 or temporally between 1978 and 1998), plant communities became functionally more similar, but species-compositional similarity declined. Thus, different communities converged on a narrower range of winning trait syndromes, but species identities remained historically contingent, differentiating a mosaic of residual species-poor habitat patches within each 1km square. The reverse trends in beta-diversity occurred where alpha-diversity increased. When impacted by the same type and intensity of environmental change, directions of change in alpha-diversity are likely to depend upon differences in starting productivity and disturbance. This is one reason why local diversity change and BH across habitats are not likely to be consistently coupled.