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Aim: Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are sparse, and contain few observations of rare species but a relatively large number of non‐detection observations (a problem known as class imbalance). Robinson et al. (Diversity and Distributions, 24, 460) proposed a method for under‐sampling non‐detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under‐sampling data removes information. We tested whether spatially stratified under‐sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location: Island of Ireland. Methods: We tested the spatially stratified under‐sampling method of Robinson et al. (Diversity and Distributions, 24, 460) by using biological records to train species distribution models of rare millipedes. Results: Using spatially stratified under‐sampled data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The spatial pattern of under‐sampling affected model performance. Training data that was under‐sampled in a spatially stratified way sometimes produced worse models than did data that was under‐sampled in an unstratified way. Geographic coordinates were as good as or better than environmental variables for predicting distributions of one out of six species. Main Conclusions: Spatially stratified under‐sampling improved prediction performance of species distribution models for rare millipedes. Spatially stratified under‐sampling was most effective for rarer species, although unstratified under‐sampling was sometimes more effective. The good prediction performance of models using geographic coordinates is promising for modelling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence. [ABSTRACT FROM AUTHOR]

To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite‐derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field‐plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small‐scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely‐sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices. [ABSTRACT FROM AUTHOR]

The impact of productivity on species diversity is often studied at small spatial scales and without taking additional environmental factors into account. Focusing on small spatial scales removes important regional scale effects, such as the role of land cover heterogeneity. Here, we use a regional spatial scale (10 km square) to establish the relationship between productivity and vascular plant species richness across the island of Ireland that takes into account variation in land cover. We used generalized additive mixed effects models to relate species richness, estimated from biological records, to plant productivity. Productivity was quantified by the satellite-derived enhanced vegetation index. The productivity-diversity relationship was fitted for three land cover types: pasture-dominated, heterogeneous, and non-pasture-dominated landscapes. We find that species richness decreases with increasing productivity, especially at higher productivity levels. This decreasing relationship appears to be driven by pasture-dominated areas. The relationship between species richness and heterogeneity in productivity (both spatial and temporal) varies with land cover. Our results suggest that the impact of pasture on species richness extends beyond field level. The effect of human modified landscapes, therefore, is important to consider when investigating classical ecological relationships, particularly at the wider landscape scale. [ABSTRACT FROM AUTHOR]