Holistic conservation of ecosystem services (ES) requires a greater understanding of how the interactions of biotic and abiotic aspects of nature provide them. Currently, geodiversity, the diversity of geology, geomorphology, sediments and soils and hydrology, as well as the services that they provide in isolation of interactions with biotic nature – geosystem services (GS) – are overlooked in ES literature and frameworks. Here, we provide a series of three nested frameworks which together help to provide clarity for both the theoretical role of geodiversity in service production as well as the basis for real-world management strategies. First, we present the ‘Geodiversity Flower’, a framework that can be operationalised to provide clarity in terminology to decision-makers. Second, we present the ‘Geo-Eco Services Framework’, which establishes the difference between ES and GS. The final framework presented is the ‘Geo-Eco Services Cascade Model’, which builds upon the widely used ES cascade model by demonstrating how geodiversity interacts with biotic nature to simultaneously provide ES and GS. Providing a holistic model that integrates both biotic and abiotic nature alongside ES and GS allows for a greater understanding of the roles of abiotic and biotic nature to services and their associated benefits and values to people.

Polar marine ecosystems are particularly vulnerable to the effects of climate change. Warming temperatures, freshening seawater, and disruption to sea‐ice formation potentially all have cascading effects on food webs. New approaches are needed to better understand spatiotemporal interactions among biogeochemical processes at the base of Southern Ocean food webs. In marine systems, isoscapes (models of the spatial variation in the stable isotopic composition) of carbon and nitrogen have proven useful in identifying spatial variation in a range of biogeochemical processes, such as nutrient utilization by phytoplankton. Isoscapes provide a baseline for interpreting stable isotope compositions of higher trophic level animals in movement, migration, and diet research. Here, we produce carbon and nitrogen isoscapes across the entire Southern Ocean (>40°S) using surface particulate organic matter isotope data, collected over the past 50 years. We use Integrated Nested Laplace Approximation‐based approaches to predict mean annual isoscapes and four seasonal isoscapes using a suite of environmental data as predictor variables. Clear spatial gradients in δ¹³C and δ¹⁵N values were predicted across the Southern Ocean, consistent with previous statistical and mechanistic views of isotopic variability in this region. We identify strong seasonal variability in both carbon and nitrogen isoscapes, with key implications for the use of static or annual average isoscape baselines in animal studies attempting to document seasonal migratory or foraging behaviors.

Spatial models of variation in the isotopic composition of structural nutrients across habitats (isoscapes) offer information on physical, biogeochemical and anthropogenic processes occurring across space, and provide a tool for retrospective assignment of animals or animal products to their foraging area or geographic origin. The isotopic differences among reference samples used to construct isoscapes may vary spatially and according to non‐spatial terms (e.g. sampling date, or among individual or species effects). Partitioning variance between spatially dependent and spatially independent terms is a critical but overlooked aspect of isoscape creation with important consequences for the design of studies collecting reference data for isoscape creation and the accuracy and precision of isoscape models. We introduce the use of integrated nested Laplace approximation (INLA) to construct isoscape models. Integrated nested Laplace approximation provides a computationally efficient framework to construct spatial models of isotopic variability explicitly addressing additional variation introduced by including multiple reference species (or other recognized sources of variance). We present carbon, nitrogen and sulphur isoscape models extending over c. 1 million km² of the UK shelf seas. Models were built using seven different species of jellyfish as spatial reference data and a suite of environmental correlates. Compared to alternative isoscape prediction methods, INLA‐spatial isotope models show high spatial precision and reduced variance. We briefly discuss the likely biogeochemical explanations for the observed spatial isotope distributions. We show for the first time that sulphur isotopes display systematic spatial variation across open marine shelf seas and may therefore be a useful additional tool for marine spatial ecology. The INLA technique provides a promising tool for generating isoscape models and associated uncertainty surfaces where reference data are accompanied by multiple, quantifiable sources of uncertainty.

A key aim of ecology is to understand the drivers of ecological patterns, so that we can accurately predict the effects of global environmental change. However, in many cases, predictors are measured at a finer resolution than the ecological response. We therefore require data aggregation methods that avoid loss of information on fine‐grain heterogeneity. We present a data aggregation method that, unlike current approaches, reduces the loss of information on fine‐grain spatial structure in environmental heterogeneity for use with coarse‐grain ecological datasets. Our method contains three steps: (a) define analysis scales (predictor grain, response grain, scale‐of‐effect); (b) use a moving window to calculate a measure of variability in environment (predictor grain) at the process‐relevant scale (scale‐of‐effect); and (c) aggregate the moving window calculations to the coarsest resolution (response grain). We show the theoretical basis for our method using simulated landscapes and the practical utility with a case study. Our method is available as the grainchanger r package. The simulations show that information about spatial structure is captured that would have been lost using a direct aggregation approach, and that our method is particularly useful in landscapes with spatial autocorrelation in the environmental predictor variable (e.g. fragmented landscapes) and when the scale‐of‐effect is small relative to the response grain. We use our data aggregation method to find the appropriate scale‐of‐effect of land cover diversity on Eurasian jay Garrulus glandarius abundance in the UK. We then model the interactive effect of land cover heterogeneity and temperature on G. glandarius abundance. Our method enables us quantify this interaction despite the different scales at which these factors influence G. glandarius abundance. Our data aggregation method allows us to integrate variables that act at varying scales into one model with limited loss of information, which has wide applicability for spatial analyses beyond the specific ecological context considered here. Key ecological applications include being able to estimate the interactive effect of drivers that vary at different scales (such as climate and land cover), and to systematically examine the scale dependence of the effects of environmental heterogeneity in combination with the effects of climate change on biodiversity.

Water point mapping databases, generated through surveys of water sources such as wells and boreholes, are now available in many low and middle income countries, but often suffer from incomplete coverage. To address the partial coverage in such databases and gain insights into spatial patterns of water resource use, this study investigated the use of a maximum entropy (MaxEnt) approach to predict the geospatial distribution of drinking-water sources, using two types of unimproved sources in Kenya as illustration. Geographic locations of unprotected dug wells and surface water sources derived from the Water Point Data Exchange (WPDx) database were used as inputs to the MaxEnt model alongside geological/hydrogeological and socio-economic covariates. Predictive performance of the MaxEnt models was high (all > 0.9) based on Area Under the Receiver Operator Curve (AUC), and the predicted spatial distribution of water point was broadly consistent with household use of these unimproved drinking-water sources reported in household survey and census data. In developing countries where geospatial datasets concerning drinking-water sources often have necessarily limited resolution or incomplete spatial coverage, the modelled surface can provide an initial indication of the geography of unimproved drinking-water sources to target unserved populations and assess water source vulnerability to contamination and hazards.