Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

The Ecosystem Services (ES) concept highlights the varied contributions the environment provides to humans and there are a wide range of methods/tools available to assess ES. However, in real-world decision contexts a single tool is rarely sufficient and methods must be combined to meet practitioner needs. Here, results from the OpenNESS project are presented to illustrate the methods selected to meet the needs of 24 real-world case studies and better understand why and how methods are combined to meet practical needs. Results showed that within the cases methods were combined to: i) address a range of ES; ii) assess both supply and demand of ES; iii) assess a range of value types; iv) reach different stakeholder groups v) cover weaknesses in other methods used and vi) to meet specific decision context needs. Methods were linked in a variety of ways: i) as input–output chains of methods; ii) through learning; iii) through method development and iv) through comparison/triangulation of results. The paper synthesises these case study-based experiences to provide insight to others working in practical contexts as to where, and in what contexts, different methods can be combined and how this can add value to case study analyses