It is critical that we move our understanding of the ecosystem services (ESs) produced by landscapes from the present abundance of analysis to a fundamental basis of design. This involves enhancing the ability to understand and model the interconnected, coevolving system of humans and the rest of nature, thus contributing to the design of sustainable landscapes. In this paper, we hypothesise that the spatial configuration of landscape components (the size and arrangement of tree clumps, paddocks, crops, water features, etc.) impacts the production of regulating ESs, which in turn have a leveraging effect on provisioning and cultural ESs. Drawing on the precepts of Ecological Field Theory, we present the development and implications of a conceptual Geographic Information System (GIS)-based model, ESMAX, that utilises the idiosyncratic distance-decay characteristics of regulating ESs. These ‘ES fields’ are visualised as radiating into the landscape from their source components, addressing a gap in biophysical reality that has been identified as a shortcoming of existing ES modelling based on landcover proxies. Hypothetical landscape arrangements of simplified landscape components are tested with ESMAX across three regulating ESs: cooling effect, nitrogen retention, and habitat provision. The model calculates the overall ES performance of each landscape arrangement by tabulating the ES fields produced and, critically, the nonlinear response where fields overlap. The results indicate a primary sensitivity to the size of components and a secondary sensitivity to the arrangement of components. Consequently, ESMAX can be used to design landscape configurations that (1) maximise the production of specific regulating ESs and (2) improve the utilisation of natural ES-producing resources.

The re-integration of native woody vegetation within agricultural areas has the potential to support multifunctional productive landscapes that enhance livestock welfare and restore habitat for native wildlife. As there is minimal research on this issue in Aotearoa New Zealand, this study aimed to identify species of native woody vegetation and propose spatial configurations and site designs to increase multifunctionality on a case study site. The three components of a multifunctional agricultural landscape focused on in this study were (1) enhancing foraging opportunities for livestock, (2) optimizing shade and shelter, and (3) establishing native bush bird habitat. During the first phase, sixty-three suitable species were identified and assigned scores based on the primary objectives and site constraints. This produced four optimized plant lists, one each for the three multifunctional components identified above and one combined multifunctional list incorporating those scores with additional environment and soil scores. The second phase used design thinking methodology to strategically locate these plants within an established case study site. Nine different planting configurations (three for each multifunctional component) were proposed and then, informed by site-specific opportunities and constraints, located on the case study site to produce three individual site designs. Finally, these three site designs were combined to propose an exemplar of a multifunctional agricultural landscape. The results indicate that reintegrating native woody vegetation has the potential to contribute toward multifunctional agricultural landscapes, proposing species and spatial layouts from which further investigation into livestock foraging, increased shade and shelter, and restoration of bush bird habitat can follow. This research advances sustainable land management practices by offering valuable insights into future agricultural landscape design.

A spatial modelling approach was used to explore how a proximity limit (150 m, 300 m, and 450 m) between tobacco retailers may impact different neighbourhoods in New Zealand. Neighbourhoods were categorised into three density groups (0, 1-2, 3+ retailers). As the proximity limit increases, there is a progressive redistribution of neighbourhoods in the three density groups with, the 3+ density group incorporating fewer neighbourhoods and the 0 and 1-2 density groups conversely each consisting of more. The differing measures available at the neighbourhood level enabled our study to discern potential inequities. More directed policies targeting these inequities are needed.