Understanding complex spatial dynamics from mechanistic models through spatio‐temporal point processes

Abstract

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a metamodelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories (i.e. spatiotemporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

Best organic farming expansion scenarios for pest control: a modeling approach

“…Aggregating outputs over space, however, may have masked local patterns and possible local peaks in pest densities. In a recent modelling study of a specialist pest and a generalist predator interacting in an heterogeneous agricultural landscape, Zamberletti et al (2021, 2022) showed for example that semi-natural habitats increased the average landscape scale pest density (by reducing the number of necessary pesticide treatments) but locally reduced peaks of pest populations(Zamberletti et al, 2021, 2022). Further analyses of pest density dynamics at the field level would, thus, be necessary to confirm the better ranking of the IP scenario regarding local CBC and pest densities.…”

Reconstructing mechanisms of extinctions to guide mammal conservation biogeography

“…Stage 3 identifies the causes of initial population declines and later extinctions. This is done by analysing simulation outputs from the 'best' SEPMs, using spatiotemporal statistical techniques that allow the role of environmental threats on change in occupancy and abundance to be disentangled spatiotemporally (Zamberletti et al, 2022).…”

“…Stage 3 identifies the causes of initial population declines and later extinctions. This is done by analysing simulation outputs from the ‘best’ SEPMs, using spatiotemporal statistical techniques that allow the role of environmental threats on change in occupancy and abundance to be disentangled spatiotemporally (Zamberletti et al, 2022). Causal explanations regarding the isolated or interactive effects of these threatening processes on emergent patterns can be further tested using counterfactual scenarios (Figure 4), whereby combinations of drivers of environmental change are switched off, while keeping all other parameters fixed (Fordham et al, 2021, 2022).…”

Best organic farming expansion scenarios for pest control: a modeling approach

“…Aggregating outputs over space, however, may have masked local patterns and possible local peaks in pest densities. In a recent modelling study of a specialist pest and a generalist predator interacting in an heterogeneous agricultural landscape, Zamberletti et al (2021Zamberletti et al ( , 2022 showed for example that semi-natural habitats increased the average landscape scale pest density (by reducing the number of necessary pesticide treatments) but locally reduced peaks of pest populations (Zamberletti et al, 2021(Zamberletti et al, , 2022. Further analyses of pest density dynamics at the field level would, thus, be necessary to confirm the better ranking of the IP scenario regarding local CBC and pest densities.…”