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May 4, 2026ZooKeys0 citationsOpen Access

Development of a spatio-temporal representation of agricultural landscapes as the modelling environment for spatially explicit agent-based models in the Animal Landscape and Man Simulation System (ALMaSS)

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EZElżbieta ZiółkowskaBJBarbara JaśkowiecGGGeoffrey Brian Groom

Key Points

  • This research aims to build a modeling environment for the ALMaSS to evaluate how landscape changes impact animal species in agriculture.
  • Developed algorithms for generating land-use/land-cover raster maps.
  • Classified and defined ALMaSS farm types and crop rotations.
  • Outlined data requirements and handling procedures for model inputs.
  • Successfully generated detailed landscape representations for ALMaSS, enhancing simulation accuracy.
  • Identified challenges related to input data quality and processing that affect model outputs.

Abstract

The model environment constitutes the core component of the Animal, Landscape, and Man Simulation System (ALMaSS). ALMaSS was developed to evaluate the effects of changes in landscape structure and management on key animal species within agroecosystems. Consequently, it is designed to work with representations of actual agricultural landscape areas, capturing both spatial and temporal landscape heterogeneity to fulfil the specific requirements of the ALMaSS species models. This article presents the methodology for describing the land-use/land-cover of agricultural landscapes and generating detailed landscape representations for use in ALMaSS landscape simulations. We outline the external data requirements and data handling procedures necessary to prepare the final set of input files for an ALMaSS run. We provide a mapping algorithm to generate a landscape (land-use/land-cover) raster map and describe the methods for classifying and defining ALMaSS farm types and crop rotations. We present exemplary results and discuss potential applications beyond the ALMaSS modelling framework. Finally, we examine the ALMaSS landscape model generation process in the context of input data quality, accessibility, and data processing challenges and offer a perspective on future developments.

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Cite This Study

Ziółkowska et al. (2026) studied this question.

synapsesocial.com/papers/69f836aa3ed186a739980d82https://doi.org/10.3897/aem.8.167439
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