4.5 Article

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Journal

OECOLOGIA
Volume 196, Issue 3, Pages 649-665

Publisher

SPRINGER
DOI: 10.1007/s00442-021-04967-y

Keywords

Habitat configuration; Habitat networks; Landscape connectivity; Movement networks; Social networks

Categories

Funding

  1. Projekt DEAL
  2. Max Planck Society, a Deutsche Forschungsgemeinschaft Scientific Network grant [FA 1420/3-1]
  3. China Scholarship Council [201706100183]
  4. CAPES-Brazil [88881.170254/2018-01]
  5. DFG Centre of Excellence 2117 Centre for the Advanced Study of Collective Behaviour under Germany's Excellence Strategy-EXC [2117-422037984]
  6. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [850859]

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This study introduces a modeling framework inspired by habitat connectivity studies to address the lack of quantitative predictions about the role of habitat configuration in modulating population outcomes. By defining animal habitat networks and simulating alternative habitat configurations, the framework aims to explore the consequences of different habitat structures and address key research questions. The study highlights how considering underlying habitat configuration is important in understanding the impacts of social structure on population-level outcomes.
By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom-up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

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