Disentangling Landscape Heterogeneity: Compositional, Configurational, Vertical, and Temporal Heterogeneity

Soyeon Bae , Catrin Westphal, Christian Ammer, Patrick Hostert, Silke Hüttel, Krisztina Kis-Katos, Thomas Kneib, Holger Kreft, Carola Paul, Tobias Plieninger, Birgitta Putzenlechner, Reimund Rötter, Michael Schlund, Dominik Seidel, Stefan Siebert, Kerstin Wiegand, Alexander Knohl

Aim
Landscape heterogeneity is a key driver of biodiversity, ecosystem functioning, and resilience. However, the complex relationships among different components of heterogeneity—compositional, configurational, vertical, and temporal—remain underexplored for large areas such as at the national scale. This study examines the associations among multiple landscape heterogeneity components across land-cover types to refine their use in ecological research.
Location
Germany
Time Period
Mainly 2017-2020
Major Taxa Studied
Not taxa-specific; focuses on landscape heterogeneity as an ecological driver.
Methods
We analysed nationwide spatial datasets at very high resolution (10–30 m resolution) of land-cover types, dominant tree species, canopy height, and time-series of crop types as well as grassland mowing frequency. We applied Structural Equation Modeling (SEM) to assess the statistical relationship between heterogeneity indices and their interactions. Specifically, we examined (i) compositional vs. configurational heterogeneity (i.e., Shannon diversity vs. edge density), (ii) configurational heterogeneity vs. connectivity, (iii) horizontal vs. vertical and temporal heterogeneities, and (iv) heterogeneities across multiple land-cover types based on grid cells of 3 x 3 km2.
Results
Our findings reveal that compositional and configurational heterogeneities exhibit positive correlations, but their relationships are moderated by the proportions of land-cover types. Configurational heterogeneity does not enhance connectivity; after controlling for land-cover proportions, its partial association with connectivity is negative. Vertical and temporal heterogeneities show limited associations with horizontal compositional and configurational heterogeneities, suggesting relative independence. Principal component analysis indicates that landscape heterogeneity is primarily driven by heterogeneities of forest and overall land-cover, e.g., edge densities of forest dominant tree species and overall land-cover types, whereas cropland heterogeneity, e.g., Shannon diversity of crop types, contributes negatively.
Main Conclusions
Our study underscores the importance of accounting for land-cover proportions when analysing landscape heterogeneity relationships. Failing to do so can distort the model due to potential hidden collinearity. Additionally, our findings highlight the need to capture the multi-dimensional nature of landscape heterogeneity in biodiversity and ecosystem studies. Landscape heterogeneity is shaped by the interdependencies between prevailing land-cover patterns, likely influenced by land-use decisions and history as well as social-ecological contexts, highlighting the need for cross-national or cross-administrative studies.