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Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes

Cite this dataset

Jucker, Tommaso; Rosen, Alice (2024). Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes [Dataset]. Dryad. https://doi.org/10.5061/dryad.tmpg4f55v

Abstract

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.

 

 

README: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes

data_code_archive/
   |-----data/
   |       |-----new_standardised_chms/ # <- ready to use canopy height models.
   |       |-----sensitivity_polygons_2023/ # <- shapefile containing the polygons needed for the sensitivity analysis.
   |       |-----SAFE forest classification/ # <- raster and .xlsx file classifying SAFE landscape by habitat type.
   |       |-----Danum_boundary_extent/ # <- shapefile of Danum CHM boundary extent.
   |       |-----SAFE_boundary_extent/ # <- shapefile of SAFE CHM boundary extent.
   |       |-----Maliau_boundary/ # <- shapefile of Maliau CHM boundary extent.
   |       |-----whole_landscape/ # <- shapefile containing gridded polygons used in the whole landscape analysis.
   |       |-----Land_use_gradient/ # <- shapefile containing polygons used in the space-for-time analysis.
   |       |-----other_metrics/ # <- rasters of plant area density (PAD) for each of the sites.
   |       |-----2014_2016_logged/ # <- shapefile containing polygons for the temporal analysis.
   |       |-----2013_2020_recovery/ # <- shapefile containing polygons for the temporal analysis.
   |       \-----2014_2020_old_growth/ # <- shapefile containing polygons for the temporal analysis.
   |-----R code/
   |           |-----surface_geometry_functions.R # <- main functions used for calculating R, D and deltaH.
   |           |-----alternative_metrics_functions.R # <- functions for calculating alternative structural metrics, compared in SI.
   |           |-----plotting_functions.R # <- functions for plotting.
   |           |-----calculate_RDH_borneo.R # <- calculate RDH across a land-use gradient of forest disturbance or across time.
   |           |-----RDH_whole_landscape.R # <- calculate RDH across the entire SAFE and Danum landscapes.
   |           |-----calculate_RDH_sensitivity_analysis.R # <- calculate RDH for sensitivity analysis.
   |           |-----PCA.R # <- PCA analysis.
   |           |-----analysis.R # <- t-tests comparing RDH in forests vs. non-forests.
   |           |-----calculate_alternative_metrics.R # <- calculate alternative metrics for comparison with R, D and H (in SI).
   |           |-----calculate_percentage_na_rasters.R # <- calculate percentage of NAs in each CHM.
   |           |-----whole_landscape_plot.R # <- plot Figure 4.
   |           |-----sensitivity_analysis_plots.R # <- plot Figure 3.
   |           |-----temporal_plots.R # <- plot Figure 5.
   |           |-----plot_CHMs_temporal_changes.R # <- plot Figure 6.
   |           |-----testing_CHM_resolution.qmd # <- testing impact of CHM resolution on RDH estimates. Figures for SI.
   |           \-----testing_method_changes.qmd # <- testing impact of various changes to the geometric theory.
   \-----README.md

Methods

For details of the methods used to generate the data see Rosen et al. (2024) Ecography, 10.1111/ecog.07377 

Funding

Natural Environment Research Council, Award: NE/S01537X/1