A scalable multi-resolution framework for connectivity-based biodiversity indicators

Roozbeh Valavi , Karel Mokany, Chris Ware, Mat Vickers, Kathryn M Giljohann, Simon Ferrier

1. Landscape connectivity links local habitat condition to broader ecological processes including dispersal, recolonisation, range shifts, and species persistence, that underpin biodiversity responses to land-use and climate change. Several established biodiversity indicators — habitat connectedness (including as an input to the Biodiversity Habitat Index, BHI), Protected Area Connectedness Index (commonly referred to as PARC-connectedness), and the Bioclimatic Ecosystem Resilience Index (BERI) — formalise these processes as least-cost, condition-weighted representations of connected habitat. Applying these landscape connectivity formulations consistently across large spatial extents, multiple dispersal scales, protected-area configurations, and climate futures remains constrained by the computational cost of fine-resolution connectivity over broad landscapes.
2. We present a multi-resolution analytical framework that addresses this constraint by extending established cost-benefit connectivity logic into a scalable, indicator-oriented architecture. Building on the long-recognised principle that spatial precision matters most for areas close to each focal cell, formalised in earlier implementations through distance-dependent aggregation, we reformulate the calculation around a hierarchical graph derived from globally anchored, precomputed raster overviews. This design preserves fine-grain detail where it most influences connectivity, represents more distant landscape context at progressively coarser resolution, and produces reusable path-level outputs that are computed once and applied across dispersal scales and climate scenarios without repeating graph traversal.
3. From these shared path distances, habitat connectedness, PARC-connectedness, and BERI are each derived as condition-weighted, dispersal-decayed integrals over accessible habitat, differing only in how destination nodes are weighted and what ecological quantity is being assessed. The unified derivation ensures consistency across indicators while eliminating redundant computation.
In an example application for Tasmania, Australia (approximately 68,400 km2; more than 300,000 cells at about 900 m resolution), all three indicators were computed across three dispersal scales and six climate scenarios in under a minute on a standard laptop.
4. The framework makes no claim to a new definition of connectivity. Its contribution is analytical and architectural, enabling established and well-validated connectivity concepts to be applied reproducibly and efficiently at the scales demanded by contemporary biodiversity monitoring and conservation planning. It is distributed as a Python package with a highperformance Rust engine, designed for integration into open geospatial workflows and large-scale indicator applications.