Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions

TL;DR

This paper presents a high-performance Julia implementation of the Circuitscape connectivity model, enabling faster and larger-scale ecological connectivity analyses to support conservation efforts.

Contribution

The paper introduces Circuitscape.jl, a Julia-based version that significantly improves speed and scalability over previous implementations, facilitating advanced ecological modeling.

Findings

Speed improvements of up to 1800%

Scaling to datasets with 437 million cells

Enhanced ability to analyze larger landscapes

Abstract

Connectivity across landscapes influences a wide range of conservation-relevant ecological processes, including species movements, gene flow, and the spread of wildfire, pests, and diseases. Recent improvements in remote sensing data suggest great potential to advance connectivity models, but computational constraints hinder these advances. To address this challenge, we upgraded the widely-used Circuitscape connectivity package to the high performance Julia programming language. Circuitscape.jl allows users to solve problems faster via improved parallel processing and solvers, and supports applications to larger problems (e.g., datasets with hundreds of millions of cells). We document speed improvements of up to 1800\%. We also demonstrate scaling of problem sizes up to 437 million grid cells. These improvements allow modelers to work with higher resolution data, larger landscapes and perform sensitivity analysis effortlessly. These improvements accelerate the pace of innovation, helping modelers address pressing challenges like species range shifts under climate change. Our collaboration between ecologists and computer scientists has led to the use of connectivity models to inform conservation decisions. Further, these next generation connectivity models will produce results faster, facilitating stronger engagement with decision-makers.