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.
Highlights
Connectivity models provide important insights into ecological processes that involve variation in movement or flow patterns across heterogeneous environments [6]
The growth in popularity of circuit theory to model ecological processes has to lead to widespread adoption of the Circuitscape software package
The Circuitscape project has always evolved with the demands of the users and this upgrade to the Julia programming language seeks to drive the generation of computeintensive connectivity models - by shortening execution time from weeks to days and from days to hours
Summary
Connectivity models provide important insights into ecological processes that involve variation in movement or flow patterns across heterogeneous environments [6]. A common requirement for modeling connectivity is a gridded depiction of the landscape in which values for each cell represent some relative value of “resistance” to movement These resistance grids are developed through several different methods, often involving iterative processes for categorizing resistance weights for different types of barriers based on expert opinion and information on species’ life histories and movement behaviors [27, 36]. This grid can be abstracted as a graph [30], providing a way to quantify ecological distance measures via graph-theoretic metrics
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