  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.
