Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment
The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the cap...
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Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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Online Access: | https://hdl.handle.net/1721.1/131349 |
_version_ | 1811092806586859520 |
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author | Hall, Kimberly R. Anantharaman, Ranjan Landau, Vincent A. Clark, Melissa Dickson, Brett G. Jones, Aaron Platt, Jim Edelman, Alan Shah, Viral B. |
author2 | Massachusetts Institute of Technology. Department of Mathematics |
author_facet | Massachusetts Institute of Technology. Department of Mathematics Hall, Kimberly R. Anantharaman, Ranjan Landau, Vincent A. Clark, Melissa Dickson, Brett G. Jones, Aaron Platt, Jim Edelman, Alan Shah, Viral B. |
author_sort | Hall, Kimberly R. |
collection | MIT |
description | The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the capacity of their analytic tools to handle these datasets. Technology developments in software and high-performance computing are rapidly emerging in many fields, but uptake within conservation may lag, as our tools or our choice of computing language can constrain our ability to keep pace. We recently updated Circuitscape, a widely used connectivity analysis tool developed by Brad McRae and Viral Shah, by implementing it in Julia, a high-performance computing language. In this initial re-code (Circuitscape 5.0) and later updates, we improved computational efficiency and parallelism, achieving major speed improvements, and enabling assessments across larger extents or with higher resolution data. Here, we reflect on the benefits to conservation of strengthening collaborations with computer scientists, and extract examples from a collection of 572 Circuitscape applications to illustrate how through a decade of repeated investment in the software, applications have been many, varied, and increasingly dynamic. Beyond empowering continued innovations in dynamic connectivity, we expect that faster run times will play an important role in facilitating co-production of connectivity assessments with stakeholders, increasing the likelihood that connectivity science will be incorporated in land use decisions. |
first_indexed | 2024-09-23T15:27:16Z |
format | Article |
id | mit-1721.1/131349 |
institution | Massachusetts Institute of Technology |
last_indexed | 2024-09-23T15:27:16Z |
publishDate | 2021 |
publisher | Multidisciplinary Digital Publishing Institute |
record_format | dspace |
spelling | mit-1721.1/1313492023-12-20T15:51:34Z Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment Hall, Kimberly R. Anantharaman, Ranjan Landau, Vincent A. Clark, Melissa Dickson, Brett G. Jones, Aaron Platt, Jim Edelman, Alan Shah, Viral B. Massachusetts Institute of Technology. Department of Mathematics The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the capacity of their analytic tools to handle these datasets. Technology developments in software and high-performance computing are rapidly emerging in many fields, but uptake within conservation may lag, as our tools or our choice of computing language can constrain our ability to keep pace. We recently updated Circuitscape, a widely used connectivity analysis tool developed by Brad McRae and Viral Shah, by implementing it in Julia, a high-performance computing language. In this initial re-code (Circuitscape 5.0) and later updates, we improved computational efficiency and parallelism, achieving major speed improvements, and enabling assessments across larger extents or with higher resolution data. Here, we reflect on the benefits to conservation of strengthening collaborations with computer scientists, and extract examples from a collection of 572 Circuitscape applications to illustrate how through a decade of repeated investment in the software, applications have been many, varied, and increasingly dynamic. Beyond empowering continued innovations in dynamic connectivity, we expect that faster run times will play an important role in facilitating co-production of connectivity assessments with stakeholders, increasing the likelihood that connectivity science will be incorporated in land use decisions. 2021-09-20T14:16:19Z 2021-09-20T14:16:19Z 2021-03-15 2021-03-26T14:17:05Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131349 Land 10 (3): 301 (2021) PUBLISHER_CC http://dx.doi.org/10.3390/land10030301 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
spellingShingle | Hall, Kimberly R. Anantharaman, Ranjan Landau, Vincent A. Clark, Melissa Dickson, Brett G. Jones, Aaron Platt, Jim Edelman, Alan Shah, Viral B. Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title | Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title_full | Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title_fullStr | Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title_full_unstemmed | Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title_short | Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment |
title_sort | circuitscape in julia empowering dynamic approaches to connectivity assessment |
url | https://hdl.handle.net/1721.1/131349 |
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