Speeding up the simulation of population spread models
<p>1. Simulating spatially explicit population models to predict population spread allows environmental managers to make better-informed decisions. Accurate simulation requires high spatial resolution, which, using existing techniques, can require prohibitively large amounts of computational...
| Main Authors: | , , , |
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| Format: | Journal article |
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British Ecological Society
2017
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| _version_ | 1826262928358113280 |
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| author | Gilbert, M White, S Bullock, J Gaffney, E |
| author_facet | Gilbert, M White, S Bullock, J Gaffney, E |
| author_sort | Gilbert, M |
| collection | OXFORD |
| description | <p>1. Simulating spatially explicit population models to predict population spread allows environmental managers to make better-informed decisions. Accurate simulation requires high spatial resolution, which, using existing techniques, can require prohibitively large amounts of computational resources (RAM, CPU etc).</p> <p>2. We developed and implemented a novel algorithm for the simulation of integro-difference equations (IDEs) modelling population spread, including stage-structure, which uses adaptive mesh refinement.</p> <p>3. We measured the accuracy of the adaptive algorithm, by comparing the results of simulations using the adaptive and a standard non-adaptive algorithm. The relative error of the population’s spatial extent was low (< 0.05) for a range of parameter values. Comparing efficiency, we found that our algorithm used up to ten times less CPU-time and RAM than the non-adaptive algorithm.</p> <p>4. Our approach provides large improvements in efficiency without significant loss of accuracy, so enables faster simulation of IDEs and simulation at scales and at resolutions that have not been previously feasible. As an example, we simulate the spread of a hypothetical species over the UK at a resolution of 25m. We provide our implementation of the algorithm as a user-friendly executable application.</p> |
| first_indexed | 2024-03-06T19:43:35Z |
| format | Journal article |
| id | oxford-uuid:217dafec-e868-4fd9-becf-56f540b04da9 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:43:35Z |
| publishDate | 2017 |
| publisher | British Ecological Society |
| record_format | dspace |
| spelling | oxford-uuid:217dafec-e868-4fd9-becf-56f540b04da92022-03-26T11:33:47ZSpeeding up the simulation of population spread modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:217dafec-e868-4fd9-becf-56f540b04da9Symplectic Elements at OxfordBritish Ecological Society2017Gilbert, MWhite, SBullock, JGaffney, E<p>1. Simulating spatially explicit population models to predict population spread allows environmental managers to make better-informed decisions. Accurate simulation requires high spatial resolution, which, using existing techniques, can require prohibitively large amounts of computational resources (RAM, CPU etc).</p> <p>2. We developed and implemented a novel algorithm for the simulation of integro-difference equations (IDEs) modelling population spread, including stage-structure, which uses adaptive mesh refinement.</p> <p>3. We measured the accuracy of the adaptive algorithm, by comparing the results of simulations using the adaptive and a standard non-adaptive algorithm. The relative error of the population’s spatial extent was low (< 0.05) for a range of parameter values. Comparing efficiency, we found that our algorithm used up to ten times less CPU-time and RAM than the non-adaptive algorithm.</p> <p>4. Our approach provides large improvements in efficiency without significant loss of accuracy, so enables faster simulation of IDEs and simulation at scales and at resolutions that have not been previously feasible. As an example, we simulate the spread of a hypothetical species over the UK at a resolution of 25m. We provide our implementation of the algorithm as a user-friendly executable application.</p> |
| spellingShingle | Gilbert, M White, S Bullock, J Gaffney, E Speeding up the simulation of population spread models |
| title | Speeding up the simulation of population spread models |
| title_full | Speeding up the simulation of population spread models |
| title_fullStr | Speeding up the simulation of population spread models |
| title_full_unstemmed | Speeding up the simulation of population spread models |
| title_short | Speeding up the simulation of population spread models |
| title_sort | speeding up the simulation of population spread models |
| work_keys_str_mv | AT gilbertm speedingupthesimulationofpopulationspreadmodels AT whites speedingupthesimulationofpopulationspreadmodels AT bullockj speedingupthesimulationofpopulationspreadmodels AT gaffneye speedingupthesimulationofpopulationspreadmodels |