Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems

Repository accompanying this pre-print: https://www.biorxiv.org/content/10.1101/847632v1 Abstract: The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programming (EILP) solvers. Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12 to 30% cheaper than plans of Marxan using SA, due to EILP’s ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1071 times faster than the Marxan SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process

Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems

The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programing (EILP) solvers. Using a case study in BC, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12-30% cheaper than plans using SA, due to EILP's ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1,071 times faster than the SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process

Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization

Decision-support tools are commonly used to maximize return on investments (ROI) in conservation. We evaluated how the relative value of information on biodiversity features and land cost varied with data structure and variability, attributes of focal species and conservation targets, and habitat suitability thresholds for contrasting bird communities in the Pacific Northwest of North America. Specifically, we used spatial distribution maps for 20 bird species, land values, and an integer linear programming model to prioritize land units (1 km2) that met conservation targets at the lowest estimated cost (hereafter ‘efficiency’). Across scenarios, the relative value of biodiversity data increased with conservation targets, as higher thresholds for suitable habitat were applied, and when focal species occurred disproportionately on land of high assessed value. Incorporating land cost generally improved planning efficiency, but at diminishing rates as spatial variance in biodiversity features relative to land cost increased. Our results offer a precise, empirical demonstration of how spatially-optimized planning solutions are influenced by spatial variation in underlying feature layers. We also provide guidance to planners seeking to maximize efficiency in data acquisition and resolve potential trade-offs when setting targets and thresholds in financially-constrained, spatial planning efforts aimed at maximizing ROI in biodiversity conservation

Bird Cams Lab Video Data

Thank you to the thousands of volunteers who participated in the Bird Cams Lab investigations. Thank you as well to Eliot Miller for working with the Bird Cams Lab community in the Battling Birds: Panama Edition investigation and Wesley Hochachka for advising on how to work with the data. A special thank you to the Cornell Lab’s Web Communications team for designing the live data tagging tool and online collaboration spaces that made data collection and exploration possible.Bird Cams Lab was a project funded by the National Science Foundation that ran from 2018 to 2021, providing opportunities for the public to work with scientists to co-create scientific investigations using online wildlife cams. Participants had the opportunity to be a part of each stage of the scientific process: making observations, posing questions to investigate, designing the study, collecting data, exploring results, and sharing findings. There were six investigations, and a total of more than 500,000 observations. Participants collected data from archived video footage on the Zooniverse platform, and in real time using a data tagging tool on the Bird Cams Lab and Bird Cams website. We have organized the 10-15-second video clips by investigation. To access the observational data, see https://doi.org/10.7298/fxqt-zw38. When using the data, please acknowledge the contributions and funding under Acknowledgements and use the suggested citation. To learn more about the Bird Cams Lab project, visit https://birdcamslab.allaboutbirds.org/. For any questions about the data, please contact Bird Cams ([email protected]).For three investigations (Battling Birds, Hawk Talk, and Battling Birds: Panama Edition), participants collected data from archived video clips on the Zooniverse.org platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation. The overall Bird Cams Lab work was funded by the National Science Foundation under Grant #1713225. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Bird Cams Lab Biological Data

Thank you to the thousands of volunteers who participated in the Bird Cams Lab investigations. Thank you as well to Eliot Miller for working with the Bird Cams Lab community in the Battling Birds: Panama Edition investigation and Wesley Hochachkafor advising on how to work with the data. A special thank you to the Cornell Lab’s Web Communications team for designing the live data tagging tool and online collaboration spaces that made data collection and exploration possible.Bird Cams Lab was a project funded by the National Science Foundation that ran from 2018 to 2021, providing opportunities for the public to work with scientists to co-create scientific investigations using online wildlife cams. Participants had the opportunity to be a part of each stage of the scientific process: making observations,posing questions to investigate, designing the study, collecting data, exploring results, and sharing findings. There were six investigations, and a total of more than 500,000 observations. Participants collected data from archived video footage on the Zooniverse platform, and in real time using a data tagging tool on the Bird Cams Lab and Bird Cams website. The data are intended to be open access and we encourage anyone to use the information to explore or conduct research. We have organized the data for each investigation in its own folder. When using the data, please acknowledge the contributions and funding under Acknowledgements and use the suggested citation. To learn more about the Bird Cams Lab project, visit https://birdcamslab.allaboutbirds.org/. To access the code used to work with this data, visit https://bitbucket.org/cornellbirds/bcl-data-workflow/src/live/.For any questions about the data, please contact Bird Cams ([email protected]).For three investigations (Battling Birds, Hawk Talk, and Battling Birds: Panama Edition), participants collected data from archived video clips on the Zooniverse.org platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.The overall Bird Cams Lab work was funded by the National Science Foundation under Grant #1713225. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Critical Natural Assets

ustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature’s contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world’s population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals