This Statistical Methods seminar by the Ecological Forecasting Initiative and the Ecological Society of America Statistical Ecology Section is hosting Dr. Richard Schuster from Nature Conservancy of C...

Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation...

In the wake of the Democratic Party’s disappointing 2024 election losses, a quiet but seismic shift is underway within its ranks. At the center of this transformation is Third Way, a self-proclaimed “centrist” think tank that has long positioned itself...

This Statistical Methods seminar by the Ecological Forecasting Initiative and the Ecological Society of America Statistical Ecology Section is hosting Dr. Richard Schuster from Nature Conservancy of C...

Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation...

Systematic conservation prioritization with robust optimization techniques. This is important because conservation prioritizations typically only consider the most likely outcome associated with a conservation action (e.g., establishing a protected area will safeguard a threatened species population) and fail to consider other outcomes and their consequences for meeting conservation objectives. By extending the 'prioritizr' package, this package can be used to generate conservation prioritizations that account of uncertainty in the climate change scenario projections, species distribution models, ecosystem service models, and measurement errors. In particular, prioritizations can be generated to be fully robust to uncertainty by minimizing (or maximizing) objectives under the worst possible outcome. Since reducing the associated with achieving conservation objectives may sacrifice other objectives (e.g., minimizing protected area implementation costs), prioritizations can also be generated to be partially robust based on a specified confidence level parameter. Partially robust prioritizations can be generated based on the chance constrained programming problem (Charnes &amp; Cooper 1959, &lt;<a href="https://doi.org/10.1287%2Fmnsc.6.1.73" target="_top">doi:10.1287/mnsc.6.1.73</a>&gt;) and the conditional value-at-risk problem (Rockafellar &amp; Uryasev 2000, &lt;<a href="https://doi.org/10.21314%2FJOR.2000.038" target="_top">doi:10.21314/JOR.2000.038</a>&gt;).

Provides tools for multiscale systematic conservation planning using the H3 hierarchical hexagonal grid system (Uber Technologies (2024) &lt;<a href="https://h3geo.org" target="_top">https://h3geo.org</a>&gt;) and the 'prioritizr' package (Hanson et al. (2025) &lt;<a href="https://doi.org/10.1111%2Fcobi.14376" target="_top">doi:10.1111/cobi.14376</a>&gt;). Supports the definition and solution of conservation problems across nested H3 resolutions with resolution-specific features, costs, and management attributes, including cross-scale connectivity penalties derived from parent-child relationships. Also includes utilities to evaluate solutions using multiscale-aware diagnostics and to post-process optimization outputs into alternative area-targeted conservation scenarios.

We used the spatial decision-support tool, prioritizR44, to explore four zoning scenarios for Algoa Bay. PrioritizR with multiple zones is a systematic-site prioritization tool that allocates a user-defined proportion of features or activities to several user-defined zones at the lowest...