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.

Motivated by declines in biodiversity exacerbated by climate change, we identified a network of conservation sites designed to provide resilient habitat for species, while supporting dynamic shifts in ranges and changes in ecosystem composition. Our 12-y study involved 289 scientists in 14 study regions across the conterminous United States (CONUS), and our intent was to support local-, regional-, and national-scale conservation decisions. To ensure that the network represented all species and ecosystems, we stratified CONUS into 68 ecoregions, and, within each, we comprehensively mapped the geophysical settings associated with current ecosystem and species distributions. To identify sites most resilient to climate change, we identified the portion of each geophysical setting with the most topoclimate variability (high landscape diversity) likely to be accessible to dispersers (high local connectedness). These “resilient sites” were overlaid with conservation priority maps from 104 independent assessments to indicate current value in supporting recognized biodiversity. To identify key connectivity areas for sustaining species movement in response to climate change, we codeveloped a fine-scale representation of human modification and ran a circuit-theory-based analysis that emphasized movement potential along geographic climate gradients. Integrating areas with high values for two or more factors, we identified a representative, resilient, and connected network of biodiverse lands covering 35% of CONUS. Because the network connects climatic gradients across 250,000 biodiversity elements and multiple resilient examples of all geophysical settings in every ecoregion, it could form the spatial foundation for targeted land protection and other conservation strategies to sustain a diverse, dynamic, and adaptive world.

Restoring landscape connectivity is a key strategy for sustaining biodiversity and ecosystem services. We developed a decision‐focused process that moves from opportunistic siting of restoration to strategic prioritization by incorporating connectivity enhancement in the Appalachian Mountains, USA. Our approach builds from a recent national‐scale assessment identifying a Resilient and Connected Network (RCN) to inform land protection priorities under climate change. In three high‐ranking study areas within the Appalachians, we demonstrated a circuit‐theory based approach simulating the connectivity value of restoring natural vegetation at sites with high human modification. Our methods were co‐developed by scientists and local decision‐makers. This emphasis on study area‐specific decisions led to differences in how model inputs were defined, for instance using feasible potential restoration sites instead of pixels to define opportunities. Similarly, landscape context influenced our process and in fragmented study areas we added a step that considered additional potential restoration sites outside of the current land protection‐based boundaries. To help interpretation of the results, and link back to the broader network of conservation priorities, we mapped connectivity values in current flow categories that matched the RCN product. Our innovative approach and the decision‐relevant framing, can inform a broad range of connectivity science applications.

Context Structural connectivity models provide critical insights on how movement potential varies across landscapes and can support decisions on where to prioritize land protection and restoration. Water is a common component of landscape models used to inform conservation strategies, but the impacts of water parameterization on model behavior are poorly understood. Objectives We assessed how various parameterization approaches for water bodies, such as rivers, lakes, and shorelines, in structural, omnidirectional circuit-theory connectivity models affect patterns in the primary output, cumulative current flow values, in terrestrial grid cells. Methods We ran two structural modeling approaches with ten simulated and six real-world landscapes using three methods for assigning the resistance of water relative to terrestrial resistance values: low, high, or neutral values drawn randomly from a distribution informed by the terrestrial resistance grid cells. We then compared the cumulative current outputs from each combination. Results The influence of how water was parameterized was consistent across all landscapes. When resistance values for water grid cells were low, the mean values of cumulative current on land were lower, indicating a dampening effect. When water resistances were high, the means and standard deviations of current flow on land were higher, with very high values (a pattern often used to indicate key barriers related to habitat fragmentation and loss) often observed near water. Assigning neutral resistance values to water had the least impact on the current flow across land, relative to a null model. Conclusions We demonstrate water parameterization has important impacts on the outputs of terrestrial structural connectivity models that can complicate model interpretation. When the modeler’s intent is to inform land protection or restoration actions in response to human modification, the choice to treat water as neutral minimizes the interaction between water and land grid cells, clarifying output interpretation and use.