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.

Unprecedented rates of climate change and biodiversity loss have galvanized efforts to expand protected areas (PAs) globally. However, limited spatial overlap between the most important landscapes for mitigating climate change and those with the highest value for biodiversity may impede efforts to simultaneously address both issues through new protections. At the same time, there is a need to understand how lands with high conservation value align with existing patterns of land management, both public and private, which will inform strategies for developing new conservation areas. To address these challenges, we developed three composite indices to identify the highest conservation value lands across the conterminous United States (CONUS) and Alaska, drawing on a suite of key ecological and environmental indicators. Two indices characterize the most important conservation lands for addressing climate change (based on climate accessibility, climate stability, and total carbon storage) and biodiversity (based on species richness, ecological integrity, and ecological connectivity), while a third, combined index simultaneously addresses both conservation challenges. We found that existing PAs in the United States have relatively low overlap with the highest conservation value lands, regardless of the index used (10%–13% in CONUS, 27%–34% in Alaska), suggesting limited effectiveness of current protections but substantial opportunity for expanding conservation into high‐value, unprotected areas. In unprotected landscapes, the highest value lands for addressing climate change generally diverged from those identified as most important for protecting biodiversity (22%–38% overlap, depending on index and geography). Our combined index reconciled these spatial trade‐offs through high overlap with both the climate and biodiversity indices (66%–72%). Of the unprotected high conservation value lands identified by each of our three indices, we found ≥70% are privately managed in CONUS, while 16%–27% are privately managed in Alaska, underscoring the need to engage private landowners and land trusts in efforts to substantially increase the total footprint of conservation lands in the United States. Our findings highlight the importance of balancing climate and biodiversity objectives when identifying new lands for conservation and provide guidance on where to target new protections to simultaneously address both goals. To facilitate planning using the indices, we developed an interactive web application.

This paper explores the application of a novel vision transformer (ViT) model for the estimation of canopy height models (CHMs) using 4-band National Agriculture Imagery Program (NAIP) imagery across the western United States. We compare the effectiveness of this model in terms of accuracy and precision aggregated across ecoregions and class heights versus three other benchmark peer-reviewed models. Key findings suggest that, while other benchmark models can provide high precision in localized areas, the VibrantVS model has substantial advantages across a broad reach of ecoregions in the western United States, with higher accuracy, higher precision, the ability to generate updated inference at a cadence of three years or less, and high spatial resolution. The VibrantVS model provides significant value for ecological monitoring and land management decisions, including for wildfire mitigation.

Data from long‐term monitoring programs, such as the US Fish and Wildlife Service (USFWS) line distance sampling (LDS) program for Mojave desert tortoises (Gopherus agassizii), are increasingly being used in new ways to elucidate trends in population dynamics. We used the USFWS LDS data in a novel way to generate range‐wide predictions of occupancy, colonization, and local extinction rates from 2001 to 2018. We developed a dynamic occupancy model to answer fundamental questions posed by Bureau of Land Management personnel regarding how G. agassizii are distributed across the landscape over space and time. We transformed the LDS data into detection/nondetection data and constructed a Bayesian dynamic occupancy model using several time‐varying (e.g., temperature, precipitation, normalized difference vegetation index, fire, and a proxy for invasive grasses) and static covariates (e.g., soil properties, topography, distance to roads, distance to urban areas) hypothesized to influence G. agassizii occupancy dynamics. We estimated that over the entire time series (2001–2018) the probability of G. agassizii occupancy is declining in over one quarter (26%) of the range, largely in the northeastern part of the range, but that from 2011 to 2018, 77% of the range has a declining trend. Drawing on these model outputs, we developed an interactive, web‐based tool for exploring trends in dynamic occupancy across the species range, allowing users to focus on areas of management interest or concern.

Estimating population density and identifying those areas where density is changing through time are central to prioritizing conservation and management strategies. Obtaining reliable estimates of density and trends can be challenging, however, especially for long‐lived species that are rare, have broad geographic distributions, and are difficult to detect reliably during field surveys. We developed a hierarchical model for distance‐sampling data that characterizes spatial variation in density at two scales and simultaneously estimates regional trends while accounting for variation in detection probability and availability across surveys. We applied the model to data collected over a 20‐year period (2001–2020) in an area that encompassed most of the geographic range of the Mojave desert tortoise (Gopherus agassizii). Density of adult tortoises varied with multiple biotic and abiotic features, including topography, aspect, geology, and seasonal precipitation and temperature regimes. Across the entire period and study area, the density of adult tortoises decreased by an average of 1.8% per year (95% CI = −3.5% to −0.2%). Trends varied geographically, however, with the steepest declines in the western part of the range (−4.1%, −6.9% to −1.3%). Accounting for habitat loss across our study area, the abundance of this threatened species declined by an estimated 129,000 adults (36%) between 2001 and 2020. Our modeling approach extends traditional distance‐sampling frameworks by accounting for ecological and observational processes that could mask spatiotemporal variation in density and, at the same time, provides spatially explicit estimates to guide conservation and management strategies for tortoises and other rare species.