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.

No abstract

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.

Species distribution models (SDMs) have become an essential tool for the management and conservation of imperiled species. However, many at-risk species are rare and characterized by limited data on their spatial distribution and habitat relationships. This has led to the development of SDMs that integrate multiple types and sources of data to leverage more information and provide improved predictions of habitat associations. We developed a novel integrated species distribution model to predict habitat suitability for jaguars (Panthera onca) in the border region between northern Mexico and the southwestern USA. Our model combined presence-only and occupancy data to identify key environmental correlates, and we used model results to develop a probability of use map. We adopted a logistic regression modeling framework, which we found to be more straightforward and less computationally intensive to fit than Poisson point process-based models. Model results suggested that high terrain ruggedness and the presence of riparian vegetation were most strongly related to habitat use by jaguars in our study region. Our best model, on average, predicted that there is currently 25,463 km 2 of usable habitat in our study region. The United States portion of the study region, which makes up 38.6% of the total area, contained 40.6% of the total usable habitat. Even though there have been few detections of jaguars in the southwestern USA in recent decades, our results suggest that protection of currently suitable habitats, along with increased conservation efforts, could significantly contribute to the recovery of jaguars in the USA.