Search USGS⌕ Search
Home/Archive/USGS

USGS · 70278762

Columbia Basin pygmy rabbit recovery planning through structured decision making

Kelly Mistry·Sarah J. Converse
View original source ↗Export metadata ↓

Abstract

The endangered Columbia Basin pygmy rabbit (CBPR) faces multiple threats, particularly increasing risk of larger and more intense wildfires due to climate change, emerging disease, and sagebrush habitat loss due to agriculture and development. Through the use of conservation breeding, the CBPR wild population grew from a low of 16 individuals captured in 2001 to over 100 individuals in two subpopulations in 2024. However, these subpopulations are geographically proximate, with potential risk that both subpopulations could be affected by a single wildfire or disease event. Additionally, a succession of setbacks in the breeding program has prompted a natural re-evaluation point for the CBPR conservation program. We undertook a structured decision-making (SDM) process with participants from both Washington Department of Fish and Wildlife (WDFW) and US Fish and Wildlife Service (USFWS) to develop a strategy that is sustainable and implementable for guiding management in the coming decades across the range of the CBPR, taking into account changing conditions and updated information. A population model that incorporated both demographic and high impact event uncertainties was developed to test how alternative strategies – defined by conservation breeding program, vaccination, and translocation components – affect CBPR population growth and cost objectives. Based on analysis of the model results, we identified the following actions that appear to have the greatest potential to allow WDFW and USFWS to meet their conservation objectives for CBPR: Continue conservation breeding program, and possibly expand to include an island subpopulation (an isolated, unfenced area that can serve as a source for rabbits while requiring fewer management inputs) Continue Rabbit Hemorrhagic Disease Virus (RHDV2) annual vaccinations in both breeding and wild populations When juveniles are available for translocation, prioritize recovery areas that are in the establishing phase In addition, while not analyzed explicitly in the model, discussions during the SDM process led to the identification of the following actions, which the group considered to have potential to benefit the CBPR either directly or indirectly: Increase the amount of suitable habitat available to pygmy rabbits Increase protections for existing and potential recovery areas Design future monitoring to better estimate survival and reproduction, with an emphasis on understanding how these vital rates vary between wild and semi-captive individuals, between vaccinated and unvaccinated individuals, and as a function of habitat factors

Explore related subjects

BioRxivPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kelly Mistry, Sarah J. Converse. 2026. Columbia Basin pygmy rabbit recovery planning through structured decision making. https://doi.org/10.64898/2026.04.11.716011

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Movement mechanisms explain current animal density patterns and predict their reorganization under global change

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

BioRxiv↗
USGS2026

The hidden prior: Variance constraints under data augmentation

Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation and hence a binomial support constraint on the number of undetected individuals. In a Bayesian implementation that constraint appears as an induced prior; in a likelihood implementation it is the same finite-support assumption reached by another route. That the Bernoulli specification for the inclusion indicators induces a binomial prior on abundance is established ( Schofield & Barker 2014 ); our concern is what that choice costs in estimated uncertainty. We develop the argument using a simple closed-population abundance estimation problem. We show that augmented occupancy and Huggins conditional-likelihood analyses give numerically identical point estimates of N^ once M is sufficiently large. Their uncertainty estimates, however, need not agree. We distinguish two sources of discrepancy. First, when M is small relative to the number of undetected individuals, the finite binomial ceiling truncates the likelihood or posterior and suppresses uncertainty. Second, once that ceiling no longer binds, Taylor-series (Delta-method) approximations still understate variance, because the quantity of interest is a strongly non-linear function of the estimated parameters and local linearization does not reproduce its curvature. Gauss–Hermite quadrature on the unconstrained logit scale recovers much of the shortfall and approaches the MCMC posterior benchmark, though a small residual remains that does not close as M grows, reflecting the distinction between asymptotic likelihood theory and finite-sample Bayesian inference. Neither mechanism is peculiar to abundance estimation: the first follows from the augmented representation itself, the second from any derived quantity that is a non-linear function of estimated parameters. We close with framework-specific guidance for choosing M.

BioRxiv↗
USGS2026

Southern (California) sea otter population status and trends at San Nicolas Island, 2023–winter 2026

The southern sea otter ( Enhydra lutris nereis ) population at San Nicolas Island, California, has been monitored annually since the translocation of 140 sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and method across years. In 2017, in accordance with the National Defense Authorization Act for Fiscal Year 2016, the U.S. Navy and the U.S. Fish and Wildlife Service formally initiated a sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The monitoring program, at its basic level, includes quarterly seasonal surveys of population abundance, distribution, and foraging activity. This report presents data from the program with a focus on the recent three years from winter 2023 through winter (February) 2026. From 2023 to 2026, we measured an 8.1-percent per annum decrease in population abundance (95-percent confidence interval =1.1–14.6 percent), with 106 total individuals counted as of February 2026. Historically, sea otter habitat usage at San Nicolas Island was concentrated on the west end of the island. Between 2017 and 2019, we observed increased seasonal usage of the north and south sides of the island, and in 2020-2022, a large (approximately 30-40 individuals) group of sea otters (raft) took up residence off the east end. During 2023-2026 the east end raft disappeared, and sea otters returned to their historical habitat usage patterns at the west end of the island. Foraging data were collected from summer 2023 to winter 2026 on a total of 461 foraging dives in 32 foraging bouts, and the majority of identified prey on successful dives (n=325) were sea urchins (124) followed by snails (48), bivalves (41) and crabs (23). One lobster and one octopus were also identified among the sea otter prey items. We combined these data with data from 2020-2022 to estimate overall energy intake rates that averaged 7.7 kilocalories per minute (95-percent credible interval =6.6–9.1 kilocalories per minute). These results can be useful to the planning of future monitoring and research of sea otters at San Nicolas Island.

BioRxiv↗