Reference genome of Townsend's big-eared bat, Corynorhinus townsendii.

Townsend's big-eared bat, Corynorhinus townsendii, is a cave- and mine-roosting species found largely in western North America. Considered a species of conservation concern throughout much of its range, protection efforts would greatly benefit from understanding patterns of population structure, genetic diversity, and local adaptation. To facilitate such research, we present the first de novo genome assembly of C. townsendii as part of the California Conservation Genomics Project (CCGP). Pacific Biosciences HiFi long reads and Omni-C chromatin-proximity sequencing technologies were used to produce a de novo genome assembly, consistent with the standard CCGP reference genome protocol. This assembly comprises 391 scaffolds spanning 2.1 Gb, represented by a scaffold N50 of 174.6 Mb, a contig N50 of 23.4 Mb, and a benchmarking universal single-copy ortholog (BUSCO) completeness score of 96.6%. This high-quality genome will be a key tool for informed conservation and management of this vulnerable species in California and across its range.

Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change.

Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. The estimated distribution differed between the combined (full dataset) and phenologically explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scenarios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Comparison of phenologically explicit models with combined models indicates the combined models better predict the extent of the known range of C. townsendii in California. However, life-history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.