Background: Allegheny woodrats (Neotoma magister) are found in metapopulations distributed throughout the Interior Highlands and Appalachia. Historically these metapopulations persisted as relatively fluid networks, enabling gene flow between subpopulations and recolonization of formerly extirpated regions. However, over the past 45 years, Allegheny woodrat populations have experienced population declines throughout their range due to a combination of habitat destruction, declining hard mast availability, and roundworm parasitism. In an effort to initiate genetic rescue of a small, genetically depauperate subpopulation in New Jersey, woodrats were translocated from a genetically robust population in Pennsylvania (PA) in 2015, 2016, and 2017. Herein, we assess the efficacy of these translocations to restore genetic diversity within the recipient population. 

Results: We designed a novel 134 single nucleotide polymorphism panel, which was used to genotype the six woodrats translocated from PA and 82 individuals from the NJ population captured before and after the translocation events. These data indicated that a minimum of two translocated individuals successfully produced at least 16 offspring, who reproduced as well. Further, population-wide observed heterozygosity rose substantially following the first set of translocations, reached levels comparable to that of populations in Indiana and Ohio, and remained elevated throughout the following years. Abundance also increased during the monitoring period, suggesting Pennsylvania translocations initiated the genetic rescue of the New Jersey population.

Conclusions: Our results indicate, encouragingly, that very small numbers of translocated individuals can successfully restore the genetic diversity of a threatened population. Our work also highlights the risks of managing very small populations, such as when translocated individuals have greater reproductive success relative to residents. Finally, we note that ongoing work with Allegheny woodrats may broadly shape our understanding of genetic rescue within metapopulations and across heterogeneous landscapes.

We extracted deoxyribonucleic acid (DNA) from a tail clip of a single N. magister individual by pairing commercially available extraction (DNEasy Blood and Tissue, Qiagen, Venlo, the Netherlands and clean-up (DNA clean & Concentrator, Zymo Research, Irvine, California) kits in accord with the manufacturers’ instructions. We conducted three lanes of paired-end and one lane of mate-paired sequencing using an Illumina HiSeq2500. We used Trimmomatic to remove adaptors, discard short reads, and trim poor-quality bases from 5’ and 3’ ends of raw sequence reads. We used ABySS 1.9.0 to conduct several assemblies with kmer lengths ranging from 40 to 85. PE reads were used to generate contigs. MP reads were used to infer the order, orientation, and distance between contigs, linking them together in scaffolds. The assembly with the greatest N50 value and longest scaffold was used for downstream analyses.

We used the MAKER 2.28 pipeline to annotate all scaffolds greater than 10 kb. We first used Repeat-Masker to identify and mask stretches of repetitive DNA. Second, we downloaded 6762 Mus musculus protein sequences from the UniProtKB database (www.uniprot.org) and used the protein2genome setting in MAKER to generate gene annotations. These annotations were subsequently used to train SNAP and generate ab initio predictions. Third, we aligned protein sequences and 93400 Mus musculus expressed sequence tag (EST) sequences to the genome using BLAST and used InterProScan to identify putative protein domains. Finally, all ab initio gene predictions supported by protein, EST or InterProScan evidence were promoted to gene annotations.

The Neotoma magister draft genome was generated from mate-paired and paired-end reads using ABySS vs. 1.9.0.  The scaffolds greater than 10 kb in length are described in the 10000_kmer70_scaffolds_woodrats.fasta file.

21,151 genes were annotated in the N. magister genome. These genes are described in the genome.all.gff file.