Genetic structure and population history in two critically endangered Kaua‘i honeycreepers

Museum genomics provide insight into the extinction of a specialist North American warbler species

Museum genomics provide an opportunity to investigate population demographics of extinct species, especially valuable when research prior to extinction was minimal. The Bachman's warbler (Vermivora bachmanii) is hypothesized to have gone extinct due to loss of its specialized habitat. However, little is known about other potential contributing factors such as natural rarity or changes to connectivity following habitat fragmentation. We examined mitochondrial DNA (mtDNA) and genome-wide SNPs using specimens collected from breeding and migration sites across the range of the Bachman's warbler. We found no signals of strong population structuring across the breeding range of Bachman's warblers in both mtDNA and genome-wide SNPs. Thus, long-term population isolation did not appear to be a significant contributor to the extinction of the Bachman's warbler. Instead, our findings support the theory that Bachman's warblers underwent a rapid decline likely driven by habitat destruction, which may have been exacerbated by the natural rarity, habitat specificity and low genetic diversity of the species.

How Can Genomics Help or Hinder Wildlife Conservation?

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

GADMA2: more efficient and flexible demographic inference from genetic data

BACKGROUND

Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm.

RESULTS

Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets.

CONCLUSIONS

We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species.

Conservation genomics and systematics of a near-extinct island radiation

Conservation benefits from incorporating genomics to explore the impacts of population declines, inbreeding, loss of genetic variation and hybridization. Here we use the near-extinct Mariana Islands reedwarbler radiation to showcase how ancient DNA approaches can allow insights into the population dynamics of extinct species and threatened populations for which historical museum specimens or material with low DNA yield (e.g., scats, feathers) are the only sources for DNA. Despite their having paraphyletic mtDNA, nuclear SNPs support the distinctiveness of critically endangered Acrocephalus hiwae and the other three species in the radiation that went extinct between the 1960s and 1990s. Two extinct species, A. yamashinae and A. luscinius, were deeply divergent from each other and from a third less differentiated lineage containing A. hiwae and extinct A. nijoi. Both mtDNA and SNPs suggest that the two isolated populations of A. hiwae from Saipan and Alamagan Islands are sufficiently distinct to warrant subspecies recognition and separate conservation management. We detected no significant differences in genetic diversity or inbreeding between Saipan and Alamagan, nor strong signatures of geographic structuring within either island. However, the implications of possible signatures of inbreeding in both Saipan and Alamagan, and long-term population declines in A. hiwae that predate modern anthropogenic threats require further study with denser population sampling. Our study highlights the value conservation genomics studies of island radiations have as windows onto the possible future for the world's biota as climate change and habitat destruction increasingly fragments their ranges and contributes to rapid declines in population abundances.