Haplotype tagging reveals parallel formation of hybrid races in two butterfly species.

Genetic variation segregates as linked sets of variants or haplotypes. Haplotypes and linkage are central to genetics and underpin virtually all genetic and selection analysis. Yet, genomic data often omit haplotype information due to constraints in sequencing technologies. Here, we present "haplotagging," a simple, low-cost linked-read sequencing technique that allows sequencing of hundreds of individuals while retaining linkage information. We apply haplotagging to construct megabase-size haplotypes for over 600 individual butterflies (Heliconius erato and H. melpomene), which form overlapping hybrid zones across an elevational gradient in Ecuador. Haplotagging identifies loci controlling distinctive high- and lowland wing color patterns. Divergent haplotypes are found at the same major loci in both species, while chromosome rearrangements show no parallelism. Remarkably, in both species, the geographic clines for the major wing-pattern loci are displaced by 18 km, leading to the rise of a novel hybrid morph in the center of the hybrid zone. We propose that shared warning signaling (Müllerian mimicry) may couple the cline shifts seen in both species and facilitate the parallel coemergence of a novel hybrid morph in both comimetic species. Our results show the power of efficient haplotyping methods when combined with large-scale sequencing data from natural populations.

Coarse climate change projections for species living in a fine-scaled world.

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.

Arthropod diversity in a tropical forest.

Most eukaryotic organisms are arthropods. Yet, their diversity in rich terrestrial ecosystems is still unknown. Here we produce tangible estimates of the total species richness of arthropods in a tropical rainforest. Using a comprehensive range of structured protocols, we sampled the phylogenetic breadth of arthropod taxa from the soil to the forest canopy in the San Lorenzo forest, Panama. We collected 6144 arthropod species from 0.48 hectare and extrapolated total species richness to larger areas on the basis of competing models. The whole 6000-hectare forest reserve most likely sustains 25,000 arthropod species. Notably, just 1 hectare of rainforest yields >60% of the arthropod biodiversity held in the wider landscape. Models based on plant diversity fitted the accumulated species richness of both herbivore and nonherbivore taxa exceptionally well. This lends credence to global estimates of arthropod biodiversity developed from plant models.