Maintaining mimicry diversity: optimal warning colour patterns differ among microhabitats in Amazonian clearwing butterflies

Mimicry is one of the best-studied examples of adaptation, and recent studies have provided new insights into the role of mimicry in speciation and diversification. Classical Müllerian mimicry theory predicts convergence in warning signal among protected species, yet tropical butterflies are exuberantly diverse in warning colour patterns, even within communities. We tested the hypothesis that microhabitat partitioning in aposematic butterflies and insectivorous birds can lead to selection for different colour patterns in different microhabitats and thus help maintain mimicry diversity. We measured distribution across flight height and topography for 64 species of clearwing butterflies (Ithomiini) and their co-mimics, and 127 species of insectivorous birds, in an Amazon rainforest community. For the majority of bird species, estimated encounter rates were non-random for the two most abundant mimicry rings. Furthermore, most butterfly species in these two mimicry rings displayed the warning colour pattern predicted to be optimal for anti-predator defence in their preferred microhabitats. These conclusions were supported by a field trial using butterfly specimens, which showed significantly different predation rates on colour patterns in two microhabitats. We therefore provide the first direct evidence to support the hypothesis that different mimicry patterns can represent stable, community-level adaptations to differing biotic environments.</jats:p

The Distribution of Giant Manta Rays In The Western North Atlantic Ocean Off The Eastern United States

Abstract In 2018, the giant manta ray (Manta birostris) was listed as threatened under the U.S. Endangered Species Act. We integrated decades of sightings and survey effort data from multiple sources in a comprehensive species distribution modeling (SDM) framework to evaluate the distribution of giant manta rays off the eastern United States, including the Gulf of Mexico. Manta rays were most commonly detected at productive nearshore and shelf-edge upwelling zones at surface thermal frontal boundaries within a temperature range of approximately 15–30 °C. SDMs predicted high nearshore concentrations off Northeast Florida during April, with the distribution extending northward along the shelf-edge as temperatures warm, leading to higher occurrences north of Cape Hatteras, North Carolina from June to October, and then south of Savannah, Georgia from November to March as temperatures cool. In the Gulf of Mexico, the highest nearshore concentrations were predicted near the Mississippi River delta from April to June and again from October to November. SDM predictions will allow resource managers to more effectively protect manta rays from fisheries bycatch, boat strikes, oil and gas activities, contaminants and pollutants, and other threats.</jats:p

The distribution of manta rays in the western North Atlantic Ocean off the eastern United States

AbstractIn 2018, the giant manta ray was listed as threatened under the U.S. Endangered Species Act. We integrated decades of sightings and survey effort data from multiple sources in a comprehensive species distribution modeling (SDM) framework to evaluate the distribution of giant manta rays off the eastern United States, including the Gulf of Mexico. Manta rays were most commonly detected at productive nearshore and shelf-edge upwelling zones at surface thermal frontal boundaries within a temperature range of approximately 20–30 °C. SDMs predicted highest nearshore occurrence off northeastern Florida during April, with the distribution extending northward along the shelf-edge as temperatures warm, leading to higher occurrences north of Cape Hatteras, North Carolina from June to October, and then south of Savannah, Georgia from November to March as temperatures cool. In the Gulf of Mexico, the highest nearshore occurrence was predicted around the Mississippi River delta from April to June and again from October to November. SDM predictions will allow resource managers to more effectively protect manta rays from fisheries bycatch, boat strikes, oil and gas activities, contaminants and pollutants, and other threats.</jats:p