Hidden biodiversity in entomological collections: The overlooked co-occurrence of dipteran and hymenopteran ant parasitoids in stored biological material.

Gabriela Pérez-Lachaud,Jean-Paul Lachaud

doi:10.1371/journal.pone.0184614

Abstract

Biological collections around the world are the repository of biodiversity on Earth; they also hold a large quantity of unsorted, unidentified, or misidentified material and can house behavioral information on species that are difficult to access or no longer available to science. Among the unsorted, alcohol-preserved material stored in the Formicidae Collection of the ‘El Colegio de la Frontera Sur’ Research Center (Chetumal, Mexico), we found nine colonies of the ponerine ant Neoponera villosa, that had been collected in bromeliads at Calakmul (Campeche, Mexico) in 1999. Ants and their brood were revised for the presence of any sign of parasitism. Cocoons were dissected and their content examined under a stereomicroscope. Six N. villosa prepupae had been attacked by the ectoparasitoid syrphid fly Hypselosyrphus trigonus Hull (Syrphidae: Microdontinae), to date the only known dipteran species of the Microdontinae with a parasitoid lifestyle. In addition, six male pupae from three colonies contained gregarious endoparasitoid wasps. These were specialized in parasitizing this specific host caste as no gyne or worker pupae displayed signs of having been attacked. Only immature stages (larvae and pupae) of the wasp could be obtained. Due to the long storage period, DNA amplification failed; however, based on biological and morphological data, pupae were placed in the Encyrtidae family. This is the first record of an encyrtid wasp parasitizing N. villosa, and the second example of an encyrtid as a primary parasitoid of ants. Furthermore, it is also the first record of co-occurrence of a dipteran ectoparasitoid and a hymenopteran endoparasitoid living in sympatry within the same population of host ants. Our findings highlight the importance of biological collections as reservoirs of hidden biodiversity, not only at the taxonomic level, but also at the behavioral level, revealing complex living networks. They also highlight the need for funding in order to carry out biodiversity inventories and manage existing collections.

Highlights

Specimens stored in natural history collections, together with their associated data, provide a rich repository of information on biodiversity, ecology, behavior, natural resources, species interactions, and ecosystems [1,2,3]
In order to discard any confusion between N. villosa, N. curvinodis, and N. inversa–all three species belonging to the N. foetida species complex and reported in southern Mexico– a sample of workers from our host species was sent to the specialist of this group, JHC Delabie (Laboratorio de Mirmecologia, Itabuna, Bahia, Brazil), who confirmed the identity of our species as N. villosa
New species are identified on the basis of new collection effort and through discriminating among cryptic species or previously “hidden” material stored in natural museum collections

Summary

Introduction

Specimens stored in natural history collections, together with their associated data, provide a rich repository of information on biodiversity, ecology, behavior, natural resources, species interactions, and ecosystems [1,2,3]. For species that are unidentified, difficult to access, extinct in historical times or threatened, these collections constitute a critical resource and are the only means to access such information and document populations or locations no longer available to science [8,9,10]. This is clearly evident for endangered species such as primates, as collections can provide data on anatomy, developmental biology, and life history traits that cannot be replicated today, allowing for comparisons between modern and historical environmental and behavioral variables [10]. It has led to applications including species distribution modeling, tracking patterns in phenology and resources use [13,14,15,16], or constructing inter-species interactions networks and prediction models with applications in ecology, biodiversity, and emerging diseases [17]

Methods

Results

Conclusion