Richness, abundance, and complementarity of fruit-feeding butterfly species in relict sacred forests and forest reserves of Ghana

Abstract

Sacred forest groves in Ghana are centuries old protected areas that were once part of continuous forest cover but now mostly exist as relict forest patches embedded in an agropastoral landscape. We conducted a year-long survey of the fruit-feeding butterfly fauna of four sacred groves and two forest reserves in the moist semi-deciduous forest zone of Ghana to characterize resident species diversity and complementarity among communities. Joint analysis of frugivorous butterfly diversity at these six forest fragments, which ranged in size from 6 to 5000 ha, was used to evaluate the conservation potential of these ancient indigenous reserves. A total of 6836 individuals were trapped across all sites, representing 79 species and five subfamilies. Community diversity was characterized in terms of, (a) number of species accumulated versus sampling effort, (b) rarefied species richness, (c) nonparametric richness estimates, (d) species evenness, (e) Simpson’s Index of Diversity, and (f) complementarity of communities. Diversity of the fruit-feeding butterfly communities, quantified in terms of both species evenness and rarefied species richness, was higher at the larger forest reserves than at the small sacred forest groves. Additionally, although all sites had species trapped only at that site, the 5000-ha forest reserve harbored a resident community that was clearly distinctive from and more diverse than the other communities including the other forest reserve. Hence, our findings add to the burgeoning body of data that indicates large reserves are the foundation of successful conservation programs. Nonetheless, we found these small forest patches contribute to biodiversity conservation in at least three ways and these are identified and discussed. We also identify a number of species that appear more or less vulnerable to dynamics of forest fragmentation based on changes in their relative abundance across sites and we interpret these data in the context of potential indicator species and theoretical predictions of at-risk species.