Korte, L. | bioRxiv Science

bioRxiv2019

Abundance, density, and social structure of African forest elephants (Loxodonta cyclotis) in a human-modified landscape in southwestern Gabon

The noninvasive monitoring of population size and demography is critical to effective conservation, but forest living taxa can be difficult to directly observe due to elusiveness and/or inaccessible habitat. This has been true of African forest elephants (Loxodonta cyclotis), for which we have limited information regarding population size and social behavior despite their threatened conservation status. In this study, we estimated demographic parameters focusing specifically on population size and density using genetic capture-recapture of forest elephants in the southern Industrial Corridor of the Gamba Complex of Protected Areas, which is considered a global stronghold for forest elephants in southwestern Gabon. Additionally, we examined forest elephant sociality through analysis of social networks, predicting that we would find matrilineal structure as exhibited by savanna elephants and other forest elephants. Given 95% confidence intervals, we estimate the size of the population in the sampled area to be between 754 and 1,502 individuals and our best density estimate ranges from 0.47 to 0.80 elephants per km2. When extrapolated across the entire Industrial Corridor, this estimate suggests an elephant population size of 3,033 to 6,043 in this area based on abundance or 1,684 to 2,832 based on density, which is 40 - 83% smaller than previously suggested. Furthermore, our social network analysis revealed approximately half of network components included females with different mitochondrial haplotypes; this suggests a wider range of variation in forest elephant sociality than has previously been reported. This study emphasizes the threatened status of forest elephants and demonstrates the need to further refine baseline estimates of population size and knowledge on social behavior in this taxon, both of which will aid in determining how population dynamics in this keystone species may be changing through time in relation to increasing conservation threats.

genetics↗

bioRxiv2018

Use of RNA and DNA to Identify Mechanisms of Microbial Community Homogenization

Biotic homogenization is a commonly observed response following conversion of native ecosystems to agriculture, but our mechanistic understanding of this process is limited for microbial communities. In the case of rapid environmental changes, inference of homogenization mechanisms may be confounded by the fact that only a minority of taxa is active at any given point. RNA- and DNA-based community inference may help to distinguish the active fraction of a community from inactive taxa. Using these two community inference methods, we asked how soil prokaryotic communities respond to land use change following transition from rainforest to agriculture in the Congo Basin. Our results indicate that the magnitude of community homogenization is larger in the RNA-inferred community than the DNA-inferred perspective. We show that as the soil environment changes, the RNA-inferred community structure tracks environmental variation and loses spatial structure. The DNA-inferred community loses its association with environmental variability. Homogenization of the DNA-inferred community appears to instead be driven by the range expansion of a minority of taxa shared between the forest and conversion sites, which is also seen in the RNA-inferred community. Our results suggest that complementing DNA-based surveys with RNA can provide unique perspectives on community responses to environmental change.\n\nIMPORTANCETwo primary mechanisms by which community homogenization occurs are: 1) the loss of environmental heterogeneity driving community convergence, and 2) increased rates of biotic mixing, driven by exotic invasions or range expansions. Better identifying these mechanisms could help inform future mitigation strategies. Only a minority of soil taxa tends to be active at any time, which makes identifying these mechanisms difficult. To circumvent this problem, we measured prokaryotic community structure in two ways: RNA-based inference (which should enrich for active taxa), and DNA-based inference (which includes active and inactive taxa) along a gradient of land use change. Our results suggest that changes to soil heterogeneity impact the RNA-inferred community, while range expansions contribute to the homogenization of both DNA- and RNA-inferred communities. Thus, RNA-based community inference may be a more sensitive indicator of environmentally driven homogenization, and researchers interested in microbial responses to rapid environmental change should consider this method.

ecology↗