Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population

Abstract

The Virunga Massif mountain gorilla population has been periodically monitored since the early 1970s, with gradually increasing effort. The population declined drastically in the 1970s, but the numbers stabilized in the 1980s. Since then, the population has been steadily increasing within their limited habitat fragment that is surrounded by a dense human population. We examined fecal samples collected during the Virunga 2015-2016 surveys in monitored and unmonitored gorilla groups and quantified strongylid and tapeworm infections using egg counts per gram to determine environmental and host factors that shape these helminth infections. We showed that higher strongylid infections were present in gorilla groups with smaller size of the 500-m buffered minimum-convex polygon (MCP) of detected nest sites per gorilla group, but in higher gorilla densities and inhabiting vegetation types occurring at higher elevations with higher precipitation and lower temperatures. On the contrary, the impact of monitoring (habituation) was minor, detected in tapeworms and only when in the interaction with environmental variables and MCP area. Our results suggest that the Virunga mountain gorilla population may be partially regulated by strongylid nematodes at higher gorilla densities. New health challenges are probably emerging among mountain gorillas because of the success of conservation efforts, as manifested by significant increases in gorilla numbers in recent decades, but few possibilities for the population expansion due to limited amounts of habitat.

Keywords: Environmental and host factors; Helminth infection; Mountain gorilla; Strongylid nematode; Tapeworm.

Graphical abstract

Fig. 1

Location of study gorilla groups during the Virunga Massif 2015–2016 surveys (Hickey et al., 2019) expressed as centroids of their 500-m buffered minimum-convex polygon. Vegetation data were adopted according to WWF-Germany and IGCP 2017; boundaries of protected areas were derived from ProtectedPlanet.net database. Map was created using ArcGIS Desktop 10.8 (ESRI, 2020. ArcGIS Desktop: Release 10.8. Redlands, CA: Environmental Systems Research Institute; esri.com).

Fig. 2

Principal component analysis output showing associations between variables and the first two principal components PC1 and PC2. Each variable contribution to principal components and its quality are represented by length of vector and its color, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Predicted lines from a generalized linear mixed model for significant effects of (a) the first (PC1) and (b) second principal component (PC2), (c) Density = mean relative density of gorillas per MCP and (d) MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) on strongylid infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).

Fig. 4

Predicted lines from a generalized linear mixed model for significant effects of (a) the second principal component (PC2), (b) interaction between the first principal component (PC1) and MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) and (c) interaction between monitoring (habituation) status and MCP on tapeworm infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).