Nesting behavior of Gorilla gorilla diehli at Kagwene Mountain, Cameroon: implications for assessing group size and density

We recorded nesting data at 569 fresh night nest sites, comprising 7032 individual nests, of Cross River gorillas inhabiting the Kagwene Mountain in western Cameroon. The mean night nest group size was 12.4. Overall, 55% of night nests were constructed on the ground and 45% in trees. Significantly more arboreal nests were constructed in the wet season (69%), vs. the dry season (19%). Day nest construction was common at Kagwene (n = 260 nest sites, mean nest group size = 5.98) and we encountered significantly more day nest sites in the wet season. Nest site reuse was also common (35%), though not related to season. Our results of nesting habits concur with those from other western gorilla studies, in which rainfall influences arboreal nesting. However, we encountered wet season arboreal nesting, day nest construction, and overall nest site reuse more frequently than reported for other sites. Our observations have considerable implications when estimating group size and density using traditional nest count data. The gorillas at Kagwene inhabit the highest altitudinal range of all Cross River gorilla subpopulations and rainfall is also high; therefore other subpopulations may demonstrate different nesting characteristics. However, one should consider our findings when attempting to estimate Cross River gorilla density at other localities through nest site data

Devastating Decline of Forest Elephants in Central Africa.

African forest elephants– taxonomically and functionally unique–are being poached at accelerating rates, but we lack range-wide information on the repercussions. Analysis of the largest survey dataset ever assembled for forest elephants (80 foot-surveys; covering 13,000 km; 91,600 person-days of fieldwork) revealed that population size declined by ca. 62% between 2002–2011, and the taxon lost 30% of its geographical range. The population is now less than 10% of its potential size, occupying less than 25% of its potential range. High human population density, hunting intensity, absence of law enforcement, poor governance, and proximity to expanding infrastructure are the strongest predictors of decline. To save the remaining African forest elephants, illegal poaching for ivory and encroachment into core elephant habitat must be stopped. In addition, the international demand for ivory, which fuels illegal trade, must be dramatically reduced

Encounter rate of elephant dung per kilometre.

<p>Results are shown for the 80 survey sites in Central Africa included in this study. Grey shading represents forest cover.</p

Estimated change in elephant dung density (/km²) distribution during 2002–2011 across the Central African forests.

<p>Results are shown as a percentage of the total area of potential elephant habitat overall (A & B) and by country (C & D) for the predictive model with variables: (A & C) survey year, Human Influence Index, corruption and the presence/absence of guards, and (B & D) survey year, proximity to road, human population density, corruption and the presence/absence of guards. The dung density (per km²) intervals are unequal and correspond to the following elephant population categories: extremely low density (0–100), very low (100–250), low (250–500), medium (500–1,000), high (1,000–3,000) and very high (3,000–7,500). With the loss of very high elephant populations in 2011, there is a significant shift into the lower density intervals over the nine years.</p

Elephant dung density and range reduction across the Central African forests.

<p>Predictions are shown for (A) 2002 and (B) 2011 for the model with variables: survey year∧, Human Influence Index***, corruption*** and the presence/absence of guards***, and (C) 2002 and (D) 2011 for the model with variables: survey year∧, proximity to road∧, human population density***, corruption*** and the presence/absence of guards*** (P-values are: ‘***’ <0.001 and ‘∧’ <0.1). Increasingly darker shades of green correspond to higher densities, grey represents extremely low elephant density range (the first interval: 0–100 elephant dung piles/km²) and white is non-habitat (80 survey sites outlined in red). Cutpoints are: 0; 100; 250; 500; 1,000; 1,500; 3,000; 5,000; and 7,500 dung piles/km². Countries 1–5 are: Cameroon; Central African Republic; Republic of Congo; DRC; Gabon.</p

Boxplots of indices of elephant abundance and hunting intensity.

<p>Summaries shown are the natural logarithm of: (A) elephant dung encounter rate per 100 km grouped by the presence/absence of wildlife guards, (B) elephant dung encounter rate per 100 km grouped by the level of hunting intensity (group cutpoints are 0.6 and 1.75 hunter sign/km), and (C) hunter-sign frequency per 100 km grouped by the presence/absence of wildlife guards. Box-widths are proportional to the number of observations in each group.</p

Estimated conditional dependence of elephant dung density for top-ranked multi-variable models including hunter sign.

<p>Results are shown for the top-ranked model with variables: (A) hunter sign*, (B) survey year*, (C) proximity to roads∧, (D) human population density***, (E) corruption*** (higher values = less corrupt) and presence/absence of guards***. Also shown is (F) the Human Influence Index (HII) for the model with proximity to road and human population density variables replaced by the HII, i.e. one of the top-ranking models with variables: hunter sign**, survey year*, HII*, corruption***, and presence/absence of guards***. P-value significance codes are: ‘***’<0.001, ‘**’<0.01, ‘*’<0.05, and ‘∧’<0.1. Plot components are: Estimates on the scale of the linear predictor (solid lines) with the y-axis scale for each variable selected to optimally display the results, confidence intervals (dashed lines), and explanatory variable values of observations with a focus on the core 95% of values for hunter sign, proximity to road and human population density (rug plot - short vertical bars along each x-axis showing the x value for each site).</p