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Although Lower Congo rock art was identified as far back as the nineteenth Although Lower Congo rock art was identified as far back as the nineteenth century, it had never been a subject of thorough investigation. Presently inhabited by the Ndibu, one of the Kongo sub-groups, the Lovo Massif is situated north of the ancient Kongo Kingdom. With sites including 16 decorated caves , the Lovo Massif has the largest concentration of rock art in the entire region. In and , we were able to collected pigment samples directly on the panels in the undiscovered decorated cave of Tovo. Unlike the Sahara and Southern Africa, both extensively prospected, rock art of Central Africa is still widely unknown and not dated.
Plant inventories were conducted in the immediate surroundings within 50m of the trap, in the same season of insect sampling. These data permitted the assessment of plant species richness numbers of herbs, shrubs and trees and environmental conditions based on average Ellenberg values [ 46 — 48 ], as well as changes therein over time. Each Ellenberg indicator we considered nitrogen, pH, light, temperature and moisture was averaged over all species for each location-year combination.
We examined annual trends in each of the above-mentioned variables in order to uncover potential structural changes in habitat characteristics over time.
Species richness was analyzed using mixed-effects generalized linear models [ 49 ] with a random intercept for trap location and assuming a Poisson distribution for species richness, and a normal distribution for mean Ellenberg indicator values. Although the fit is not perfect in the case of herb richness, we believe our trend adequately describes direction of change over time. Mean changes in plant species richness are depicted in S3C Fig.
Insect biomass model The temporal resolution of the trap samples accumulated over several days is not directly compatible with the temporal distribution of the weather data daily values. Additionally, variable exposure intervals between trap samples is expected to induce variation in trapped biomass between samples, and hence induce heteroscedasticity. Furthermore, biomass data can numerically only be positive on the real line, and we require a model to reflect this property of the data.
Because of the unequal exposure intervals however, log-transforming the response would be inappropriate, because we require the sum of daily values after exponentiation, rather that the exponent of the sum of log-daily biomass values. In order to indirectly relate biomass to daily weather variables, to account for the variation in time exposure intervals over which biomass was accumulated in the samples, and to respect the non-negative nature of our data, we modeled the biomass of each catch as the sum of the expected but unobserved latent daily biomass.
The latent daily biomass itself is represented by a log normal distribution, in which coefficients for covariates, random effects and residual variance are all represented on the log scale.
The random term us denotes the location-specific random effect assumed to be distributed normally about zero. The exponentiation of the right hand side of Eq 3 ensures expected values to be positive. The expected residual variance of each sample , is expressed as the sum of variances of daily biomass values.
Additionally, we are interested in being able to compare the residual variance with the random effects variance, and this requires them to be on the same scale.
Therefore, we expressed the variance of the daily biomass as a function of the variance of the logarithm of the daily biomass. Using the method of moments: 6 where v represents the residual variance of daily log-biomass.
Analysis We developed a series of models each consisting of a set of explanatory variables that measure aspects of climate, land use and local habitat characteristics.
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