Spatial ecology, biodiversity, and abiotic determinants of Congo's bai ecosystem.

Canopy gaps are foundational features of rainforest biodiversity and successional processes. The bais of Central Africa are among the world's largest natural forest clearings and thought to be critically important islands of open-canopy habitat in an ocean of closed-canopy rainforest. However, while frequently denoted as a conservation priority, there are no published studies on the abundance or distribution of bais across the landscape, nor on their biodiversity patterns, limiting our understanding of their ecological contribution to Congolese rainforests. We combined remote sensing and field surveys to quantify the abundance, spatial distribution, shape, size, biodiversity, and soil properties of bais in Odzala-Kokoua National Park (OKNP), Republic of the Congo (hereafter, Congo). We related bai spatial distribution to variation in hydrology and topography, compared plant community composition and 3D structure between bais and other open ecosystems, quantified animal diversity from camera traps, and measured soil moisture content in different bai types. We found bais to be more numerous than previously thought (we mapped 2176 bais in OKNP), but their predominantly small size (80.7% of bais were <1 ha), highly clustered distribution, and restriction to areas of low topographic position make them a rare riparian habitat type. We documented low plant community and structural similarity between bai types and with other open ecosystems, and identified significant differences in soil moisture between bai and open ecosystem types. Our results demonstrate that two distinct bai types can be differentiated based on their plant and animal communities, soil properties, and vegetation structure. Taken together, our findings provide insights into how bais relate to other types of forest clearings and on their overall importance to Congolese rainforest ecosystems.

Factors affecting the identification of individual mountain bongo antelope.

The recognition of individuals forms the basis of many endangered species monitoring protocols. This process typically relies on manual recognition techniques. This study aimed to calculate a measure of the error rates inherent within the manual technique and also sought to identify visual traits that aid identification, using the critically endangered mountain bongo, Tragelaphus eurycerus isaaci, as a model system. Identification accuracy was assessed with a matching task that required same/different decisions to side-by-side pairings of individual bongos. Error rates were lowest when only the flanks of bongos were shown, suggesting that the inclusion of other visual traits confounded accuracy. Accuracy was also higher for photographs of captive animals than camera-trap images, and in observers experienced in working with mountain bongos, than those unfamiliar with the sub-species. These results suggest that the removal of non-essential morphological traits from photographs of bongos, the use of high-quality images, and relevant expertise all help increase identification accuracy. Finally, given the rise in automated identification and the use of citizen science, something our results would suggest is applicable within the context of the mountain bongo, this study provides a framework for assessing their accuracy in individual as well as species identification.