Biology subjects

Makaga, L.

Publications and source records attributed to Makaga, L..

3 recordsLinked to original sources

bioRxiv2023

Using a multiscale lidar approach to determine variation in canopy structure from African forest elephant trails.

Recently classified as a unique species by the IUCN, African forest elephants (Loxodonta cyclotis) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics would change if forest elephants were to go extinct. Although their role as seed dispersers is well known, they may also drive large-scale processes that determine forest structure, through the creation of elephant trails and browsing the understory and allowing larger, carbon-dense trees to succeed. Multiple scales of lidar were collected by NASA in Lope National Park, Gabon from 2015-2022. Utilizing two airborne lidar datasets and one spaceborne lidar in an African forest elephant stronghold, detailed canopy structural information was used in conjunction with elephant trail data to determine how forest structure varies on and off trails. Forest above elephant trails displayed different structural characteristics than forested areas off trails, with lower canopy height, canopy cover, and different vertical distribution of plant mass. Less plant area density was found on trails at 1 m in height, while more vegetation was found at 12 m, compared to off trail locations. Trails with previous logging history had lower plant area in the top of the canopy.

ecology↗

bioRxiv2022

Invasion of forested areas in Gabon (Central Africa) by the Asian tiger mosquito and the potential consequences from the One Health perspective

Since its first record in urban areas of Central-Africa in 2000s, the invasive mosquito, Aedes albopictus, has continued to spread across the region, including in remote rural areas, and promoted outbreaks of Aedes-borne diseases, such as dengue, chikungunya and Zika. From the One-Health perspective, such invasion might enhance Ae. albopictus interactions with wild animals in forest ecosystems and favor the spillover of zoonotic arboviruses to humans. From 2014 to 2018, we monitored the steady spread of this mosquito species in the wildlife reserve of La Lope National Park (Gabon), and evaluated the magnitude of its colonization of the rainforest ecosystem using ovitraps, larval surveys, BG-Sentinel traps, and human landing catches following an anthropization gradient. We detected Ae. albopictus in forest galleries up to 15km away from La Lope village. However, Ae. albopictus was significantly more abundant at anthropogenic sites than in less anthropized areas. The number of eggs laid by Ae. albopictus decreased progressively with the distance from the forest fringe up to 200m inside the forest, showing that its occurrence in forest ecosystems is restricted to anthropized-sylvatic interfaces with dense forest. This suggests that Ae. albopictus may act as bridge vector of zoonotic pathogens between wild and anthropogenic compartments.

ecology↗

bioRxiv2021

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

O_LIEfforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. C_LIO_LIWe modified an off-the-shelf camera trap (Bushnell) and customised existing open-source hardware to rapidly create a smart camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an alert containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. C_LIO_LIResults show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding uncertain labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. C_LIO_LIWe anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the systems potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas. C_LI

ecology↗