The socioecological benefits and consequences of oil palm cultivation in its native range: The Sustainable Oil Palm in West Africa (SOPWA) Project.

Agriculture is expanding rapidly across the tropics. While cultivation can boost socioeconomic conditions and food security, it also threatens native ecosystems. Oil palm (Elaeis guineensis), which is grown pantropically, is the most productive vegetable oil crop worldwide. The impacts of oil palm cultivation have been studied extensively in Southeast Asia and - to a lesser extent - in Latin America but, in comparison, very little is known about its impacts in Africa: oil palm's native range, and where cultivation is expanding rapidly. In this paper, we introduce a large-scale research programme - the Sustainable Oil Palm in West Africa (SOPWA) Project - that is evaluating the relative ecological impacts of oil palm cultivation under traditional (i.e., by local people) and industrial (i.e., by a large-scale corporation) management in Liberia. Our paper is twofold in focus. First, we use systematic mapping to appraise the literature on oil palm research in an African context, assessing the geographic and disciplinary focus of existing research. We found 757 publications occurring in 36 African countries. Studies tended to focus on the impacts of palm oil consumption on human health and wellbeing. We found no research that has evaluated the whole-ecosystem (i.e., multiple taxa and ecosystem functions) impacts of oil palm cultivation in Africa, a knowledge gap which the SOPWA Project directly addresses. Second, we describe the SOPWA Project's study design and-using canopy cover, ground vegetation cover, and soil temperature data as a case study-demonstrate its utility for assessing differences between areas of rainforest and oil palm agriculture. We outline the socioecological data collected by the SOPWA Project to date and describe the potential for future research, to encourage new collaborations and additional similar projects of its kind in West Africa. Increased research in Africa is needed urgently to understand the combined ecological and sociocultural impacts of oil palm and other agriculture in this unique region. This will help to ensure long-term sustainability of the oil palm industry-and, indeed, all tropical agricultural activity-in Africa.

How index selection, compression, and recording schedule impact the description of ecological soundscapes.

Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down-sampling are sometimes employed to reduce data storage and transmission costs. These parameters vary widely between experiments, with the consequences of this variation remaining mostly unknown.We analyse field recordings from North-Eastern Borneo across a gradient of historical land use. We quantify the impact of experimental parameters (MP3 compression, recording length and temporal subsetting) on soundscape descriptors (Analytical Indices and a convolutional neural net derived AudioSet Fingerprint). Both descriptor types were tested for their robustness to parameter alteration and their usability in a soundscape classification task.We find that compression and recording length both drive considerable variation in calculated index values. However, we find that the effects of this variation and temporal subsetting on the performance of classification models is minor: performance is much more strongly determined by acoustic index choice, with Audioset fingerprinting offering substantially greater (12%-16%) levels of classifier accuracy, precision and recall.We advise using the AudioSet Fingerprint in soundscape analysis, finding superior and consistent performance even on small pools of data. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies.