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Title: More than $1 billion needed annually to secure Africa’s protected areas with lions

Protected areas (PAs) play an important role in conserving biodiversity and providing ecosystem services, yet their effectiveness is undermined by funding shortfalls. Using lions (Panthera leo) as a proxy for PA health, we assessed available funding relative to budget requirements for PAs in Africa’s savannahs. We compiled a dataset of 2015 funding for 282 state-owned PAs with lions. We applied three methods to estimate the minimum funding required for effective conservation of lions, and calculated deficits. We estimated minimum required funding as $978/km2per year based on the cost of effectively managing lions in nine reserves by the African Parks Network; $1,271/km2based on modeled costs of managing lions at ≥50% carrying capacity across diverse conditions in 115 PAs; and $2,030/km2based on Packer et al.’s [Packer et al. (2013)Ecol Lett16:635–641] cost of managing lions in 22 unfenced PAs. PAs with lions require a total of $1.2 to $2.4 billion annually, or ∼$1,000 to 2,000/km2, yet received only $381 million annually, or a median of $200/km2. Ninety-six percent of range countries had funding deficits in at least one PA, with 88 to 94% of PAs with lions funded insufficiently. In funding-deficit PAs, available funding satisfied just 10 to 20% of PA requirements on more » average, and deficits total $0.9 to $2.1 billion. African governments and the international community need to increase the funding available for management by three to six times if PAs are to effectively conserve lions and other species and provide vital ecological and economic benefits to neighboring communities.

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Authors:
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Publication Date:
NSF-PAR ID:
10077678
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
115
Issue:
45
Page Range or eLocation-ID:
p. E10788-E10796
ISSN:
0027-8424
Publisher:
Proceedings of the National Academy of Sciences
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. 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New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
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