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Estimation of minimum viable population for giant panda ecosystems with membrane computing models

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Abstract

Even though the giant panda’s extinction status is downgraded from endangered to vulnerable, but the animals still face an uphill battle for survival. Moreover, the number of giant pandas is also rare. With the increase of captive individuals, wild release has become an inevitable task that human face. It is important to evaluate at least how much giant pandas are remained for being released to the wild. Several methods have been presented in the literature to assess minimum viable population (MVP) of giant pandas. However, each of these methods is designed only based on population sizes. In this paper, a membrane computing model based on behavioral biology is proposed to evaluate the MVP of the species. An application of the proposed data processing to binary tree whose nodes are composed of population sizes is also proposed. An binary tree is generated by integrating the following three techniques: (a) a random technique to generate the vast majority of populations; (b) a modified binary search method to obtain an order population size sequence; (c) a modified inorder traversal to generate an order binary tree and a parent tree. This is the first attempt to apply membrane computing model to assess the MVP of giant pandas. The effectiveness of the presented general PDP system is verified by means of the numbers or amounts of subpopulations. Besides, we also provide the population distribution of the obtained MVP.

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References

  • Adorna HN (2020) Computing with SN P systems with i/o mode. J Membr Comput 2(4):230–245

    Article  MathSciNet  MATH  Google Scholar 

  • Buño K, Adorna H (2020) Distributed computation of ak P systems with active membranes for SAT using clause completion. J Membr Comput 2(2):108–120

    Article  MathSciNet  MATH  Google Scholar 

  • Ceterchi R, Zhang L, Subramanian KG, Zhang G (2021) Hilbert words as arrays generated with P systems. J Membr Comput 3(3):163–169

    Article  MathSciNet  MATH  Google Scholar 

  • Cardona M, Colomer MA, Margalida A et al (2011) A computational modeling for real ecosystems based on P systems. Nat Comput 10(1):39–53

    Article  MathSciNet  MATH  Google Scholar 

  • Cardona M, Colomer MA, Pérez-Jiménez MJ et al (2008) A P System modeling an ecosystem related to the bearded vulture. In: Proceedings of the sixth brainstorming week on membrane computing, pp 51–66

  • Cardona M, Colomer MA, Margalida A et al (2009) AP system based model of an ecosystem of some scavenger birds. In: International workshop on membrane computing, pp 182–195

  • Ceterchi R, Subramanian KG (2020) Generating pictures in string representation with P systems: The case of space-filling curves. J Membr Comput 2(4):369–379

    Article  MathSciNet  MATH  Google Scholar 

  • Colomer MÁ, Margalida A, Sanuy D, Pérez-Jiménez MJ (2011) A bio-inspired computing model as a new tool for modeling ecosystems: the avian scavengers as a case study. Ecol Modell 222(1):33–47

    Article  Google Scholar 

  • Colomer M, Margalida A, Valencia-Cabrera L et al (2014) Application of a computational model for complex fluvial ecosystems: The population dynamics of zebra mussel Dreissena polymorpha as a case study. Ecol Complexity 20:116–126

    Article  Google Scholar 

  • Connor T, Qiao M, Scribner K et al (2021) Complex effects of habitat amount and fragmentation on functional connectivity and inbreeding in a giant panda population. Conservation Biology

  • Dai QL, Li JW, Yang Y, Li M et al (2020) Genetic diversity and prediction analysis of small isolated giant panda populations after release of individuals. Evolut Bioinf 16:1176934320939945

    Google Scholar 

  • Deng X, Dong J, Wang S, Luo B, Feng H, Zhang G (2022) Reducer lubrication optimization with an optimization spiking neural P system. Inf Sci 604:28–44

    Article  Google Scholar 

  • Dong J, Zhang G, Luo B, Yang Q et al (2022) A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems. Inf Sci 596:1–14

    Article  Google Scholar 

  • Duan Y, Rong0 H, Qi D, Valencia-Cabrera L, Zhang G, Pérez-Jiménez MJ (2020) A review of membrane computing models for complex ecosystems and a case study on a complex giant panda system, Complexity, Volume 2020, Article ID 1312824, 26 pages, https://doi.org/10.1155/2020/1312824

  • Flather CH, Hayward GD, Beissinger SR (2011) Minimum viable populations: is there a ‘magic number’ for conservation practitioners? Trends Ecol Evolut 26(6):307–316

  • García-Dorado A (2015) On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity 115(3):185

    Article  Google Scholar 

  • Guo J (2007) Wildlife conservation-Giant panda numbers are surging-or are they? Science 316(5827):974–975

    Article  Google Scholar 

  • Guo J, Chen Y, Zhang H, Chen G, Hu J, Wu YI (2002) A mathematical model for the population of giant pandas and bamboo in Yele nature reserve of Xiangling mountains. J Nat Conserv 10(2):69–74

    Article  Google Scholar 

  • Hong M, Wei W, Zhou H, Tang J, Han H, Zhang Z (2019) Creative conservation in China: releasing captive giant pandas into the wild. Environ Sci Pollut Res 26(30):31548–31549

    Article  Google Scholar 

  • Huang Z, Zhang G, Qi D, Rong H (2017) Application of probabilistic membrane systems to model giant panda population data. Comput Syst Appl 26(8):252–256

    Google Scholar 

  • Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evolution 27(10):578–584

    Article  Google Scholar 

  • Jiang H, Hu J (2010) Population viability analysis for the giant panda in Baoxing County, Sichuan. Sichuan J Zool 29(2):160–165

    Google Scholar 

  • Kang DW, Zhao ZJ, Chen XY, etc. (2020) Characteristics and impacts of solid waste on giant panda habitat in Wanglang nature reserve. Sci Total Environ 724:138–210

  • Komorowski M, Trzciński T (2019) Random binary search trees for approximate nearest neighbour search in binary spaces. Appl Soft Comput 79:87–93

    Article  Google Scholar 

  • Lyngdoh MK, Chettri A, Adhikari D, Barik SK (2018) Metapopulation modelling of threatened plants to assess conservation status and determine minimum viable population size. Curr Sci 532–538

  • Molnár PK, Bitz CM, Holland MM, Kay JE, Penk SR, Amstrup SC (2020) Fasting season length sets temporal limits for global polar bear persistence. Nature Clim Change 10(8):732–738

    Article  Google Scholar 

  • Nabutanyi P, Wittmann MJ (2021) Modeling minimum viable population size with multiple genetic problems of small populations. bioRxiv. https://doi.org/10.1101/2021.08.02.454753

  • Orellana-Martín D, Riscos-Núñez A (2020) Seeking computational efficiency boundaries: the Pǎun’s conjecture. J Membr Comput 2(4):323–331

  • Pérez-Hurtado I, Orellana-Martín D, Zhang G, Pérez-Jiménez MJ (2019) P-Lingua in two steps: flexibility and efficiency. J Membr Comput 1(2):93–102

    Article  MathSciNet  Google Scholar 

  • Qi D, Xu C, Hou R, Chen P, Owens JR, Zhang Z, etc. (2015) Using habitat models to evaluate protected area designing for giant pandas. J Vertebr Biol 64(1):56–64

  • Ren W, Yang G, Wei F (2002) A simulation model for population viability analysis of giant panda in Mabian Dafengding nature reserve. Acta Theriologica Sinica 22(4):264–269

    Google Scholar 

  • Shaffer ML (1981) Minimum population sizes for species conservation. BioScience 31(2):131–134

    Article  MathSciNet  Google Scholar 

  • Shi XY, Song GH (2013) A mathematical model with pulse effect for three populations of the giant panda and two kinds of bamboo. Sci World J

  • Song B, Luo X, Valencia-Cabrera L, Zeng X (2020) The computational power of cell-like P systems with one protein on membrane. J Membr Comput 2(4):332–340

    Article  MathSciNet  MATH  Google Scholar 

  • Tian H, Zhang G, Rong H, Pérez-Jiménez MJ (2018) Population model of giant panda ecosystem based on population dynamics P system. J Comput Appl 38(5):1488–1493, 1499

  • Tong Y (2019) Modeling of giant panda population characteristics based on multi-environment membrane systems. Southwest Jiaotong University, Master thesis

  • Valencia-Cabrera L, Pérez-Hurtado I, Martínez-del-Amor MÃ (2020) Simulation challenges in membrane computing. J Membr Comput 2(4):392–402

    Article  MathSciNet  MATH  Google Scholar 

  • Wu X, Li G, Bi B (2017) A study on population viability analysis and estimation of minimum viable population in triplophysa venusts. Acta Hydrobioogica Sinica 41(3):543–551

    Google Scholar 

  • Xu HF, Lu JH (1996) Minimum viable population-a basic theory fo conservation biology. Chin J Ecol 15(2):25–30

    Google Scholar 

  • Xu W, Viña A, Kong L, Pimm, etc. (2017) Reassessing the conservation status of the giant panda using remote sensing. Nat Ecol Evolut 1(11):1635–1638

  • Yang Z, Gu X, Nie Y (2018) Reintroduction of the giant panda into the wild: a good start suggests a bright future. Biol Conserv 217:181–186

    Article  Google Scholar 

  • Yi MH (2018) Study on minimum viable population of seed bank in pine forest. Korean J Environ Ecol 32(5):497–506

    Article  Google Scholar 

  • Zhang D, Yu B, Yu J et al (2015) Scheme design and main result analysis of the fouth national survey on giant pandas. For Resour Manag 1:11–16

    Google Scholar 

  • Zhang G, Pérez-Jiménez MJ, Gheorghe M (2017) Real-life applications with membrane computing. Springer, Berlin

    Book  MATH  Google Scholar 

  • Zhang G, Pérez-Jiménez MJ, Riscos-Nuñez A et al. (2021) Membrane Computing Models: Implementations, Springer, Berlin July

  • Zhang G, Shang Z, Verlan S, Martínez-Del-Amor MA et al (2020) An overview of hardware implementation of membrane computing models. ACM Comput Surv 53(4):1–38

    Google Scholar 

  • Zhang G, Rong H, Paul P, He Y, Neri F, Péez-Jiménez MJ (2021) A complete arithmetic calculator constructed from spiking neural P Systems and its application to information fusion. Int J Neural Syst 31(1):1–17

    Article  Google Scholar 

  • Zhang G, Zhang X, Rong H, Paul P et al (2022) A layered spiking neural system for classification problems. Int J Neural Syst 2022:32. https://doi.org/10.1142/S012906572250023X

    Article  Google Scholar 

  • Zhang J, Hu J, Wu H (2002) A analysis on population viability for giant panda in Tangjiahe. Acta Ecologica Sinca 22(7):990–998

    Google Scholar 

  • Zhang M, Song G (2016) The effect of diffusion loss on the time-varying giant panda population. Int J Biomath 9(04):1650062

    Article  MathSciNet  MATH  Google Scholar 

  • Zhu L, Wen W, Zhang H, Hu J (2008) Population viability analysis of giant pandas in the Xiaoxaingling mountains. J China West Norm Univ 29(2):112–116

    Google Scholar 

  • Zhu LF, Zhang SN, Gu XD, Wei FW (2011) Significant genetic boundaries and spatial dynamics of giant pandas occupying fragmented habitat across Southwest China. Mol Ecol 20:1122–1132

    Article  Google Scholar 

  • Zhu M, Yang Q, Dong J, Zhang G, et al. (2021) An adaptive optimization spiking neural P system for binary problems. Int J Neural Syst 31(1):1–17. Article No. 2050054. https://doi.org/10.1142/S0129065720500549

  • http://www.panda.org.cn/china/news/news/2013-01-19/198.html

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (61672437 and 61972324) and Sichuan Science and Technology Program (2022YFG0181). We also acknowledge the support of the research project P20_00486 (Junta de Andalucía, Consejería de Economía, Conocimiento, Empresas y Universidad, along with Fondo Europeo de Desarrollo Regional (FEDER) of the European Union).

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Yingying Duan, Haina Rong & Gexiang Zhang

  2. School of Automation, Chengdu University of Information Technology, Chengdu, 610225, China

    Gexiang Zhang

  3. Chengdu Research Base of Giant Panda Breeding, Chengdu, 610081, China

    Dunwu Qi

  4. Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012, Sevilla, Spain

    Luis Valencia-Cabrera & Mario J. Pérez-Jiménez

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  1. Yingying Duan
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  2. Haina Rong
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  4. Dunwu Qi
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  6. Mario J. Pérez-Jiménez
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Correspondence to Haina Rong.

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Duan, Y., Rong, H., Zhang, G. et al. Estimation of minimum viable population for giant panda ecosystems with membrane computing models. Nat Comput 22, 69–93 (2023). https://doi.org/10.1007/s11047-022-09901-6

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