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Modeling spatial distribution of plant species using autoregressive logistic regression method-based conjugate search direction

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Abstract

Modeling plant habitat range distributions is critical for monitoring and restoring species in their natural habitat. The classical logistic regression (LR) model for plant habitat distribution has several drawbacks such as neglecting the effects of the important variables and sensitivity to non-correlation variables. In this paper, an autoregressive logistic regression (ALR)-based conjugate gradient training approach was proposed to improve the drawbacks of LR in predicting the presence and absence of spatial habitat distribution based on input attributes including soil gypsum amount (gyps), lime content, soil available moisture (AM), soil electrical conductivity (EC), clay, and gravel amounts in Poshtkouh rangelands of Yazd Province, Iran. The conjugate gradient approach to calibrate logit model is extended by an iterative formulation using a limited scalar factor and adaptive step size. The predicted results of the classical LR and ALR were validated for nine plant habitats based on several comparative error statistics. The results illustrated that different coefficients were obtained for LR and ALR models but the proposed ALR performed better than the LR in estimating the occurrence probability of plant species.

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References

  • Abd El-Ghani MM, Amer WM (2003) Soil-vegetation relationships in a coastal desert plain of southern Sinai Egypt. J Arid Environ 55:607–628. https://doi.org/10.1016/S0140-1963(02)00318-X

    Article  Google Scholar 

  • Allen SE (1974) Chemical analysis of ecological material. Blackwell, Hoboken

    Google Scholar 

  • Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22(1):42–47. https://doi.org/10.1016/j.tree.2006.09.010

    Article  PubMed  Google Scholar 

  • Ardestani EG, Tarkesh M, Bassiri M, Vahabi MR (2015) Potential habitat modeling for reintroduction of three native plant species in central Iran. J Arid Land 7(3):381–390

    Article  Google Scholar 

  • Augustin NH, Mugglestone MA, Buckland ST (1996) An autologistic model for the spatial distribution of wildlife. J Appl Ecol 33(2):339–347

    Article  Google Scholar 

  • Bo YC, Song C, Wang JF, Li XW (2014) Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China. BMC Public Health 14(1):358

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen G, Tu L, Chen G, Hu J, Han Z (2018) Effect of six years of nitrogen additions on soil chemistry in a subtropical Pleioblastus amarus forest Southwest China. J For Res 29(6):1657–1664

    Article  CAS  Google Scholar 

  • Diop L, Bodian A, Djaman K, Yaseen ZM, Deo RC, El-shafie A, Brown LC (2018) The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environ Earth Sci 77:182. https://doi.org/10.1007/s12665-018-7376-8

    Article  Google Scholar 

  • Dubuis A, Pottier J, Rion V (2011) Predicting spatial patterns of plant species richness: a comparison of direct macro ecological and species stacking modeling approaches. Divers Distrib 17:1122–1131. https://doi.org/10.1111/j.1472-4642.2011.00792.x

    Article  Google Scholar 

  • Elith J, Graham C (2009) Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77

    Article  Google Scholar 

  • Elzwayie A, El-shafie A, Yaseen ZM, Afan HA, Allawi MF (2016) RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. Neural Comput Appl. https://doi.org/10.1007/s00521-015-2174-7

    Article  Google Scholar 

  • Franklin J, Davis FW, Ikegami M, Syphard AD, Flint LE, Flint AL, Hannah L (2013) Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Glob Change Biol 19:473–483. https://doi.org/10.1111/gcb.12051

    Article  Google Scholar 

  • Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15:578–587. https://doi.org/10.1111/j.1466-8238.2006.00257.x

    Article  Google Scholar 

  • Guisan A, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modeling of plant species distribution. Plant Ecol 143:107–122

    Article  Google Scholar 

  • Guisan A, Edwards T.C., Hastie, T., (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100. https://doi.org/10.1016/S0304-3800(02)00204-1

    Article  Google Scholar 

  • Guisan A, Graham CH, Elith J, Huettmann F (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340

    Article  Google Scholar 

  • Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, Soto A, Swenson JJ, Tovar C, Valqui TH (2008) Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17:1353–1366

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, Hoboken

    Book  Google Scholar 

  • Hosseini SZ, Kappas M, Zare Chahouki MA, Gerold G, Erasmi S, Rafiei Emam A (2013) Modeling potential habitats for Artemisia sieberi and Artemisia aucheri in Poshtkouh area, central Iran using the maximum entropy model and geostatistics. Ecol Inform 18:61–68. https://doi.org/10.1016/j.ecoinf.2013.05.002

    Article  Google Scholar 

  • Ji X, Chen L, Zhang A (2017) Anchorage properties at the interface between soil and roots with branches. J For Res 28(1):83–93

    Article  Google Scholar 

  • Jones-Farrand DT, Fearer TM, Thogmartin WE, Iii FRT, Nelson MD, Tirpak JM (2011) Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction. Ecol Appl 21:2269–2282

    Article  PubMed  Google Scholar 

  • Keshtegar B, Piri J, Kisi O (2016a) A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Comput Electron Agric 127:120–130

    Article  Google Scholar 

  • Keshtegar B, Allawi MF, Afan HA, El-Shafie A (2016b) Optimized river stream-flow forecasting model utilizing high-order response surface method. Water Resour Manage 30:3899–3914. https://doi.org/10.1007/s11269-016-1397-4

    Article  Google Scholar 

  • Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement 131:35–41

    Article  Google Scholar 

  • Khalasi Ahvazi L, Zare Chahouki MA, Ghorbannezhad F (2012) Comparing discriminant analysis, ecological niche factor analysis and logistic regression methods for geographic distribution modeling of Eurotia ceratoides (L.) C A. Mey. Rangel Sci 3:45–57

    Google Scholar 

  • Kikvidze Z, Pugnaire FI, Brooker RW et al (2005) Linking patterns and processes in alpine plant communities: a global study. Ecology 86:1395–1400. https://doi.org/10.1890/04-1926

    Article  Google Scholar 

  • Kowsar R, Keshtegar B, Marey MA, Miyamoto A (2017) An autoregressive logistic model to predict the reciprocal effects of oviductal fluid components on in vitro spermophagy by neutrophils in cattle. Sci Rep. https://doi.org/10.1038/s41598-017-04841-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393

    Article  Google Scholar 

  • Luoto M, Hjort J (2005) Downscaling of coarse-grained geomorphological data. Earth Surf Process Landf 33:75–89

    Article  Google Scholar 

  • Maalouf M, Trafalis TB (2011) Robust weighted kernel logistic regression in imbalanced and rare events data. Comput Stat Data Anal 55:168–183. https://doi.org/10.1016/j.csda.2010.06.014

    Article  Google Scholar 

  • Manel S, Dias JM, Buckton ST, Ormerod SJ (1999) Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J Appl Ecol 36:734–747

    Article  Google Scholar 

  • Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modeling. Divers Distrib 15:59–69

    Article  Google Scholar 

  • McCune B (2006) Non-parametric habitat models with automatic interactions. J Veg Sci 17:819–830

    Article  Google Scholar 

  • Miller J, Franklin J (2002) Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol Model 157:227–247

    Article  Google Scholar 

  • Phillips SJ, R.P. Anderson., Schapire, R.E., (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Piri Sahragard H, Ajorlo M (2017) A comparison of logistic regression and maximum entropy for distribution modeling of range plant species (A case study in rangelands of Western Taftan, Southeastern Iran). Turk J Bot 42:28–37. https://doi.org/10.3906/bot-1612-5

    Article  Google Scholar 

  • Piri Sahragard H, Zare Chahouki MA (2015) An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province. Ecol Model 309–310:64–71. https://doi.org/10.1016/j.ecolmodel.2015.04.005

    Article  Google Scholar 

  • Piri Sahragard H, Ajorlo M, Karami P (2018) Modeling habitat suitability of range plant species using random forest method in arid mountainous rangelands. J Mt Sci 15(10):1–13. https://doi.org/10.1007/s11629-018-4898-1

    Article  Google Scholar 

  • Pottier J, Dubuis A, Pellissier L et al (2013) The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients. Glob Ecol Biogeogr 22:52–63. https://doi.org/10.1111/j.1466-8238.2012.00790.x

    Article  Google Scholar 

  • Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM, Peterson AT, Owens HL, Lira-Noriega A (2012) Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. Ecol Model 237–238:11–22. https://doi.org/10.1016/j.ecolmodel.2012.04.001

    Article  Google Scholar 

  • Segurado P, Araújo M (2004) An evaluation of methods for modeling species distributions. J Biogeogr 31:1555–1569

    Article  Google Scholar 

  • Shabani F, Kumar L, Ahmadi M (2016) A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol Evol 6:5973–5986. https://doi.org/10.1002/ece3.2332

    Article  PubMed  PubMed Central  Google Scholar 

  • Tan CO, Özesmi U, Beklioglu M, Per E, Kurt B (2006) Predictive models in ecology: comparison of performances and assessment of applicability. Ecol Inform 1:195–211. https://doi.org/10.1016/j.ecoinf.2006.03.002

    Article  Google Scholar 

  • Tarkesh M, Jetschke G (2012) Comparison of six correlative models in predictive vegetation mapping on a local scale. Environ Ecol Stat 19:437–457. https://doi.org/10.1007/s10651-012-0194-3

    Article  Google Scholar 

  • Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, Ferreira De Siqueira M, Grainger A, Hannah L, Hughes L, Huntley B, Van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–148. https://doi.org/10.1038/nature02121

    Article  PubMed  CAS  Google Scholar 

  • Wang HH, Wonkka CL, Treglia ML, Grant WE, Smeins FE, Rogers WE (2015) Species distribution modeling for conservation of an endangered endemic orchid. AoB Plants. https://doi.org/10.1093/aobpla/plv039

    Article  PubMed  PubMed Central  Google Scholar 

  • Wilson DJ, Western AW, Grayson RB (2004) Identifying and quantifying sources of variability in temporal and spatial soil moisture observations. Water Resour Res. https://doi.org/10.1029/2003WR002306

    Article  Google Scholar 

  • Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773

    Article  Google Scholar 

  • Wu H, Huffer FRW (1997) Modeling the distribution of plant species using the autologistic regression model. Environ Ecol Stat 4(1):31–48

    Article  Google Scholar 

  • Yang XQ, Kushwaha SPS, Saran S (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng. 51:83–87

    Article  CAS  Google Scholar 

  • Zare Chahouki MA, Piri Sahragard H (2016) Maxent modeling for distribution of plant species habitats of rangelands (Iran). Pol J Ecol 64(4):453–467. https://doi.org/10.3161/15052249PJE2016.64.4.002

    Article  Google Scholar 

  • Zare Chahouki MA, Azarnivand H, Jafari M, Tavili A (2010) Multivariate statistical methods as a tool for model-based prediction of vegetation types. Russ J Ecol 41:84–94. https://doi.org/10.1134/S1067413610010169

    Article  Google Scholar 

  • Zare Chahouki MA, Khalasi Ahvazi L, Azarnivand H (2012) Comparison of three modeling approaches for predicting plant species distribution in mountainous scrub vegetation (Semnan rangelands, Iran). Pol J Ecol 60(2):277–289

    Google Scholar 

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Authors and Affiliations

  1. Rangeland and Watershed Department, Water and Soil Faculty, University of Zabol, Zabol, Iran

    Hossen Piri Sahragard

  2. Department of Civil Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran

    Behrooz Keshtegar

  3. Department of Rehabilitation of Arid and Mountainous Regions, Natural Resources Faculty, University of Tehran, Tehran, Iran

    Mohammad Ali Zare Chahouki

  4. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Zaher Mundher Yaseen

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  1. Hossen Piri Sahragard
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  2. Behrooz Keshtegar
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  3. Mohammad Ali Zare Chahouki
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  4. Zaher Mundher Yaseen
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Correspondence to Zaher Mundher Yaseen.

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Communicated by Joseph Paul Messina.

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Sahragard, H.P., Keshtegar, B., Chahouki, M.A.Z. et al. Modeling spatial distribution of plant species using autoregressive logistic regression method-based conjugate search direction. Plant Ecol 220, 267–278 (2019). https://doi.org/10.1007/s11258-019-00911-6

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  • DOI: https://doi.org/10.1007/s11258-019-00911-6

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