Page 110 - AJWEP-22-4
P. 110

Salako, et al.

                   bias and presence-only distribution models: Implications   Neural Networks R Package e Version; 2019. Available
                   for background and pseudo-absence  data.  Ecol  Appl.   from: https://cran.r-project.org/package=neuralnet [Last
                   2009;19:181-197.                                     accessed on 2024 Apr 12].
                   doi: 10.1890/07-2153.1                           42.  Salako G, Zaitsev A, Betancur-Corredor B, Russell DJ.
                28.  Ghorbani A, Amir MM, Esmali  Ouri A. Utility  of the   Modelling  and spatial  prediction  of earthworms
                   NDVI  for land/canopy  cover mapping in Khalkhal     ecological-categories distribution reveal their habitat and
                   County (Iran). Ann Biolo Res. 2012;3:5494-5503.      environmental preferences. Ecol Indic. 2024;169:1128.
                29.  Rouse JW, Haas RH, Scheel JA, Deering DW. Monitoring      doi: 10.1016/j.ecolind.2024.112832
                   vegetation  systems in  the  great  plains  with  ERTS.   43.  Jiménez-Valverde  A.  Insights  into  the  area  under
                   Proceedings,  3   Earth  Resource  Technology  Satellite   the receiver  operating  characteristic  curve (AUC) as
                                rd
                   (ERTS) Symposium. 1974;1:48-62.                      discrimination measure in species distribution modelling.
                30.  McFeeters  SK.  The  use  of  the  normalized  difference   Glob Ecol Biogr. 2011;21(4):498-507.
                   water  index  (NDWI) in the  delineation  of open water      doi: 10.1111/j.1466-8238.2011.00683.x
                   features. Int J Remote Sens. 1996;17:1425-1432.  44.  Cohen J. A coefficient of agreement for nominal scales.
                   doi: 10.1080/0143116960894871                        Educ Psychol Measure. 1960;20:37-46.
                31.  Xu H. Modification of normalized difference water index      doi: 10.1177/001316446002000104
                   (NDWI) to enhance  open water features in remotely   45.  Manel S, Ceri WH, Ormerod SJ. Evaluating presence-
                   sensed imagery. Int J Remote Sens. 2006;27:3025-3033.  absence  models  in  ecology:  The  need  to  account  for
                   doi: 10.1080/01431160600589179                       prevalence. J Appl Ecol. 2001;38:921-931.
                32.  Lymburner  L,  Beggs  PJ, Jacobson CR. Estimation      doi: 10.1046/j.1365-2664.2001.00647.x
                   of  canopy-average  surface-specific  leaf  area  using   46.  Silva MC, Moonlight P, Oliveira RS, Rowland L,
                   landsat tm data.  Photogrammetr Eng Remote Sens      Pennington  TR. COSST:  A  tool to facilitate  seed
                   2000;66(2):183-191.                                  provenancing  for climate-smart  ecosystem  restoration.
                33.  Karger  DN, Conrad  O, Böhner  J,  et  al. Climatologies   J Appl Ecol. 2024;62:677-688.
                   at high resolution for the earth’s land surface areas. Sci      doi: 10.1111/1365-2664.14854
                   Data. 2017;4:170122.                             47.  Varvatkar  A.  Improve Neural Network  Performance;
                   doi: 10.1038/sdata.2017.122                          2023.  Available  from: https://www.kaggle.com/code/
                34.  Breiman L, Cutler  A.  Description:  Classification  and   avadhutvarvatkar/improve-neural-network-performance
                   Regression Based on a Forest of Trees Using Random   [Last accessed on 2024 May 15].
                   Inputs, Based on Breiman. Package: RandomForest; 2001.  48.  Kipp  S,  Mistele  B,  Schmidhalter  U.  Identification  of
                   doi: 10.1023/A:1010933404324                         stay-green and early senescence  phenotypes in high-
                35.  Valavi  R, Guillera-Arroita G, Lahoz-Monfort  JJ,   yielding  winter  wheat,  and their  relationship  to grain
                   Elith J. Predictive performance  of presence only    yield and grain protein concentration  using high-
                   species distribution models:  A  benchmark study with   throughput  phenotyping  techniques.  Funct  Plant  Biol.
                   reproducible code. Ecolo Monograp. 2022;92(1):e0148.  2013;41:227-235.
                   doi: 10.1002/ecm.1486                                doi: 10.1071/FP13221
                36.  Salako G, Russell DJ, Stucke A, Einar  E. Assessment   49.  Jansen M, Pinto  F, Nagel  KA,  et  al. Non-invasive
                   of multiple  model  algorithms  to  predict  earthworm   phenotyping  methodologies  enable  the  accurate
                   geographic distribution  range and biodiversity  in   characterization  of growth and performance  of shoots
                   Germany:  Implications  for soil-monitoring  and     and roots. In:  Tuberosa R, editor.  Genomics of Plant
                   species-conservation  needs.  Biodivers  Conserv.    Genetic  Resources. Dordrecht:  Springer Science
                   2023;32:2365-2394.                                   Business Media; 2014.
                   doi:10.1007/s10531-023-02608-9                   50.  Li C, Zhu X, Wei Y, et al. Estimating apple tree canopy
                37.  Elith J, Graham CH, Anderson RP, et al. Novel methods   chlorophyll content based on sentinel-2A remote sensing
                   improve  prediction  of  species’  distributions  from   imaging. Sci Rep. 2018;8:3756.
                   occurrence data. Ecography. 2006;29:129-151.         doi: 101038/s41598-018-21963-0
                38.  Li X, Wang YL. Applying various algorithms for species   51.  He L, Ren X, Wang Y,  et al.  Comparing  methods  for
                   distribution modelling. Integr Zool. 2013;8:124-135.  estimating  leaf area index by multi-angular  remote
                   doi: 10.1111/1749-4877.12000                         sensing in winter wheat. Sci Rep. 2020;10:13943.
                39.  Liaw  A,  Wiener  M.  Classification  and  regression  by      doi: 101038/s41598-020-70951-w
                   random forest. R News. 2002;2:18-22.             52.  Bao J, Yu M, Li J, Wang G, Tang Y, Zhi J. Determination
                40.  Hijmans RJ, Elith J. Spatial Distribution Models, Spatial   of leaf  nitrogen  content  in apple and jujube  by near
                   Data Science With R; 2019.  Available from: https://  infrared spectroscopy. Sci Rep. 2024;14:20884.
                   rspatial.org/sdm/sdm.pdf [Last accessed on 2024 Jun 10].     doi: 10.1038/s41598-024-71590-1
                41.  Fritsch S, Guenther F, Wright M. Neuralnet: Training of   53.  Oyebanji OO, Salako G, Nneji LM,  et al.  Impact  of



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