Modelling patterns of pollinator species richness and diversity using satellite image texture

PLoS ONE

Volumn 12, Issue 10, 2017, Pages

  Hofmann, Sylvia ^a   Everaars, Jeroen ^a,b   Schweiger, Oliver ^a   Frenzel, Mark ^a   Bannehr, Lutz ^c   Cord, Anna F ^a  

^a HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^b GERMAN CENTRE FOR INTEGRATIVE BIODIVERSITY RESEARCH IDIV HALLE JENA LEIPZIG   (Germany)

^c Facility Management and Geoinformation   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIODIVERSITY; BIRD; BUMBLEBEE; ENTROPY; GERMANY; HABITAT; HUMAN; HUMAN EXPERIMENT; LANDSCAPE; MONITORING; NONHUMAN; PLANT COMMUNITY; POLLINATOR; SATELLITE IMAGERY; SPECIES DISTRIBUTION; SPECIES RICHNESS; ANIMAL; CLASSIFICATION; ECOSYSTEM; INSECT; PHYSIOLOGY; POLLINATION; REMOTE SENSING;

ANIMALS; BIODIVERSITY; ECOSYSTEM; GERMANY; INSECTS; POLLINATION; REMOTE SENSING TECHNOLOGY;

EID: 85030622455     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0185591     Document Type: Article

References (81)

1. Henry PY, Lengyel S, Nowicki P, Julliard R, Clobert J, Celik T, et al. Integrating ongoing biodiversity monitoring: potential benefits and methods. Biodivers Conserv. 2008; 17(14):3357–82.
2. Palmer MW, Earls PG, Hoagland BW, White PS, Wohlgemuth T. Quantitative tools for perfecting species lists. Environmetrics. 2002; 13:121–37.
3. Rocchini D, He K, Oldeland J, Wesuls D, Neteler M. Spectral variation versus species beta-diversity at different spatial scales: a test in african highland savannas. J Environ Monitoring. 2010; 12:825–31.
4. Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges. Ecol Inform. 2010; 5(5):318–29.
5. MacArthur R, MacArthur J. On bird species diversity. Ecology. 1961; 42(3):594–8.
6. MacArthur RH, Wilson EO. The theory of island biogeography. Princeton: Princeton: University Press; 1967.
7. Baldi G, Paruleo JM. Land-Use and land cover dynamics in South American temperate grasslands. Ecol Soc. 2008; 13(2):6.
8. Duro DC, Girard J, King DJ, Fahrig L, Mitchell S, Lindsay K, et al. Predicting species diversity in agricultural environments using Landsat TM imagery. Remote Sens Environ. 2014; 144:214–25.
9. Rocchini D, Boyd DS, Féret J-B, Foody GM, He KS, Lausch A, et al. Satellite remote sensing to monitor species diversity potential and pitfalls. Remote Sens Ecol Conserv. 2016 01; 2(1):25–36.
10. St-Louis V, Pidgeon AM, Radeloff VC, Hawbaker TJ, Clayton MK. High-resolution image texture as a predictor of bird species richness. Remote Sens Environ. 2006; 105:299–312.
11. Wood EM, Pidgeon AM, Radeloff VC, Keuler NS. Image texture as a remotely sensed measure of vegetation structure. Remote Sens Environ. 2012; 121:516–26.
12. Rocchini D. Effects of spatial and spectral resolution in estimating ecosystem alpha-diversity by satellite imagery. Remote Sens Environ. 2007; 111(4):423–34.
13. Herkul K, Kotta J, Kutser T, Vahtmae E. Relating remotely sensed optical variability to marine benthic biodiversity. PLOS One. 2013; 8(2): e55624. https://doi.org/10.1371/journal.pone.0055624 PMID: 23405180
14. Culbert PD, Pidgeon AM, St-Louis V, Bash D, Radeloff VC. The impact of phenological variation on texture measures of remotely sensed imagery. Ieee J-Stars. 2009; 2(4):299–309.
15. St-Louis V, Pidgeon AM, Clayton MK, Locke BA, Bash D, Radeloff VC. Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography. 2009; 32:468–80.
16. Haralick RM, Shanmuga K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973; SMC-3:610–21.
17. Culbert PD, Radeloff VC, V S-L, Flather CH, Rittenhouse CD, Albright TP, et al. Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture. Remote Sens Environ. 2012; 118:140–50. https://doi.org/10.1016/j.rse.2011.11.004
18. St-Louis V, Pidgeon AM, Kuemmerle T, Sonnenschein R, Radeloff VC, Clayton MK, et al. Modelling avian biodiversity using raw, unclassified satellite imagery. Philos Trans R Soc Lond B Biol Sci. 2014; 369:1471–2970.
19. Hernandez-Stefanoni JL, Gallardo-Cruz JA, Meave JA, Rocchini D, Bello-Pineda J, Lopez-Martinez JO. Modeling alpha- and beta-diversity in a tropical forest from remotely sensed and spatial data. Int J Appl Earth Obs. 2012; 19:359–68.
20. Muller J, Brandl R. Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. J Appl Ecol. 2009; 46(4):897–905.
21. Vierling KT, Bassler C, Brandl R, Vierling LA, Weiss I, Muller J. Spinning a laser web: predicting spider distributions using LiDAR. Ecol Appl. 2011; 21(2):577–88. PMID: 21563587
22. Garibaldi LA, Muchhala N, Motzke I, Bravo-Monroy L, Olschewski R, Klein A-M. Services from Plant—Pollinator interactions in the Neotropics. In: Rapidel B, DeClerck F, Le Coq J, Beer J, editors. Ecosystem services from agriculture and agroforestry: measurement and payment. London, UK: Earthscan; 2011. p. 119–39.
23. Allen-Wardell G, Bernhardt P, Bitner R, Burquez A, Buchmann S, Cane J, et al. The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv Biol. 1998; 12:8–17.
24. Roulston TH, Goodell K. The role of resources and risks in regulating wild bee populations. Annu Rev Entomol. 2011; 56:293–312. https://doi.org/10.1146/annurev-ento-120709-144802 PMID: 20822447
25. Batary P, Baldi A, Kleijn D, Tscharntke T. Landscape-moderated biodiversity effects of agri-environ-mental management: a meta-analysis. Proceedings of Biological Science. 2011; 278(1713):1894–902.
26. Holzschuh A, Steffan-Dewenter I, Kleijn D, Tscharntke T. Diversity of flower-visiting bees in cereal fields: effects of farming system, landscape composition and regional context. J Appl Ecol. 2007; 44 (1):41–9.
27. Boscolo D, Tokumoto PM, Ferreira PA, Ribeiro JW, DosSantos JS. Positive responses of flower visiting bees to landscape heterogeneity depend on functional connectivity levels. Perspectives in Ecology and Conservation. 2017; 15(1):18–24.
28. Frenzel M, Everaars J, Schweiger O. Bird communities in agricultural landscapes: What are the current drivers of temporal trends? Ecol Indic. 2016; 65:113–21.
29. Duelli P, Obrist MK, Schmatz DR. Biodiversity evaluation in agricultural landscapes: above-ground insects. Agric Ecosyst Environ. 1999; 74:33–64.
30. Papanikolaou AD, Kühn I, Frenzel M, Schweiger O. Semi-natural habitats mitigate the effects of temperature rise on wild bees. J Appl Ecol. 2017; 54:527–36.
31. Schweiger O, Maelfait JP, Van Wingerden W, Hendrickx F, Billeter R, Speelmans M, et al. Quantifying the impact of environmental factors on arthropod communities in agricultural landscapes across organizational levels and spatial scales. J Appl Ecol. 2005; 42(6):1129–39.
32. Masek JG, Vermote EF, Saleous N, Wolfe R, Hall FG, Huemmrich F, et al. LEDAPS Landsat calibration, reflectance, atmospheric correction preprocessing code. Oak Ridge, Tennessee, USA.: ORNL DAAC; 2012.
33. Tuanmu M-N, Jetz W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob Ecol Biogeogr. 2015; 24:1329–39.
34. McGarigal K, Marks BJ. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-351. USDA Forest Service, 1995.
35. Baker WL, Cai YM. The r.le programs for multiscale analysis of landscape structure using the Grass Geographical Information-System. Landscape Ecol. 1992; 7(4):291–302.
36. Hall-Beyer M. The GLCM Tutorial Home Page Calgary, Canada: University of Calgary; 2007. Available from: http://www.fp.ucalgary.ca/mhallbey/the_glcm.htm.
37. Beyer HL. Geospatial Modelling Environment (Version 0.7.3.0). 2012.
38. Blaszczynski JS. Landform characterization with geographic information systems. Photogramm Eng Remote Sensing. 1997; 63(2):183–91.
39. Riley SJ, D DS, Elliot R. A terrain ruggedness index that quantifies topographic heterogeneity. Intermt J Sci. 1999; 5:1–4.
40. Evans JS, Oakleaf J, Cushman SA, Theobald D. An ArcGIS Toolbox for Surface Gradient and Geomor-phometric Modeling, version 2.0–0 2014. Available from: http://evansmurphy.wix.com/evansspatial.
41. Anys H, Bannari A, He DC, Morin D, editors. Texture analysis for the mapping of urban areas using airborne MEIS-II images. Proceedings of the First International Airborne Remote Sensing Conference and Exhibition 3; 1994.
42. Hall-Beyer M. The GLCM tutorial home page. http://www.fp.ucalgary.ca/mhallbey/tutorial.htm [March 2016].
43. Patel D, Stonham TJ. Texture image classification and segmentation using RANK-order clustering. 11th IAPR International Conference on Pattern Recognition; The Hague, Netherlands: IEEE Computer Society Press; 1992. p. 92–5.
44. Warner T. Kernel-based Texture in Remote Sensing Image Classification. Geography Compass. 2011; 5(10):781–98.
45. Rocchini D, Delucchi L, Ricotta C, Ghisla A, Castellani C, Zorer R, et al. Measuring spatial diversity in a free algorithmic environment. FOSS4G; Barcelona, Spain2010.
46. RCoreTeam. R: A language and environment for statistical computing Vienna, Austria: R Foundation for Statistical Computing; 2016. Available from: https://www.R-project.org/.
47. Murray TE, Fitzpatrick Ú, Byrne A, Fealy R, Brown MJF, Paxton RJ. Local-scale factors structure wild bee communities in protected areas. J Appl Ecol. 2012; 49(5):998–1008.
48. Riedinger V, Mitesser O, Hovestadt T, Steffan-Dewenter I, Holzschuh A. Annual dynamics of wild bee densities attractiveness and productivity effects of oilseed rape. Ecology. 2015; 96(5):1351–60. PMID: 26236848
49. Hsieh TC, Ma KH, Chao A. iNEXT: iNterpolation and EXTrapolation for species diversity. R package version 2.0.12 URL: http://chao.stat.nthu.edu.tw/blog/software-download/. 2016.
50. Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, et al. Rarefaction and extrapolation with Hill numbers a framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014; 84(1):45–67.
51. Shannon CE. A mathematical theory of communication. The Bell System Technical Journal. 1948; 27:379–423 and 623–56.
52. >Lau MK. DiversitySampler: Functions for re-sampling a community matrix to compute diversity indices at different sampling levels. R package version 2.1. 2015. Available from: http://cran.r-project.org/web/packages/DiversitySampler/. 2015.
53. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952; 47:583–621.
54. Fox J, Weisberg S, Adler D, Bates D, Baud-Bovy G, Ellison S>, et al. Car: Companion to applied regression. R package version 2.1–3. http://www.CRAN.R-project.org/package=car. x ed. Vienna, Austria: R project; 2016.
55. Ripley R, Venables W, Bates D, Hornik K, Gebhardt A, Firth D. MASS: Support functions and datasets for venables and Ripley’s MASS. R package version 7.3–45. https://cran.r-project.org/web/packages/MASS/. 2016.
56. Barton K. MuMIn: Multi-model inference. R package version 1.16–0. http://CRAN.R-project.org/package=MuMIn. 2016.
57. Bjornstad ON. Ncf: Spatial nonparametric covariance functions. R package version 1.1–7. https://cran.r-project.org/web/packages/ncf/index.html. 2016.
58. Pinheiro J, Bates D, DebRoy S, Sarkar D, authors E, S. H, et al. Nlme: Linear and nonlinear mixed effects models. R package version 3.1–128. http://CRAN.R-project.org/package=nlme. 2016.
59. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York, NY: Springer US; 2002.
60. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010; 1(1):3–14.
61. Bjornstad O, Falck W. Nonparametric spatial covariance functions: Estimation and testing. Environ Ecol Stat. 2001; 8:53–70.
62. Johnson JB, Omland KS. Model selection in ecology and evolution. Trends Ecol Evol. 2004; 19:101–8. https://doi.org/10.1016/j.tree.2003.10.013 PMID: 16701236
63. Wallis CIB, Brehm G, Donoso DA, Fiedler JH, Paulsch D, Süßenbach D, et al. Remote sensing improves prediction of tropical montane speciesdiversity but performance differs among taxa. Ecol Indic. 2017. https://doi.org/10.1016/j.ecolind.2017.01.022
64. Wood EM, Pidgeon AM, Radeloff VC, Keuler NS. Image Texture Predicts Avian Density and Species Richness. PLOS One. 2013; 8(5): e63211. https://doi.org/10.1371/journal.pone.0063211 PMID: 23675463
65. Kuemmerle T, Hostert P, St-Louis V, Radeloff VC. Using image texture to map farmland field size: a case study in Eastern Europe. J Land Use Sci. 2009; 4(1–2):85–107.
66. Steckel J, Westphal C, Peters MK, Bellach M, Rothenwoehrer C, Erasmi S, et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol Conserv. 2014; 172:56–64.
67. Dufour A, Gadallah F, Wagner HH, Guisan A, Buttler A. Plant species richness and environmental heterogeneity in a mountain landscape: effects of variability and spatial configuration. Ecography. 2006; 29 (4):573–84.
68. Coblentz DD, Riitters KH. Topographic controls on the regional-scale biodiversity of the south-western USA. J Biogeogr. 2004; 31(7):1125–38.
69. Arellano G, Umaña MN, Macía MJ, Loza MI, Fuentes A, Cala V, et al. The role of niche overlap, environmental heterogeneity, landscape roughness and productivity in shaping species abundance distributions along the Amazon-Andes gradient. Glob Ecol Biogeogr. 2017; 26(2):191–202.
70. Duro DC, Coops NC, Wilder MA, Han T. Development of a large area biodiversity monitoring system driven by remote sensing. Prog Phys Geogr. 2007; 31(3):1–26.
71. Carmo FF, Campos IC, Jacobi CM. Effects of fine-scale surface heterogeneity on rock outcrop plant community structure. J Veg Sci. 2016; 27(1):50–9.
72. Luoto M, Virkkala R, Heikkinen RK, Rainio K. Predicting bird species richness using remote sensing in boreal agricultural-forest mosaics. Ecol Appl. 2004; 14(6):1946–62.
73. Marini L, Bona E, Kunin WE, Gaston KJ. Exploring anthropogenic and natural processes shaping fern species richness along elevational gradients. J Biogeogr. 2011; 38(1):78–88.
74. Wrbka T, Erb KH, Schulz NB, Peterseil J, Hahn C, Haberl H. Linking pattern and process in cultural landscapes. An empirical study based on spatially explicit indicators. Land Use Policy. 2004; 21 (3):289–306.
75. Holzschuh A, Dormann CF, Tscharntke T, Staffan-Dewenter I. Mass-flowering crops enhance wild bee abundance. Oecologia. 2013; 172:477–84. https://doi.org/10.1007/s00442-012-2515-5 PMID: 23114428
76. Torne-Noguera A, Rodrigo A, Arnan X, Osorio S, Barril-Graells H, da Rocha-Filho LC, et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLOS One. 2014; 9(5):e97255. https://doi.org/10.1371/journal.pone.0097255 PMID: 24824445
77. Breitbach N, Tillmann S, Schleuning M, Grunewald C, Laube I, Steffan-Dewenter I, et al. Influence of habitat complexity and landscape configuration on pollination and seed-dispersal interactions of wild cherry trees. Oecologia. 2012; 168(2):425–37. https://doi.org/10.1007/s00442-011-2090-1 PMID: 21818655
78. Gallardo-Cruz JA, Meave JA, Gonzalez EJ, Lebrija-Trejos EE, Romero-Romero MA, Perez-Garcia EA, et al. Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture? PLoS One. 2012; 7(2). https://doi.org/10.1371/journal.pone.0030506 PMID: 22363443
79. Chapman RN, Engle DM, Masters RE, Leslie DM. Grassland vegetation and bird communities in the southern Great Plains of North America. Agr Ecosyst Environ. 2004; 104(3):577–85.
80. Jayapal R, Qureshi Q, Chellam R. Importance of forest structure versus floristics to composition of avian assemblages in tropical deciduous forests of Central Highlands, India. Forest Ecol Manag. 2009; 257(11):2287–95.
81. Lee PY, Rotenberry JT. Relationships between bird species and tree species assemblages in forested habitats of eastern North America. J Biogeogr. 2005; 32(7):1139–50.