Identifying priority sites for insect conservation in forest ecosystems at high resolution: the potential of LiDAR data

Journal of Insect Conservation volume 23, pages689–698(2019)Cite this article

Abstract

Focusing conservation efforts on priority areas is crucial for maximizing the effects of the limited available resources. Unfortunately, there is often a conflict between the optimal spatial scale for conservation measures and data available for planning. We tested the potential contribution of LiDAR data in providing high-resolution environmental predictors for conservation planning. We used detailed samples of ground beetles communities and high-resolution environmental data (i.e. land morphology, forest structure, and water availability) for modeling the habitat suitability for each species. All the specific models were combined to define priority areas for the conservation of carabids at the local level and to clarify the effect of each environmental feature on the forest suitability for ground beetles. The highest levels of suitability were obtained in less steep and north facing zones and in those where trees are taller, total volume of trees is larger, and tree density is lower. These parameters should be taken into account in sustainable forest management options aimed to preserve carabid communities. Moreover, our approach allowed to find exactly where, within the considered forest stands, the adoption of these measures would be particularly efficient. Our study is a demonstrative case that can be adapted to different taxa and different areas for improving the efficacy of forest management and of biodiversity conservation initiatives.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Ackers SH, Davis RJ, Olsen KA, Dugger KM (2015) The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): a comparison of photo-interpreted, landsat-based, and lidar-based habitat maps. Remote Sens Environ 156:361–373. https://doi.org/10.1016/j.rse.2014.09.025

    Article  Google Scholar 

  2. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688. https://doi.org/10.1111/j.1365-2699.2006.01584.x

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  4. Araújo MB, Thuiller W, Williams PH, Reginster I (2005) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Glob Ecol Biogeogr 14:17–30. https://doi.org/10.1111/j.1466-822X.2004.00128.x

    Article  Google Scholar 

  5. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118. https://doi.org/10.1016/S0304-3800(02)00205-3

    Article  Google Scholar 

  6. Bae S, Reineking B, Ewald M, Mueller J (2014) Comparison of airborne lidar, aerial photography, and field surveys to model the habitat suitability of a cryptic forest species—the hazel grouse. Int J Remote Sens 35:6469–6489. https://doi.org/10.1080/01431161.2014.955145

    Article  Google Scholar 

  7. Baker SC, Barmuta LA (2006) Evaluating spatial autocorrelation and depletion in pitfall-trap studies of environmental gradients. J Insect Conserv 10:269–276. https://doi.org/10.1007/s10841-006-0016-8

    Article  Google Scholar 

  8. Baselga A (2012) The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob Ecol Biogeogr 21:1223–1232

    Article  Google Scholar 

  9. Baselga A, Orme CDL (2012) betapart: an R package for the study of beta diversity. Methods Ecol Evol 3:808–812

    Article  Google Scholar 

  10. Belsley DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York

    Google Scholar 

  11. Bergen KM, Goetz SJ, Dubayah RO, Henebry GM, Hunsaker CT, Imhoff ML, Nelson RF, Parker GG, Radeloff VC (2009) Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions. J Geophys Res: Biogeosci 114:1–13. https://doi.org/10.1029/2008JG000883

    Article  Google Scholar 

  12. Betts MG, Diamond AW, Forbes GJ, Villard MA, Gunn JS (2006) The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence. Ecol Model 191:197–224. https://doi.org/10.1016/j.ecolmodel.2005.04.027

    Article  Google Scholar 

  13. Bombi P, D’Amen M (2012) Scaling down distribution maps from atlas data: a test of different approaches with virtual species. J Biogeogr 39:640–651. https://doi.org/10.1111/j.1365-2699.2011.02627.x

    Article  Google Scholar 

  14. Bombi P, Salvi D, Bologna MA (2012) Cross-scale predictions allow the identification of local conservation priorities from atlas data. Anim Conserv 15:378–387. https://doi.org/10.1111/j.1469-1795.2012.00526.x

    Article  Google Scholar 

  15. Bombi P, D’Amen M, Luiselli L (2013) From continental priorities to local conservation: a multi-level analysis for African tortoises. PLoS ONE 8:1–9. https://doi.org/10.1371/journal.pone.0077093

    Article  CAS  Google Scholar 

  16. Bradbury RB, Hill RA, Mason DC et al (2005) Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments. Ibis 147:443–452. https://doi.org/10.1111/j.1474-919x.2005.00438.x

    Article  Google Scholar 

  17. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  18. Brooks TM, Bakarr MI, Boucher T et al (2004) Coverage provided by the global protected-area system: is it enough? Bioscience 54:1081. https://doi.org/10.1641/0006-3568(2004)054%5b1081:CPBTGP%5d2.0.CO;2

    Article  Google Scholar 

  19. Chauve A, Vega C, Bretar F, Durrieu S, Allouis T, Pierrot-Deseilligny M, Puech W (2009) Processing full waveform lidar data in an alpine coniferous forest: assessing terrain and tree height quality. Int J Remote Sens 30(19):5211–5228

    Article  Google Scholar 

  20. D’Amen M, Bombi P, Pearman PB et al (2011) Will climate change reduce the efficacy of protected areas for amphibian conservation in Italy? Biol Cons 144:989–997. https://doi.org/10.1016/j.biocon.2010.11.004

    Article  Google Scholar 

  21. Davies AB, Asner GP (2014) Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol Evol 29:681–691. https://doi.org/10.1016/j.tree.2014.10.005

    Article  PubMed  Google Scholar 

  22. Digweed SC, Currie CR, Carcamo HA, Spence JR (1995) Digging out the digging-in effect of pitfall traps: influences depletion and disturbance on catches of ground beetles (Coleoptera: Carabidae). Pedobiologia 39:561–576

    Google Scholar 

  23. Dobson AJ (1990) An introduction to generalized linear models. Chapman and Hall, London

    Google Scholar 

  24. Dorren L (2014) FINT—find individual trees. User manual. ecorisQ paper (www.ecorisq.org), 5 p

  25. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159

    Article  Google Scholar 

  26. Farrell SL, Collier BA, Skow KL et al (2013) Using LiDAR-derived vegetation metrics for high-resolution, species distribution models for conservation planning. Ecosphere 4:1–18. https://doi.org/10.1890/ES12-000352.1

    Article  Google Scholar 

  27. Ferrier S (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst Biol 51:331–363. https://doi.org/10.1080/10635150252899806

    Article  PubMed  Google Scholar 

  28. Ficetola GF, Bonardi A, Mücher CA, Gilissen NLM, Padoa-Schioppa E (2014) How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height. Int J Geogr Inf Sci 28:1723–1739. https://doi.org/10.1080/13658816.2014.891222

    Article  Google Scholar 

  29. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49. https://doi.org/10.1017/S0376892997000088

    Article  Google Scholar 

  30. Fragoso JMV (1999) Perception of scale and resource partitioning by peccaries: behavioral causes and ecological implications. J Mammal 80:993–1003. https://doi.org/10.2307/1383270

    Article  Google Scholar 

  31. Gnetti V, Bombi P, Vigna Taglianti A et al (2015) Temporal dynamic of a ground beetle community of Eastern Alps (Coleoptera Carabidae). Bull Insectol 68:299–309

    Google Scholar 

  32. Graham CH, Ferrier S, Huettmann F, Moritz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503. https://doi.org/10.1016/j.tree.2004.07.006

    Article  PubMed  Google Scholar 

  33. Groves C (2003) Drafting a conservation blueprint: a practitioner’s guide to planning for biodiversity. Island Press, Washington, DC

    Google Scholar 

  34. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x

    Article  Google Scholar 

  35. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  36. Hanson JO, Schuster R, Morrell N et al (2018) prioritizr: systematic conservation prioritization in R. R package version 4.0.2.1. https://github.com/prioritizr/prioritizr

  37. Hortal J, Borges PAV, Gaspar C (2006) Evaluating the performance of species richness estimators: sensitivity to sample grain size. J Anim Ecol 75:274–287. https://doi.org/10.1111/j.1365-2656.2006.01048.x

    Article  PubMed  Google Scholar 

  38. Huber PR, Greco SE, Thorne JH (2010) Spatial scale effects on conservation network design: trade-offs and omissions in regional versus local scale planning. Landsc Ecol 25:683–695. https://doi.org/10.1007/s10980-010-9447-4

    Article  Google Scholar 

  39. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11:37–50

    Article  Google Scholar 

  40. Jukes MR, Peace AJ, Ferris R (2001) Carabid beetle communities associated with coniferous plantations in Britain: the influence of site, ground vegetation and stand structure. For Ecol Manage 148:271–286

    Article  Google Scholar 

  41. Kane VR, McGaughey RJ, Bakker JD et al (2010) Comparisons between field- and LiDAR-based measures of stand structural complexity. Can J For Res 40:761–773. https://doi.org/10.1139/X10-024

    Article  Google Scholar 

  42. Kienast F, Brzeziecki B, Wildi O (1996) Long-term adaptation potential of Central European mountain forests to climate change: a GIS-assisted sensitivity assessment. For Ecol Manage 80:133–153. https://doi.org/10.1016/0378-1127(95)03633-4

    Article  Google Scholar 

  43. Koivula MJ (2011) Useful model organisms, indicators, or both? Ground beetles (Coleoptera, Carabidae) reflecting environmental conditions. ZooKeys 100:287–317. https://doi.org/10.3897/zookeys.100.1533

    Article  Google Scholar 

  44. Koleff P, Gaston KJ, Lennon JK (2003) Measuring beta diversity for presence–absence data. J Anim Ecol 72:367–382

    Article  Google Scholar 

  45. Kukkala AS, Moilanen A (2013) Core concepts of spatial prioritisation in systematic conservation planning. Biol Rev 88:443–464. https://doi.org/10.1111/brv.12008

    Article  PubMed  Google Scholar 

  46. Lange M, Türke M, Pašalić E et al (2014) Effects of forest management on ground-dwelling beetles (Coleoptera; Carabidae, Staphylinidae) in Central Europe are mainly mediated by changes in forest structure. For Ecol Manage 329:166–176

    Article  Google Scholar 

  47. Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002) Lidar remote sensing of above-ground biomass in three biomes. Glob Ecol Biogeogr 11:393–399. https://doi.org/10.1046/j.1466-822x.2002.00303.x

    Article  Google Scholar 

  48. Lefsky MA, Hudak AT, Cohen WB, Acker SA (2005) Patterns of covariance between forest stand and canopy structure in the Pacific Northwest. Remote Sens Environ 95:517–531. https://doi.org/10.1016/j.rse.2005.01.004

    Article  Google Scholar 

  49. Levin N, Watson JEM, Joseph LN et al (2013) A framework for systematic conservation planning and management of Mediterranean landscapes. Biol Conserv 158:371–383. https://doi.org/10.1016/j.biocon.2012.08.032

    Article  Google Scholar 

  50. Loiselle BA, Howell CA, Graham CH et al (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600

    Article  Google Scholar 

  51. Lövei GL, Sunderland KD (1996) Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annu Rev Entomol 41:231–256

    Article  PubMed  Google Scholar 

  52. Magura T, Tothmeresz B, Bordan Z (2000) Effects of nature management practice on carabid assemblages (Coleoptera: Carabidae) in a non-native plantation. Biol Conserv 93:95–102. https://doi.org/10.1016/S0006-3207(99)00073-7

    Article  Google Scholar 

  53. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253. https://doi.org/10.1038/35012251

    Article  CAS  PubMed  Google Scholar 

  54. Meller L, Cabeza M, Pironon S et al (2014) Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks. Divers Distrib 20:309–321. https://doi.org/10.1111/ddi.12162

    Article  PubMed  PubMed Central  Google Scholar 

  55. Monterroso P, Sillero N, Rosalino LM, Loureiro F, Alves PC (2013) Estimating home-range size: when to include a third dimension? Ecol Evol 3:2285–2295. https://doi.org/10.1002/ece3.590

    Article  PubMed  PubMed Central  Google Scholar 

  56. Morales-Castilla I, Matias MG, Gravel D, Araújo MB (2015) Inferring biotic interactions from proxies. Trends Ecol Evol 30:347–356

    Article  PubMed  Google Scholar 

  57. Mourelle C, Ezcurra E (1996) Species richness of Argentine Cacti: a test of biogeographic hypotheses. J Veg Sci 7:667–680. https://doi.org/10.2307/3236378

    Article  Google Scholar 

  58. Müller J, Brandl R (2009) Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. J Appl Ecol 46:897–905. https://doi.org/10.1111/j.1365-2664.2009.01677.x

    Article  Google Scholar 

  59. North MP, Kane JT, Kane VR et al (2017) Cover of tall trees best predicts California spotted owl habitat. For Ecol Manage 405:166–178. https://doi.org/10.1016/j.foreco.2017.09.019

    Article  Google Scholar 

  60. O’Hara RB (2005) Species richness estimators: how many species can dance on the head of a pin? J Anim Ecol 74:375–386

    Article  Google Scholar 

  61. Pearce JL, Venier LA (2006) The use of ground beetles (Coleoptera: Carabidae) and spiders (Araneae) as bioindicators of sustainable forest management: a review. Ecol Ind 6:780–793. https://doi.org/10.1016/j.ecolind.2005.03.005

    Article  Google Scholar 

  62. Pearson RG, Dawson TP, Liu C (2004) Modelling species distribution in Britain: a hierarchical integration of climate and land cover. Ecography 27:285–298. https://doi.org/10.1111/j.0906-7590.2004.03740.x

    Article  Google Scholar 

  63. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

    Article  Google Scholar 

  64. Pradervand JN, Dubuis A, Pellissier L, Guisan A, Randin C (2013) Very high resolution environmental predictors in species distribution models: moving beyond topography? Prog Phys Geogr 38:79–96. https://doi.org/10.1177/0309133313512667

    Article  Google Scholar 

  65. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  66. Rabb GB, Sullivan TA (1995) Coordinating conservation: global networking for species survival. Rev Ind Organ 4:536–543. https://doi.org/10.1007/BF00222512

    Article  Google Scholar 

  67. Razgour O, Hanmer J, Jones G (2011) Using multi-scale modelling to predict habitat suitability for species of conservation concern: the grey long-eared bat as a case study. Biol Conserv 144:2922–2930. https://doi.org/10.1016/j.biocon.2011.08.010

    Article  Google Scholar 

  68. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge

    Google Scholar 

  69. Rodrigues ASL, Andelman SJ, Bakarr MI et al (2004) Effectiveness of the global protected area network in representing species diversity. Nature 428:640–643

    Article  CAS  PubMed  Google Scholar 

  70. Scherrer D, Schmid S, Körner C (2011) Elevational species shifts in a warmer climate are overestimated when based on weather station data. Int J Biometeorol 55:645–654. https://doi.org/10.1007/s00484-010-0364-7

    Article  PubMed  Google Scholar 

  71. Sillero N, Gonçalves-Seco L (2014) Spatial structure analysis of a reptile community with airborne LiDAR data. Int J Geogra Inf Sci 28:1709–1722. https://doi.org/10.1080/13658816.2014.902062

    Article  Google Scholar 

  72. Simonson WD, Allen HD, Coomes DA (2014) Applications of airborne lidar for the assessment of animal species diversity. Methods Ecol Evol 5:719–729. https://doi.org/10.1111/2041-210X.12219

    Article  Google Scholar 

  73. Taki H, Inoue T, Tanaka H et al (2010) Responses of community structure, diversity, and abundance of understory plants and insect assemblages to thinning in plantations. For Ecol Manage 259:607–613. https://doi.org/10.1016/j.foreco.2009.11.019

    Article  Google Scholar 

  74. Thuiller W, Georges D, Engler R, Breiner F (2016) biomod2: ensemble platform for species distribution modeling

  75. Timonen J, Siitonen J, Gustafsson L et al (2010) Woodland key habitats in northern Europe: concepts, inventory and protection. Scand J For Res 25:309–324. https://doi.org/10.1080/02827581.2010.497160

    Article  Google Scholar 

  76. Vane-Wright RI, Humphries CJ, Williams PH et al (1991) What to protect?—systematics and the agony of choice. Biol Conserv 55:235–254. https://doi.org/10.1016/0006-3207(91)90030-D

    Article  Google Scholar 

  77. Veloz S, Salas L, Altman B et al (2015) Improving effectiveness of systematic conservation planning with density data. Conserv Biol 29:1217–1227. https://doi.org/10.1111/cobi.12499

    Article  PubMed  Google Scholar 

  78. Vierling KT, Vierling LA, Gould WA, Martinuzzi S, Clawges RM (2008) Lidar: shedding new light on habitat characterization and modeling. Front Ecol Environ 6:90–98. https://doi.org/10.1890/070001

    Article  Google Scholar 

  79. Wagner W, Ullrich A, Melzer T, Briese C, Kraus K (2004) From single-pulse to full-waveform airborne laser scanners: potential and practical challenges. Int Arch Photogramm Remote Sens Spat Inf Sci 35:201–206

    Google Scholar 

  80. Wiens JA, Bachelet D (2010) Matching the multiple scales of conservation with the multiple scales of climate change: special section. Conserv Biol 24:51–62. https://doi.org/10.1111/j.1523-1739.2009.01409.x

    Article  PubMed  Google Scholar 

  81. Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x

    Article  PubMed  Google Scholar 

  82. Work TT, Benoit S, Jacobs JM (2011) Response of female beetles to LIDAR derived topographic variables in Eastern boreal mixedwood forests (Coleoptera, Carabidae). ZooKeys 147:623–639. https://doi.org/10.3897/zookeys.147.2013

    Article  Google Scholar 

  83. Zhang MG, Zhou ZK, Chen WY, Slik JWF, Cannon CH, Raes N (2012) Using species distribution modeling to improve conservation and land use planning of Yunnan, China. Biol Conserv 153:257–264. https://doi.org/10.1016/j.biocon.2012.04.023

    Article  Google Scholar 

Download references

Acknowledgements

We thank Nicolao Bortolo for the logistic assistance and Valerio Muzzini and Gian Ernesto Feltrin for the collaboration in the field. This study was supported by the LIFE Project ManFor C.BD (Grant No. LIFE09ENV/IT/000078). Data analysis approach were developed within the Mediterranean Thematic Center of LifeWatch-ITA.

Author information

Affiliations

  1. National Research Council of Italy - Institute of Research on Terrestrial Ecosystems (CNR-IRET), via Salaria km 29.300, Monterotondo, 00015, Rome, Italy

    Pierluigi Bombi, Vittoria Gnetti, Ettore D’Andrea & Bruno De Cinti

  2. Università Roma Tre - Dipartimento di Scienze, Rome, Italy

    Vittoria Gnetti & Marco A. Bologna

  3. Sapienza” Università di Roma - Dipartimento di Biologia e Biotecnologie, Rome, Italy

    Augusto Vigna Taglianti

  4. National Research Council of Italy - Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM), Ercolano, NA, Italy

    Giorgio Matteucci

Authors
  1. Pierluigi Bombi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vittoria Gnetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ettore D’Andrea
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bruno De Cinti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Augusto Vigna Taglianti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marco A. Bologna
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giorgio Matteucci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pierluigi Bombi.

Ethics declarations

Conflict of interest

There are no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bombi, P., Gnetti, V., D’Andrea, E. et al. Identifying priority sites for insect conservation in forest ecosystems at high resolution: the potential of LiDAR data. J Insect Conserv 23, 689–698 (2019). https://doi.org/10.1007/s10841-019-00162-w

Download citation

Keywords

  • Remote sensing
  • Ground beetles
  • Habitat suitability
  • Complementarity
  • Forest structure