778
Views
2
CrossRef citations to date
0
Altmetric
Letter to the Editor

AI will change EA practice – but are we ready for it? A call for discussion based on developments in collecting and processing biodiversity data

ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Pages 200-208 | Received 17 Jul 2023, Accepted 08 Feb 2024, Published online: 16 Feb 2024

References

  • Banhalmi-Zakar Z, Gronow C, Wilkinson L, Jenkins B, Pope J, Squires G, Witt K, Williams G, Womersley J. 2018. Evolution or revolution: where next for impact assessment? Impact Assess Proj Apprais. 36:506–515. doi: 10.1080/14615517.2018.1516846.
  • Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF2022. Towards the fully automated monitoring of ecological communities. Ecol Lett eng. [Epub 2022 Oct 20]. 25:2753–2775. doi: 10.1111/ele.14123.
  • Bice S, Fischer TB. 2020. Impact assessment for the 21st century – what future? Impact Assess Proj Apprais. 38:89–93. doi: 10.1080/14615517.2020.1731202.
  • Bohnett E, Poya Faryabi S, Lewison R, An L, Bian X, Rajabi AM, Jahed N, Rooyesh H, Mills E, Ramos S, et al. 2023. Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies. Global Ecol Conserv. 41:e02350. doi: 10.1016/j.gecco.2022.e02350.
  • Bond A, Dusík J. 2020. Impact assessment for the twenty-first century – rising to the challenge. Impact Assess Proj Apprais. 38:94–99. doi: 10.1080/14615517.2019.1677083.
  • Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE. 2022. Deep learning as a tool for ecology and evolution. Methods Ecol Evol. 13:1640–1660. doi: 10.1111/2041-210X.13901.
  • Bubnicki JW, Angelstam P, Mikusiński G, Svensson J, Jonsson BG. 2023. Mapping forests with different levels of naturalness using machine learning and landscape data mining. [place unknown]: [publisher unknown]. doi: 10.1101/2023.07.30.551142.
  • Cilliers DP, Retief FP, Bond AJ, Roos C, Alberts RC. 2022. The validity of spatial data-based EIA screening decisions. Environ Impact Assess Rev. 93:106729. doi: 10.1016/j.eiar.2021.106729.
  • Curmally A, Sandwidi BW, Jagtiani A. 2022. Artificial intelligence solutions for environmental and social impact assessments. In: Fonseca A, editor. Handbook of Environmental Impact Assessment. Cheltenham: Edward Elgar. doi: 10.4337/9781800379633.
  • Dias AM, Cook C, Massara RL, Paglia AP. 2022. Are environmental impact assessments effectively addressing the biodiversity issues in Brazil? Environ Impact Assess Rev. 95:106801. doi: 10.1016/j.eiar.2022.106801.
  • Díaz-Rodríguez N, Del Ser J, Coeckelbergh M, López De Prado M, Herrera-Viedma E, Herrera F. 2023. Connecting the dots in trustworthy artificial intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf Fusion. 99:101896. doi: 10.1016/j.inffus.2023.101896.
  • European Commission. 2022. RE Power EU plan. Communication from the commission to the European Parliament, the European Council, the Council. COM. 230. final https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52022DC0230
  • Farley SS, Dawson A, Goring SJ, Williams JW. 2018. Situating ecology as a big-data science: Current advances, challenges, and solutions. Bio Sci. 68:563–576. doi: 10.1093/biosci/biy068.
  • Feng X, Jiang Y, Yang X, Du M, Li X. 2019. Computer vision algorithms and hardware implementations: a survey. Integration. 69:309–320. doi: 10.1016/j.vlsi.2019.07.005.
  • Figueiredo Gallardo ALC, Aparecida da Conceição Dos Santos C, Bond A, Mateus Moretto E, Montaño M, Athayde S. 2022. Translating best practice principles into criteria for evaluating the consideration of biodiversity in SEA practice. Impact Assess Proj Apprais. 40(5):437–449. doi: 10.1080/14615517.2022.2084231.
  • Fonseca A. 2022. The benefits and perils of digital and automated technologies: impact assessment methods in the fourth industrial revolution. In: Fonseca A, editor. Handbook of environmental impact assessment. Cheltenham UK, Northampton MA: Edward Elgar Publishing Limited; p. 126–145. Research handbooks on impact assessment series.
  • Fothergill J, Murphy J 2021. The state of digital impact assessment: a global review of the uptake of digital technologies and approaches within impact assessment practice. [Place Unknown]: [Publisher Unknown] 82 p; [accessed 2023 Jul 11]. https://iaia.org/downloads/State%20of%20Digital%20IA%20Practice_converted.pdf.
  • Gachechiladze-Bozhesku M, Fischer TB. 2012. Benefits of and barriers to SEA follow-up — theory and practice. Environ Impact Assess Rev. 34:22–30. doi: 10.1016/j.eiar.2011.11.006.
  • Garigliotti D, Bjerva J, Årup Nielsen F, Butzbach A, Lyhne I, Kørnøv L, Hose K 2023. Do bridges dream of water pollutants? Towards DreamsKG, a knowledge graph to make digital access for sustainable environmental assessment come true. In: Ding Y, editor. Companion Proceedings of the ACM Web Conference 2023. WWW ’23: The ACM Web Conference 2023; 30 04 2023 04 05 2023; Austin TX USA. Tang J, Sequeda J, Aroyo L, Castillo C, Houben G-J., translators. New York,NY,United States: Association for Computing Machinery; p. 724–730 (ACM Digital Library).
  • Geissler G, Jiricka-Pürrer A. 2023. The future of impact assessment in Austria and Germany – streamlining impact assessment to save the planet? Impact Assess Proj Apprais. 41 (3):215–222. doi: 10.1080/14615517.2023.2186595.
  • Geissler G, Köppel J, Grimm M. 2022. The European union environmental impact assessment directive. Strengths and weaknesses of current practice. In: Hanna K, editor. Routledge handbook of environmental impact assessment. London and New York: Routledge; p. 282–301. chapter 16.
  • Ghani B, Denton T, Kahl S, Klinck H. 2023. Global birdsong embeddings enable superior transfer learning for bioacoustic classification. Sci Rep. 13(1):22876. doi: 10.1038/s41598-023-49989-z.
  • Glover-Kapfer P, Soto-Navarro CA, Wearn OR, Rowcliffe M, Sollmann R. 2019. Camera-trapping version 3.0: current constraints and future priorities for development. Remote Sens Ecol Conserv. 5(3):209–223. doi: 10.1002/rse2.106.
  • González Del Campo A. 2012. GIS in environmental assessment: A Review of current issues and future needs. J Env Assmt Pol Mgmt. 14:1250007. doi: 10.1142/S146433321250007X.
  • González Del Campo A, Gazzola P. 2020. Untapping the potential of technological advancements in strategic environmental assessment. J Environ Plann Manage. 63:585–603. doi: 10.1080/09640568.2019.1588712.
  • Hejlová V, Voženílek V. 2013. Wireless sensor network components for air pollution monitoring in the urban environment: criteria and analysis for their selection. WSN. 5(12):229–240. doi: 10.4236/wsn.2013.512027.
  • Hileman JD, Angst M, Scott TA, Sundström E. 2021. Recycled text and risk communication in natural gas pipeline environmental impact assessments. Energy Policy. 156:112379. doi: 10.1016/j.enpol.2021.112379.
  • Hill AP, Davies A, Prince P, Snaddon JL, Doncaster CP, Rogers A. 2019. Leveraging conservation action with open‐source hardware.Conservation letters. p. 12. doi: 10.1111/conl.12661.
  • Howard J. 2019. Artificial intelligence: implications for the future of work. Am J Ind Med. eng. 62: 917–926. Epub 2019 Aug 22. doi: 10.1002/ajim.23037
  • Jetz W, Tertitski G, Kays R, Mueller U, Wikelski M, Åkesson, S., Anisimov, Y, Antonov, A, Arnold, W, Bairlein, F, Baltà, O 2022. Biological earth observation with animal sensors. Trends In Ecology & Evolution. 37(4):293–298. doi: 10.1016/j.tree.2021.11.011. eng.
  • Kays R, Wikelski M2023. The internet of animals: what it is, what it could be. Trends Ecol Evol eng. [Epub 2023 May 30]. 38:859–869. doi: 10.1016/j.tree.2023.04.007.
  • Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, Bell M, Norford L, Britter R. 2015. The rise of low-cost sensing for managing air pollution in cities. Environ Int. eng. 75: 199–205. Epub 2014 Dec 5. doi: 10.1016/j.envint.2014.11.019
  • Lahoz-Monfort JJ, Magrath MJL. 2021. A comprehensive overview of technologies for species and habitat monitoring and conservation. BioScience. eng. 71: 1038–1062. Epub 2021 Jul 28. doi: 10.1093/biosci/biab073
  • Lyhne I, Nielsen PA, Hose K, Kørnøv L. 2022. Digitalization of environmental assessment: the Danish ecosystem approach. UVP-report. 36(2):63–69.
  • Machlev R, Heistrene L, Perl M, Levy KY, Belikov J, Mannor S, Levron Y. 2022. Explainable artificial intelligence (XAI) techniques for energy and power systems: review, challenges and opportunities. Energy AI. 9:100169. doi: 10.1016/j.egyai.2022.100169.
  • Perry GLW, Seidl R, Bellvé AM, Rammer W. 2022. An outlook for deep learning in ecosystem science. Ecosystems. 25:1700–1718. doi: 10.1007/s10021-022-00789-y.
  • Ravn Bøss E, Kørnøv L, Lyhne I, Partidário MR. 2021. Integrating SDGs in environmental assessment: Unfolding SDG functions in emerging practices. Environ Impact Assess Rev. 90:106632. doi: 10.1016/j.eiar.2021.106632.
  • Rhinehart TA, Chronister LM, Devlin T, Kitzes J. 2020. Acoustic localization of terrestrial wildlife: Current practices and future opportunities. Ecol Evol. 10(13):6794–6818. doi: 10.1002/ece3.6216.
  • Salman MY, Hasar H. 2023. Review on environmental aspects in smart city concept: water, waste, air pollution and transportation smart applications using IoT techniques. Sustain Cities Soc. 94:104567. doi: 10.1016/j.scs.2023.104567.
  • Samek W. 2023. Explainable deep learning: concepts, methods, and new developments. In: Benois-Pineau J, Bourqui R, Petkovic D, Quénot G, editors. Expl Deep Learning AI. Cambridge: Academic Press; p. 7–33 .
  • Schneider K, Makowski D, van der Werf W. 2021. Predicting hotspots for invasive species introduction in Europe. Environ Res Lett. 16:114026. doi: 10.1088/1748-9326/ac2f19.
  • Scott TA. 2018. Flexible, collaborative, and meaningful? The case of the US coastal nonpoint pollution control program. J Environ Plann Manage. 61:272–290. doi: 10.1080/09640568.2017.1301896.
  • Scott TA, Ulibarri N, Scott RP. 2020. Stakeholder involvement in collaborative regulatory processes: using automated coding to track attendance and actions. Regul Gov. 14:219–237. doi: 10.1111/rego.12199.
  • Speaker T, O’Donnell S, Wittemyer G, Bruyere B, Loucks C, Dancer A, Carter M, Fegraus E, Palmer J, Warren E, et al. 2022. A global community-sourced assessment of the state of conservation technology. Conserv Biol eng. [Epub 2022 Feb 3] 36:e13871. doi: 10.1111/cobi.13871.
  • Stroud S, Fennell M, Mitchley J, Lydon S, Peacock J, Bacon KL. 2022. The botanical education extinction and the fall of plant awareness. Ecol Evol. eng. 12: e9019. Epub 2022 Jul 10. doi: 10.1002/ece3.9019
  • Tabak MA, Norouzzadeh MS, Wolfson DW, Sweeney SJ, Vercauteren KC, Snow NP, Halseth JM, Di Salvo PA, Lewis JS, White MD, et al. 2019. Machine learning to classify animal species in camera trap images: applications in ecology. Methods Ecol Evol. 10(4):585–590. doi:10.1111/2041-210X.13120.
  • Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, et al. 2022. Perspectives in machine learning for wildlife conservation. Nat Commun eng. [Epub 2022 Feb 9] 13:792. doi: 10.1038/s41467-022-27980-y.
  • Uhlhorn B, Geissler G, Jiricka-Pürrer A. 2023. Is advanced digitalisation the philosopher’s stone or a complex challenge? – experiences from Austrian and German EA practice. SSRN Preprint. doi: 10.2139/ssrn.4486117.
  • Ulibarri N, Scott TA, Perez-Figueroa O. 2019. How does stakeholder involvement affect environmental impact assessment? Environ Impact Assess Rev. 79:106309. doi: 10.1016/j.eiar.2019.106309.
  • Urbano F, Cagnacci F. 2021. Data management and sharing for collaborative science: lessons learnt from the Euromammals Initiative. Front Ecol Evol. 9. doi: 10.3389/fevo.2021.727023.
  • van Klink R, August T, Bas Y, Bodesheim P, Bonn A, Fossøy F, Høye TT, Jongejans E, Menz MHM, Miraldo A, et al. 2022. Emerging technologies revolutionise insect ecology and monitoring. Trends Ecol Evol eng. [Epub 2022 Jul 8] 37:872–885. doi: 10.1016/j.tree.2022.06.001.
  • Wägele J, Bodesheim P, Bourlat SJ, Denzler J, Diepenbroek M, Fonseca V, Frommolt K-H, Geiger MF, Gemeinholzer B, Glöckner FO, et al. 2022. Towards a multisensor station for automated biodiversity monitoring. Basic Appl Ecol. 59:105–138. doi: 10.1016/j.baae.2022.01.003.
  • Wieczorek J, Bloom D, Guralnick R, Blum S, Döring M, Giovanni R, Robertson T, Vieglais D, Sarkar IN. 2012. Darwin Core: an evolving community-developed biodiversity data standard. PloS One. eng. Epub 2012 Jan 6. 7(1): e29715. 10.1371/journal.pone.0029715
  • Wild TA, van Schalkwyk L, Viljoen P, Heine G, Richter N, Vorneweg B, Koblitz JC, Dechmann DKN, Rogers W, Partecke J, et al. 2023. A multi-species evaluation of digital wildlife monitoring using the sigfox IoT network. Anim Biotelemetry. 11(1):11. doi:10.1186/s40317-023-00326-1.
  • Xing X, Yuan Y, Huang Z, Peng X, Zhao P, Liu Y. 2022. Flow trace: a novel representation of intra-urban movement dynamics. Comput Environ Urban Syst. 96:101832. doi: 10.1016/j.compenvurbsys.2022.101832.
  • Yap W, Janssen P, Biljecki F. 2022. Free and open source urbanism: software for urban planning practice. Comput Environ Urban Syst. 96:101825. doi: 10.1016/j.compenvurbsys.2022.101825.
  • Yüksel N, Börklü HR, Sezer HK, Canyurt OE. 2023. Review of artificial intelligence applications in engineering design perspective. Eng Appl Artif Intell. 118:105697. doi: 10.1016/j.engappai.2022.105697.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.