6. References

Andrew, M. E., Wulder, M. A. and Coops, N. C. (2012). Identification of de facto protected areas in boreal Canada. Biological Conservation , 146(1), pp.97–107. DOI: 10.1016/j.biocon.2011.11.029
Arriaga, L., Castellanos, A. E., Moreno, E. and Alarcón, J. (2004). Potential ecological distribution of alien invasive species and risk assessment: a case study of buffel grass in arid regions of Mexico.Conservation Biology , 18(6), pp.1504–1514. DOI: 10.1111/j.1523-1739.2004.00166.x
Azzurro, E., Soto, S., Garofalo, G. and Maynou, F. (2013).Fistularia commersonii in the Mediterranean Sea: invasion history and distribution modeling based on presence-only records.Biological Invasions , 15, pp.977–990. DOI: 10.1007/s10530-012-0344-4
Ball-Damerow, J.E., Brenskelle, L., Barve, N., Soltis, P. S., Sierwald, P., Bieler, R., LaFrance, R., Ariño, A. H. and Guralnick, R. P. (2019). Research applications of primary biodiversity databases in the digital age. PLOS ONE , 14 (9). DOI: 10.1371/journal.pone.0215794
Barilani, M., Sfougaris, A., Giannakopoulos, A., Mucci, N., Tabarroni, C. and Randi, E. (2007). Detecting introgressive hybridisation in rock partridge populations (Alectoris graeca ) in Greece through Bayesian admixture analyses of multilocus genotypes. Conservation Genetics , 8, pp.343–354. DOI: 10.1007/s10592-006-9174-1
Barricelli, N. A. (1954). ”Esempi numerici di processi di evoluzione”.Methodos , 6, pp.45–68.
Barricelli, N. A. (1962). Numerical testing of evolution theories.Acta Biotheoretica, 16, pp.69–98. DOI: 10.1007/BF01556771
Barros, L. DA. and Elkin, C. (2021). An index for tracking old-growth value in disturbance-prone forest landscapes. Ecological Indicators , 121. DOI: 10.1016/j.ecolind.2020.107175
Bayliss, P., van Dam, R. A. and Bartolo, R. E. (2012). Quantitative ecological risk assessment of the Magela Creek floodplain in Kakadu National Park, Australia: comparing point source risks from the ranger uranium mine to diffuse landscape-scale risks. Human and Ecological Risk Assessment: An International Journal , 18(1), pp.115–151. DOI: 10.1080/10807039.2012.632290
Bondé, L., Assis, J. C., Benavides-Gordillo, S., Canales-Gomez, E., Fajardo, J., Marrón-Becerra, A., Noguera-Urbano, E. A., Weidlich, E. W. A. and Ament, J. M. (2020). Scenario-modelling for the sustainable management of non-timber forest products in tropical ecosystems.Biota Neotropica , 20. DOI: 10.1590/1676-0611-BN-2019-0898
Boser, B. E., Guyon, I. M. and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory , pp. 144–152. Association for Computing Machinery. ISBN: 089791497X. DOI: 10.1145/130385.130401
Bradley, B. A. (2010). Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA.Ecography , 33, pp.198–208. DOI: 10.1111/j.1600-0587.2009.05684.x
Breiman, L. (2001). Random forests. Machine Learning , 45, pp.5–32. DOI: 10.1023/A:1010933404324
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984).Classification and regression trees . 1st ed. Chapman & Hall. ISBN: 9781315139470. DOI: 10.1201/9781315139470
Bui, D. T., Tsangaratos, P., Nguyen, V., Liem, N. V. and Trinh, P. T. (2020). Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA , 188. DOI: 10.1016/j.catena.2019.104426
Canessa, S., Taylor, G., Clarke, R. H., Ingwersen, D., Vandersteen, J. and Ewen, J. G. (2020). Risk aversion and uncertainty create a conundrum for planning recovery of a critically endangered species.Conservation Science and Practice , 2(2). DOI: 10.1111/csp2.138
Capinha, C., Ceia-Hasse, A., Kramer, A. M. and Meijer, C. (2021). Deep learning for supervised classification of temporal data in ecology.Ecological Informatics , 61. DOI: 10.1016/j.ecoinf.2021.101252
Cardoso, P., Branco, V. V., Borges, P. A. V., Carvalho, J. C., Rigal, F., Gabriel, R., Mammola, S., Cascalho, J. and Correia, L. (2020). Automated discovery of relationships, models, and principles in ecology.Frontiers in Ecology and Evolution , 8. DOI: 10.3389/fevo.2020.530135
Carneiro Freire, S. M., Macmanus, K., Pesaresi, M., Doxsey-Whitfield, E. and Mills, J. (2016). Development of new open and free multi-temporal global population grids at 250 m resolution. In: Geospatial data in a changing world; Association of Geographic Information Laboratories in Europe (AGILE) . AGILE. ISBN: 978-90-816960-6-7
Catford, J. A., Vesk, P. A., White, M. D. and Wintle, B. A. (2011). Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics. Diversity and Distributions , 17(6), pp.1099–1110. DOI: 10.1111/j.1472-4642.2011.00794.x
Cazalis, V., Di Marco, M., Butchart, S. H. M., Akçakaya, H. R., González-Suárez, M., Meyer, C., Clausnitzer, V., Böhm, M., Zizka, A., Cardoso, P., Schipper, A. M., Bachman, S. P., Young, B. E., Hoffmann, M., Benítez-López, A., Lucas, P. M., Pettorelli, N., Patoine, G., Pacifici, M., Jörger-Hickfang, T., Brooks, T. M., Rondinini, C., Hill, S. L. L., Visconti, P. and Santini, L. (2022). Bridging the research-implementation gap in IUCN Red List assessments. Trends in Ecology & Evolution , 37(4), pp.359–370. DOI: 10.1016/j.tree.2021.12.002
CBD (2021). Post-2020 Global Biodiversity Framework: Discussion Paper. Convention on Biological Diversity. [online] Available at: <https://www.cbd.int/conferences/post2020/post2020-prep-01/documents> [Accessed 16 November 2021].
Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. arXiv . DOI: 10.48550/arXiv.1603.02754
Christie, K. S., Jensen, W. F., Schmidt, J. H. and Boyce, M. S. (2015). Long-term changes in pronghorn abundance index linked to climate and oil development in North Dakota. Biological Conservation , 192, pp.445–453. DOI: 10.1016/j.biocon.2015.11.007
Christin, S., Hervet, É. and Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution , 10(10), pp.1632–1644. DOI: 10.1111/2041-210X.13256
Corbane, C., Pesaresi, M., Kemper, T., Politis, P., Florczyk, A., Syrris, V., Melchiorri, M., Sabo, F. and Soille, P. (2019). Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data , 3(2), pp.140–169. DOI: 10.1080/20964471.2019.1625528
Coste, M., Boutry, S., Tison-Rosebery, J. and Delmas, F. (2009). Improvements of the Biological Diatom Index (BDI): Description and efficiency of the new version (BDI-2006). Ecological Indicators , 9(4), pp.621–650. DOI: 10.1016/j.ecolind.2008.06.003
Coutts, S. R., van Klinken, R. D., Yokomizo, H. and Buckley, Y. M. (2011). What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we use this knowledge to aid management?. Biological Invasions , 13, pp.1649–1661. DOI: 10.1007/s10530-010-9922-5
Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J. and Lawler, J. J. (2007). Random forests for classification in ecology. Ecology , 88(11), pp.2783–2792. DOI: 10.1890/07-0539.1
Davy, C. M., Squires, K. and Zimmerling, J. R. (2021). Estimation of spatiotemporal trends in bat abundance from mortality data collected at wind turbines. Conservation Biology , 35(1), pp.227–238. DOI: 10.1111/cobi.13554
Debeljak, M., Ficko, A. and Brus, R. (2015). The use of habitat and dispersal models in protecting European black poplar (Populus nigra L.) from genetic introgression in Slovenia. Biological Conservation , 184, pp.310–319. DOI: 10.1016/j.biocon.2015.02.004
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., G. Lohmann, L., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S. and Zimmermann, N. E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography , 29(2), pp.129–151. DOI: 10.1111/j.2006.0906-7590.04596.x
Elith, J., Leathwick, J. R. and Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology , 77(4), pp.802–813. DOI: 10.1111/j.1365-2656.2008.01390.x
Fairbrass, A. J., Firman, M., Williams, C., Brostow, G. J., Titheridge, H. and Jones, K. E. (2018). CityNet—Deep learning tools for urban ecoacoustic assessment. Methods in Ecology and Evolution , 10(2), pp.186–197. DOI: 10.1111/2041-210X.13114
Feng, X., Enquist, B. J., Park, D. S., Boyle, B., Breshears, D. D., Gallagher, R. V., Lien, A., Newman, E. A., Burger, J. R., Maitner, B. S., Merow, C., Li, Y., Huynh, K. M., Ernst, K., Baldwin, E., Foden, W., Hannah, L., Jørgensen, P., Kraft, N. J. B., Lovett, J. C., Marquet, P. A., McGill, B. J., Morueta‐Holme, N., Neves, D. M., Núñez‐Regueiro, M. M., Oliveira‐Filho, A. T., Peet, R. K., Pillet, M., Roehrdanz, P. R., Sandel, B., Serra‐Diaz, J. M., Šímová, I., Svenning, J., Violle, C., Weitemier, T. D., Wiser, S., López‐Hoffman, L. and Hurlbert, A. (2022). A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base. Global Ecology and Biogeography . DOI: 10.1111/geb.13497
Fick, S. E. and Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology , 37(12), pp.4302–4315. DOI: 10.1002/joc.5086
Floreano, D. and Mattiussi, C. (2008). Bio-inspired artificial intelligence: theories, methods, and technologies . MIT press. ISBN: 9780262062718
Freund, Y. and Schapire, R. (1999). A short introduction to boosting.Journal of Japanese Society for Artificial Intelligence , 14(5), pp.771–780.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics , 29(5), pp.1189–1232. DOI: 10.1214/aos/1013203451
Gallardo, B., Errea, M. P. and Aldridge, D. C. (2012). Application of bioclimatic models coupled with network analysis for risk assessment of the killer shrimp, Dikerogammarus villosus , in Great Britain.Biological Invasions , 14, pp.1265–1278. DOI: 10.1007/s10530-011-0154-0
GBIF (2022). Global Biodiversity Information Facility . [online] Available at: <https://www.gbif.org/> [Accessed 20 April 2022].
Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep learning . MIT press. ISBN: 978-0262035613
Ho, T. K. (1995). Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition , pp. 278–282. DOI: 10.1109/ICDAR.1995.598994
Hone, J., Duncan, R. P. and Forsyth, D. M. (2010). Estimates of maximum annual population growth rates (rm) of mammals and their application in wildlife management. Journal of Applied Ecology , 47(3), pp.507–514. DOI: 10.1111/j.1365-2664.2010.01812.x
Hossain, M. and Patra, P. K. (2020). Water pollution index - A new integrated approach to rank water quality. Ecological Indicators , 117(106668). DOI: 10.1016/j.ecolind.2020.106668
Hu, C. and Albertani, R. (2019). Machine learning applied to wind turbine blades impact detection. Wind Engineering , 44(3), pp.325–338. DOI: 10.1177/0309524X19849859
IBM (2022a). What are Neural Networks? . [online] Ibm.com. Available at: <https://www.ibm.com/cloud/learn/neural-networks> [Accessed 21 April 2022].
IBM (2022b). What is Natural Language Processing? . [online] Ibm.com. Available at: <https://www.ibm.com/cloud/learn/natural-language-processing> [Accessed 21 April 2022].
iNaturalist (2022). iNaturalist . [online] Available at: <https://www.inaturalist.org/> [Accessed 20 April 2022].
Jaynes, E. T. (1957). Information Theory and Statistical Mechanics.Physical Review , 106(4), pp.620–630. DOI: 10.1103/PhysRev.106.620
Jia, W., Dong, Z., Duan, C., Ni, X. and Zhu, Z.. (2019). Ecological reservoir operation based on DFM and improved PA-DDS algorithm: A case study in Jinsha river, China. Human and Ecological Risk Assessment: An International Journal , 26(7), pp.1723–1741. DOI: 10.1080/10807039.2019.1603075
Johnstone, J. F., Hollingsworth, T. N., Chapin III, F. S. and Mack, M. C. (2010). Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Global Change Biology , 16(4), pp.1281–1295. DOI: 10.1111/j.1365-2486.2009.02051.x
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P. and Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data , 4. DOI: 10.1038/sdata.2017.122
Khalighifar, A., Brown, R. M., Vallejos, J. G. and Peterson, A. T. (2021). Deep learning improves acoustic biodiversity monitoring and new candidate forest frog species identification (genus Platymantis ) in the Philippines. Biodiversity and Conservation , 30, pp.643–657. DOI: 10.1007/s10531-020-02107-1
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Bradford Books. ISBN: 9780262527910
Křivánek, M. and Pyšek, P. (2006). Predicting invasions by woody species in a temperate zone: a test of three risk assessment schemes in the Czech Republic (Central Europe). Diversity and Distributions , 12(3), pp.319–327. DOI: 10.1111/j.1366-9516.2006.00249.x
Landuyt, D., Broekx, S., D’hondt, R., Engelen, G., Aertsens, J. and Goethals, P. L. M. (2013). A review of Bayesian belief networks in ecosystem service modelling. Environmental Modelling & Software , 46, pp.1–11. DOI: 10.1016/j.envsoft.2013.03.011
Li, B., Wan, R., Yang, G., Wang, S. and Wagner, P. D. (2020). Exploring the spatiotemporal water quality variations and their influencing factors in a large floodplain lake in China. Ecological Indicators, 115. DOI: 10.1016/j.ecolind.2020.106454
Liu, Z., Peng, C., Work, T., Candau, J., DesRochers, A. and Kneeshaw, D. (2018). Application of machine-learning methods in forest ecology: recent progress and future challenges. Environmental Reviews , 26(4). DOI: 10.1139/er-2018-0034
Łopucki, R. and Kiersztyn, A. (2020). The city changes the daily activity of urban adapters: Camera-traps study of Apodemus agrarius behaviour and new approaches to data analysis.Ecological Indicators , 110. DOI: 10.1016/j.ecolind.2019.105957
Lu, Y., Liu, H., Chen, W., Yao, J., Huang, Y., Zhang, Y. and He, X. (2021). Conservation planning of the genus Rhododendron in Northeast China based on current and future suitable habitat distributions. Biodiversity and Conservation , 30, pp.673–697. DOI: 10.1007/s10531-020-02110-6
Manel, S., Berthier, P. and Luikart, G. (2002). Detecting wildlife poaching: identifying the origin of individuals with Bayesian assignment tests and multilocus genotypes. Conservation Biology , 16(3), pp.650–659. DOI: 10.1046/j.1523-1739.2002.00576.x
Mateo-Tomás, P., Olea, P. P., Sánchez-Barbudo, I. S. and Mateo, R. (2012). Alleviating human-wildlife conflicts: identifying the causes and mapping the risk of illegal poisoning of wild fauna. Journal of Applied Ecology , 49(2), pp.376–385. DOI: 10.1111/j.1365-2664.2012.02119.x
McCarthy, M. A. and Masters, P. (2005). Profiting from prior information in Bayesian analyses of ecological data. Journal of Applied Ecology , 42(6), pp.1012–1019.
McCulloch, W.S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, pp.115–133. DOI: 10.1007/BF02478259
Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. and Schipper, A. M. (2018). Global patterns of current and future road infrastructure.Environmental Research Letters , 13(6), p.064006. DOI: 10.1088/1748-9326/aabd42
Mitchell, T. M. (1997). Machine learning . 1st ed. McGraw-Hill Education. ISBN: 978-0071154673
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018). Foundations of machine learning . MIT press. ISBN: 9780262018258
Molnár, V. E., Simon, E., Tóthmérész, B., Ninsawat, S. and Szabó, S. (2020). Air pollution induced vegetation stress - The Air Pollution Tolerance Index as a quick tool for city health evaluation.Ecological Indicators , 113. DOI: 10.1016/j.ecolind.2020.106234
Montowska, M. and Pospiech, E. (2010). Authenticity determination of meat and meat products on the protein and DNA basis. Food Reviews International , 27(1), pp.84–100. DOI: 10.1080/87559129.2010.518297
Mosavi, A., Ozturk, P. and Chau, K. (2018). Flood prediction using machine learning models: literature review. Water , 10(11). DOI: 10.3390/w10111536
Murphy, K. P. (2012). Machine learning: a probabilistic perspective . MIT press. ISBN: 9780262018029
Namkhan, M., Gale, G. A., Savini, T. and Tantipisanuh, N., (2020). Loss and vulnerability of lowland forests in mainland Southeast Asia.Conservation Biology , 35(1), pp.206–215. DOI: 10.1111/cobi.13538
Naudiyal, N., Wang, J., Ning, W., Gaire, N. P., Peili, S., Yanqiang, W., Jiali, H. and Ning, S. (2021). Potential distribution of Abies ,Picea , and Juniperus species in the sub-alpine forest of Minjiang headwater region under current and future climate scenarios and its implications on ecosystem services supply. Ecological Indicators , 121, p.107131. DOI: 10.1016/j.ecolind.2020.107131
Niell, S., Jesús, F., Díaz, R., Mendoza, Y., Notte, G., Santos, E., Gérez, N., Cesio, V., Cancela, H. and Heinzen, H. (2018). Beehives biomonitor pesticides in agroecosystems: Simple chemical and biological indicators evaluation using Support Vector Machines (SVM).Ecological Indicators , 91, pp.149–154. DOI: 10.1016/j.ecolind.2018.03.028
Oliveira, R., Godinho, R., Randi, E., Ferrand, N. and Alves, P. C. (2008). Molecular analysis of hybridisation between wild and domestic cats (Felis silvestris ) in Portugal: implications for conservation. Conservation Genetics , 9, pp.1–11. DOI: 10.1007/s10592-007-9297-z
Oliver, S. K., Collins, S. M., Soranno, P. A., Wagner, T., Stanley, E. H., Jones, J. R., Stow, C. A. and Lottig, N. R. (2017). Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990. Global Change Biology , 23(12), pp.5455–5467. DOI: 10.1111/gcb.13810
Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential reasoning. In: Proceedings of the 7th conference of the Cognitive Science Society , pp.15–17. University of California.
Peciña, M. V., Bergamo, T. F., Ward, R. D., Joyce, C. B. and Sepp, K. (2021). A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows. Ecological Indicators , 122. DOI: 10.1016/j.ecolind.2020.107227
Phillips, S. J., Anderson, R. P. and Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling , 190(3-4), pp.231–259. DOI: 10.1016/j.ecolmodel.2005.03.026
Pichler, M. and Hartif, F. (2022). Machine Learning and Deep Learning – A review for Ecologists. arXiv . DOI: 10.48550/arXiv.2204.05023
Primack, R. B., Ibáñez, I., Higuchi, H., Lee, S. D., Miller-Rushing, A. J., Wilson, A. M. and Silander Jr., J. A. (2009). Spatial and interspecific variability in phenological responses to warming temperatures. Biological Conservation , 142(11), pp.2569–2577. DOI: 10.1016/j.biocon.2009.06.003
Pyšek, P., Jarošík, V., Hulme, P. E., Pergl, J., Hejda, M., Schaffner, U. and Vilà, M. (2012). A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment. Global Change Biology , 18(5), pp.1725–1737. DOI: 10.1111/j.1365-2486.2011.02636.x
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning , 1, pp.81–106. DOI: 10.1007/BF00116251
Redmon J. and Farhadi, A. (2018). YOLOv3: An incremental improvement.arXiv . DOI: 10.48550/arXiv.1804.02767
Russell, S. and Norvig, P. (2020). Artificial intelligence: a modern approach . 4th ed. Pearson. ISBN: 978-0134610993
Sabatini, F. M., Burrascano, S., Keeton, W. S., Levers, C., Lindner, M., Pötzschner, F., Verkerk, P. S., Bauhus, J., Buchwald, E., Chaskovsky, O., Debaive, N., Horváth, F., Garbarino, M., Grigoriadis, N., Lombardi, F., Duarte, I. M., Meyer, P., Midteng, R., Mikac, S., Mikoláš, M., Motta, R., Mozgeris, G., Nunes, L., Panayotov, M., Ódor, P., Ruete, A., Simovski, B., Stillhard, J., Svoboda, M., Szwagrzyk, J., Tikkanen, O., Volosyanchuk, R., Vrska, T., Zlatanov, T. and Kuemmerle, T. (2018). Where are Europe’s last primary forests?. Diversity and Distributions , 24(10), pp.1426–1439. DOI: 10.1111/ddi.12778
Shokri, S., Jafari, A., Rabei, K., Hadipour, E., Alinejad, H., Zeppenfeld, T., Soufi, M., Qashqaei, A., Ahmadpour, M., Zehzad, B., Kiabi, B. H., Pavey, C. R., Balkenhol, N., Waltert, M. and Soofi, M. (2020). Conserving populations at the edge of their geographic range: the endangered Caspian red deer (Cervus elaphus maral ) across protected areas of Iran. Biodiversity and Conservation , 30(1), pp.85–105. DOI: 10.1007/s10531-020-02077-4
Smith, C. S., Howes, A. L., Price, B. and McAlpine, C. A. (2007). Using a Bayesian belief network to predict suitable habitat of an endangered mammal – The Julia Creek dunnart (Sminthopsis douglasi ).Biological Conservation , 139(3-4), pp.333–347. DOI: 10.1016/j.biocon.2007.06.025
Spyromitros-Xioufis, E., Moumtzidou, A., Papadopoulos, S., Vrochidis, S., Kompatsiaris, Y., Georgoulias, A. K., Alexandri, G. and Kourtidis, K. (2018). Towards improved air quality monitoring using publicly available sky images. In: Multimedia tools and applications for environmental & biodiversity informatics . ISBN: 978-3-319-76445-0. DOI: 10.1007/978-3-319-76445-0_5
Stevens, T. K., Hale, A. M., Karsten, K. B. and Bennett, V. J. (2013). An analysis of displacement from wind turbines in a wintering grassland bird community. Biodiversity and Conservation , 22(8), pp.1755-1767. DOI: 10.1007/s10531-013-0510-8
Stockwell, D. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science , 13(2), pp.143–158. DOI: 10.1080/136588199241391
Stupariu, M., Cushman, S.A., Pleşoianu, A., Pătru-Stupariu, I. and Fürst, C.(2022). Machine learning in landscape ecological analysis: a review of recent approaches. Landscape Ecology , 37, pp.1227–1250. DOI: 10.1007/s10980-021-01366-9
Thessen, A. E. (2016). Adoption of machine learning techniques in ecology and earth science. One Ecosystem, 1(e8621). DOI: 10.3897/oneeco.1.e8621
Thessen, A. E., Cui, H. and Mozzherin, D. (2012). Applications of natural language processing in biodiversity science. Advances in Bioinformatics , pp.1–17. DOI: 10.1155/2012/391574
TryDatabase (2022). TRY Plant Trait Database . [online] Available at: <https://www.try-db.org/TryWeb/Home.php> [Accessed 20 April 2022].
Tucker, C. L., Bell, J., Pendall, E. and Ogle, K. (2012). Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming?. Global Change Biology , 19(1), pp.252–263. DOI: 10.1111/gcb.12036
Valan, M., Makonyi, K., Maki, A., Vondráček, D. and Ronquist, F. (2019). Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks.Systematic Biology , 68(6), pp.876–895. DOI: 10.1093/sysbio/syz014
Van der Biest, K., D’Hondt, R., Jacobs, S., Landuyt, D., Staes, J., Goethals, P. and Meire, P. (2014). EBI: An index for delivery of ecosystem service bundles. Ecological Indicators , 37, pp.252–265. DOI: 10.1016/j.ecolind.2013.04.006
Vega, G. C., Pertierra, L. R. and Olalla-Tárraga, M. A. (2017). MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Scientific Data , 4. DOI: 10.1038/sdata.2017.78
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M. and Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications , 11. DOI: 10.1038/s41467-019-14108-y
Wang, L., Zhou, D., Wang, Y. and Zha, D. (2015). An empirical study of the environmental Kuznets curve for environmental quality in Gansu province. Ecological Indicators , 56, 96–105. DOI: 10.1016/j.ecolind.2015.03.023
Wang, R. and Wang, Y. (2006). Invasion dynamics and potential spread of the invasive alien plant species Ageratina adenophora(Asteraceae) in China. Diversity and Distributions , 12(4), pp.397–408. DOI: 10.1111/j.1366-9516.2006.00250.x
Weinstein, B. G. (2017). A computer vision for animal ecology.Journal of Animal Ecology , 87(3), pp.533–545. DOI: 10.1111/1365-2656.12780
Witten, I. H., Frank, E., Hall, M. A. and Pal, C. (2016). Data mining: practical machine learning tools and techniques . 4th ed. Morgan Kaufmann. ISBN: 978-0128042915
World Spider Trait Database (2022). World Spider Trait database . [online] Available at: <https://spidertraits.sci.muni.cz/> [Accessed 20 April 2022].
Yi, J. and Khot, R. A. (2020). ROOD: Unpacking the design and the making of a roadkill alert system. In: Proceedings of the fourteenth international conference on tangible, embedded, and embodied interaction , pp. 715–728. ISBN: 9781450361071. DOI: 10.1145/3374920.3375008
Young, A. M., Higuera, P. E., Duffy, P. A. and Hu, F. S. (2017). Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. Ecography , 40(5), pp.606–617. DOI: 10.1111/ecog.02205
Zhang, H., Yin, A., Yang, X., Fan, M., Shao, S., Wu, J., Wu, P., Zhang, M. and Gao, C., 2021. Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils. Ecological Indicators , 122. DOI: 10.1016/j.ecolind.2020.107233
Zhang, J., Jiang, F., Li, G., Qin, W., Wu, T., Xu, F., Hou, Y., Song, P., Cai, Z. and Zhang, T. (2021). The four antelope species on the Qinghai-Tibet plateau face habitat loss and redistribution to higher latitudes under climate change. Ecological Indicators , 123, p.107337. DOI: 10.1016/j.ecolind.2021.107337
Zhang, X. and Vincent, A. C. J. (2019). Using cumulative‐human‐impact models to reveal global threat patterns for seahorses.Conservation Biology , 33(6). DOI: 10.1111/cobi.13325
Zheng, Z., Gao, Y., Yang, Q., Zou, B., Xu, Y., Chen, Y., Yang, S., Wang, Y. and Wang, Z. (2020). Predicting forest fire risk based on mining rules with ant-miner algorithm in cloud-rich areas. Ecological Indicators , 118. DOI: 10.1016/j.ecolind.2020.106772