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