Andrewartha, H. G. & Birch., L. C. The distribution and abundance of animals. Sci. (New York N Y). 121, 389–390. https://doi.org/10.1126/science.121.3142.389.b (1955).
Boyce, M. S. et al. Can habitat selection predict abundance? J. Anim. Ecol. 85. REVIEW, 11–20. https://doi.org/10.1111/1365-2656.12359 (2016).
Wei, J., Niu, M., Zhang, H., Cai, B. & Ji, W. Global potential distribution of invasive species Pseudococcus viburni (Hemiptera: Pseudococcidae) under climate change. Insects 15, 195. https://doi.org/10.3390/insects15030195 (2024).
Kafash, A., Ashrafi, S. & Yousefi, M. Modeling habitat suitability of bats to identify high priority areas for field monitoring and conservation. Environ. Sci. Pollut. Res. Int. 29, 25881–25891. https://doi.org/10.1007/s11356-021-17412-7 (2022).
Bosso, L. et al. Integrating citizen science and Spatial ecology to inform management and conservation of the Italian seahorses. Ecol. Inf. 79, 102402. https://doi.org/10.1016/j.ecoinf.2023.102402 (2024).
Rêgo, M., Del-Rio, G. & Brumfield, R. Subspecies-level distribution maps for birds of the Amazon basin and adjacent areas. J. Biogeogr. 51 https://doi.org/10.1111/jbi.14718 (2023).
Ahmed, A. S., Bekele, A., Kasso, M. & Atickem, A. Impact of climate change on the distribution and predicted habitat suitability of two fruit bats (Rousettus aegyptiacus and Epomophorus labiatus) in Ethiopia: implications for conservation. Ecol. Evol. 13, e10481. https://doi.org/10.1002/ece3.10481 (2023).
Guisan, A. & Thuiller, W. 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 (2005).
Austin, M. P. 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 (2002).
Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448–16460. https://doi.org/10.1038/s41598-021-96047-7 (2021).
Jiang, T., Gradus, J. L. & Rosellini, A. J. Supervised machine learning: A brief primer. Behav. Ther. 51, 675–687. https://doi.org/10.1016/j.beth.2020.05.002 (2020).
Mi, C., Huettmann, F., Guo, Y., Han, X. & Wen, L. Why choose random forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ 5, e2849. https://doi.org/10.7717/peerj.2849 (2017).
Huettmann, F. et al. A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the great Gray Owl for Alaska. Sci. Rep. 14, 7213. https://doi.org/10.1038/s41598-024-57588-9 (2024).
Steiner, M., Huettmann, F., Bryans, N. & Barker, B. With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots. Sci. Rep. 14, 5204. https://doi.org/10.1038/s41598-024-55173-8 (2024).
Mechenich, M. F. & Žliobaitė, I. Eco-ISEA3H, a machine learning ready Spatial database for ecometric and species distribution modeling. Sci. Data. 10, 77. https://doi.org/10.1038/s41597-023-01966-x (2023).
Bonannella, C. et al. Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using Spatiotemporal machine learning. PeerJ 10, e13728. https://doi.org/10.7717/peerj.13728 (2022).
Aarts, B. G. W., Van Den Brink, F. W. B. & Nienhuis, P. H. Habitat loss as the main cause of the slow recovery of fish faunas of regulated large rivers in Europe: the transversal floodplain gradient. River Res. Appl. 20, 3–23. https://doi.org/10.1002/rra.720 (2004).
Wasehun, E. T., Beni, H., Di Vittorio, C. A. & L. & UAV and satellite remote sensing for inland water quality assessments: A literature review. Environ. Monit. Assess. 196, 277. https://doi.org/10.1007/s10661-024-12342-6 (2024).
Lee, Z., Churnside, J., Mao, Z., Wu, S. & Zibordi, G. Active and passive optical remote sensing of the aquatic environment: Introduction to the feature issue. Appl. Opt. 59, Aps1–aps2. https://doi.org/10.1364/ao.392549 (2020).
Elith, J. & Leathwick, J. R. 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 (2009).
Zhao, X. et al. Abundance and conservation status of the Yangtze finless porpoise in the Yangtze river, China. Biol. Conserv. 141, 3006–3018. https://doi.org/10.1016/j.biocon.2008.09.005 (2008).
Huang, J. et al. Population survey showing hope for population recovery of the critically endangered Yangtze finless porpoise. Biol. Conserv. 241, 108315. https://doi.org/10.1016/j.biocon.2019.108315 (2020).
Chen, M. et al. Parentage-Based group composition and dispersal pattern studies of the Yangtze finless porpoise population in Poyang lake. Int. J. Mol. Sci. 17 https://doi.org/10.3390/ijms17081268 (2016).
Jiang-Long, Q. et al. Population and distribution characteristics of Yangtze finless porpoise in JiangxiI waters during the dry season. Acta Hydrobiol. Sin. 47, 1701–1708. https://doi.org/10.7541/2023.2022.0503 (2023).
Liu, X. et al. Seasonal Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) movements in the Poyang lake, China: Implications on flexible management for aquatic animals in fluctuating freshwater ecosystems. Sci. Total Environ. 807, 150782. https://doi.org/10.1016/j.scitotenv.2021.150782 (2022).
Mei, Z. et al. Mitigating the effect of shipping on freshwater cetaceans: The case study of the Yangtze finless porpoise. Biol. Conserv. 257, 109132. https://doi.org/10.1016/j.biocon.2021.109132 (2021).
Huang, S. L. et al. Saving the Yangtze finless porpoise: Time is rapidly running out. Biol. Conserv. 210, 40–46. https://doi.org/10.1016/j.biocon.2016.05.021 (2017).
Rongcheng, R. et al. Population structure and growth status of Yangtze finless porpoise in the sand pits waters of Southern Songmen mountain, Poyang lake. J. Shanghai Ocean. Univ. 32, 1237–1244. https://doi.org/10.12024/jsou (2023).
Rongcheng, R. et al. Analysis of the population genetics of the Yangtze finless porpoise population in the Poyang lake basin: Insight of conservation recommendations. 34, e4145, (2024). https://doi.org/10.1002/aqc.4145
Leng, M. et al. Assessment of Water Eutrophication at Bao’an Lake in the Middle Reaches of the Yangtze River Based on Multiple Methods. Int. J. Environ. Res. Public. Health. 20 https://doi.org/10.3390/ijerph20054615 (2023).
Li, W. et al. A real-time passive acoustic monitoring system to detect Yangtze finless porpoise clicks in Ganjiang river, China. Front. Mar. Sci. 9 https://doi.org/10.3389/fmars.2022.883774 (2022).
Duan, P. X. et al. Anthropogenic activity, hydrological regime, and light level jointly influence Temporal patterns in biosonar activity of the Yangtze finless porpoise at the junction of the Yangtze river and Poyang lake, China. Zoological Res. 44, 919–931. https://doi.org/10.24272/j.issn.2095-8137.2022.504 (2023).
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).
Bosso, L. et al. The rise and fall of an Alien: why the successful colonizer Littorina saxatilis failed to invade the mediterranean sea. Biol. Invasions. 24, 3169–3187. https://doi.org/10.1007/s10530-022-02838-y (2022).
Hu, Z. M. et al. Intraspecific genetic variation matters when predicting seagrass distribution under climate change. Mol. Ecol. 30, 3840–3855. https://doi.org/10.1111/mec.15996 (2021).
Wang, Z. T., Duan, P. X., Akamatsu, T., Wang, K. X. & Wang, D. Temporal and Spatial biosonar activity of the recently established uppermost Yangtze finless porpoise population downstream of the Gezhouba dam: correlation with hydropower cascade development, shipping, hydrological regime, and light intensity. Ecol. Evol. 14, e11346. https://doi.org/10.1002/ece3.11346 (2024).
Li, Q. et al. Habitat configuration of the Yangtze finless porpoise in Poyang lake under a shifting hydrological regime. Sci. Total Environ. 838, 155954. https://doi.org/10.1016/j.scitotenv.2022.155954 (2022).
Mei, Z. et al. Habitat preference of the Yangtze finless porpoise in a minimally disturbed environment. Ecol. Model. 353, 47–53. https://doi.org/10.1016/j.ecolmodel.2016.12.020 (2017).
Manel, S., Williams, H. C. & Ormerod, S. J. Evaluating presence–absence models in ecology: The need to account for prevalence. J. Appl. Ecol. 38, 921–931. https://doi.org/10.1046/j.1365-2664.2001.00647.x (2001).
Zhang, X. et al. Effects of fish community on occurrences of Yangtze finless porpoise in confluence of the Yangtze and Wanhe rivers. Environ. Sci. Pollut. Res. 22, 9524–9533. https://doi.org/10.1007/s11356-015-4102-x (2015).
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x (2006).
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? 3, 327–338, (2012). https://doi.org/10.1111/j.2041-210X.2011.00172.x
Wei, Z. et al. Population size, behavior, movement pattern and protection of Yangtze finless porpoise at Balijiang section of the Yangtze river. Resour. Environ. Yangtze Basin. 11, 427–432 (2002).
Wang, Z. T. et al. Underwater noise pollution in China’s Yangtze river critically endangers Yangtze finless porpoises (Neophocaena asiaeorientalis asiaeorientalis). Environ. Pollut. 262, 114310. https://doi.org/10.1016/j.envpol.2020.114310 (2020).
Brough, T. E., Rayment, W. J., Slooten, L. & Dawson, S. Prey and habitat characteristics contribute to hotspots of distribution for an endangered coastal Dolphin. 10, (2023). https://doi.org/10.3389/fmars.2023.1204943
Wang, Z., Akamatsu, T., Wang, K. & Wang, D. The diel rhythms of biosonar behavior in the Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) in the Port of the Yangtze river: the correlation between prey availability and boat traffic. PloS One. 9, e97907. https://doi.org/10.1371/journal.pone.0097907 (2014).
Crossin, G. T. et al. Acoustic telemetry and fisheries management. Ecol. Appl. 27, 1031–1049. https://doi.org/10.1002/eap.1533 (2017).
Minamoto, T. & Environmental DNA analysis for macro-organisms: Species distribution and more. DNA Res. 29 https://doi.org/10.1093/dnares/dsac018 (2022).
