Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy |
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| Affiliation: | 1. Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, Iran;2. Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Ny Munkegade 116, Aarhus C, DK-8000, Denmark;3. National center of genetic resources, Agricultural Research Education and Extension Organization, Tehran, Iran;4. Department of Geography, The University of Burdwan, West Bengal, India;5. HUTECH University, 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Viet Nam;6. Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Ny Munkegade 116, Aarhus C, DK-8000, Denmark;7. Otago Regional Council, Dunedin 9010, New Zealand;8. Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Al. 29-Listopada 46, Krakow 31-425, Poland;1. Key Laboratory of Bio-Resources and Eco-Environment (Ministry of Education), Sichuan University, Chengdu 610064, China;2. College of Mathematics, Sichuan University, Chengdu 610044, China;3. School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK;4. Wolong National Nature Reserve Administration, Aba 623000, China;1. Department of Environmental Health Sciences and Technology, Jimma University, P.O. Box 378, Jimma, Ethiopia;2. Department of Biology, College of Natural and Computational Sciences, Dilla University, P.O. Box 419, Dilla, Ethiopia;3. Department of Medical Laboratory Sciences & Pathology, Jimma University, P.O. Box 378, Jimma, Ethiopia;4. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA;1. Faculty of Pure and Applied Sciences, Open University of Cyprus, Giannou Kranidioti 33, Latsia 2220, Cyprus;2. Terra Cypria, The Cyprus Conservation Foundation, Agiou Andreou 341, Limassol 3035, Cyprus |
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| Abstract: | Species distribution models (SDM's) are powerful tools used to describe species suitable habitats and spatial occurrences and many statistical methods and algorithms are available to model the spatial distribution of a target species. Here we explore a species distribution model framework combined with machine learning algorithms to describe the distribution of two freshwater zooplankton species Daphnia longispina (Cladocera) and Eucyclops serrulatus (Copepods) in a system of 283 shallow and ephemeral freshwater habitats in the Northern Italian Appennines. For each species, we model the habitat suitability by comparing one regression-based model, one generalized linear model (GLM) and two machine learning algorithms: random forest (RF) and artificial neural network (ANN) with one hidden layer. We used a total of 27 predictor variables. The modeling framework was used considering a scenario of future climate change in order to evaluate potential shifts in spatial distribution of the zooplankton species. For both species, the supervised machine learning algorthn (ANN) produced the highest mean values for all the performance metrics. For D. longispina and E. serrulatus, the two most important variables ranked by the shap analysis and global sensitivity and uncertainty analysis (GSUA) were temperature seasonality and precipitation of the warmest quarter. Both species, in a future climatic change scenario, are expected to shift their distribution mainly toward lower northern altitudes with an overall expansion of 7% with respect to the past/present climatic conditions. However, the spatial expansion of D. longispina and E. serrulatus was qualitatively different. In agricultural and natural areas, the expansion of E. serrulatus was greater than that of D. longispina but, in natural areas, the expansion of E. serrulatus was counterbalanced by a greater spatial contraction than that of D. longispina. As hypothesized, direct and indirect anthropogenic pressures may affect the predicted potential shift and expansion of the zooplankton species. |
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