Using distribution models to estimate blooms of phytosanitary cyanobacteria in Brazil

Abstract

The multiple uses of aquatic ecosystems by humankind and the continuous interference of their activities have contributed to the emergence of potentially toxic cyanobacteria blooms. Here, we firstly created a database of occurrences of cyanobacteria blooms in Brazil through a systematic review of the scientific literature available in online platforms (e.g. Web of Science, Capes Thesis Catalogue). Secondly, we carried out ecological niche models with occurrence data obtained from these studies to predict climatically suitable areas for blooms. We select 21 bioclimatic variables input environmental data. We used five modeling methods for the current climate scenario: (1) Maxent; (2) Support Vector Machines; (3) Random Forest; (4) Maximum Likelihood e (5) Gaussian. We found that the number of publications about bloom events was higher in 2009 with a decline in the years 2012, 2013 and 2017. Furthermore, the years with the higher records of blooms in freshwater environments were 2005, 2011 e 2014. These events occurring mainly in public supply reservoirs and are mostly of the genera Microcystis Lemmermann, 1907, Dolichospermum (Ralfs ex Bornet & Flahault) P.Wacklin, L.Hoffmann & J.Komárek, 2009 and Raphidiopsis F.E.Fritsch & F.Rich, 1929. Modeling the potential distribution of blooms, we found sampling gaps that should be targeting for future researches, especially in the Amazon biome. Overall, the models did not predict highly suitable areas in the /north of Brazil, while other regions were relatively well distributed with a higher number of occurrence records in the Southeast region.

Modeling the Distribution of the Chytrid Fungus Batrachochytrium dendrobatidis with Special Reference to Ukraine

“…Despite the apparent global invasion of Bd and a corresponding spate of past amphibian losses [ 84 ], there are many locations where this disease-causing pathogen has not yet been detected [ 102 ]. The use of broad environmental data to model the distribution of such a small fungal organism may cause some uncertainty depending on the geographical and study-variable scales; however, diverse research groups are using these tools to model pathogens’ distributions [ 22 , 65 , 103 ]. Accordingly, ecological niche modeling (ENM) is an effective way to evaluate how these environmental factors affect current species distributions [ 104 ].…”

“…SDMs help to identify such areas at risk that are suitable for the establishment of alien species, for instance by matching suitable climate conditions [ 21 ]. Identifying areas where a species is more likely to occur can also be used to guide sampling protocols and prioritizing areas of study [ 22 ]. Our objectives were to: Identify priority survey areas in Eastern Europe (with a special focus on Ukraine) where future outbreaks of Bd could occur (“hotspots”), but perhaps what was equally important was the recognition of locations that may be environmental refuges (“coldspots”) from infection (especially for amphibians that have certain sensitive conservation status) [ 23 ]; Identify bioclimatic and other environmental conditions that constrain the geographic distribution of this pathogen in the study area.…”

Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake

“…Apparent from the complexities described above, accurate prediction of Cyanobacterial blooms is urgently needed to improve the understanding of algal bloom dynamics and support proactive decision-making and risk mitigation, which requires significant complexity in modeling [5,[30][31][32][33][34][35]. Predicted information provides local water managers with tools for managing adverse effects posed by CyanoHABs [32].…”

“…Predicting water quality changes in freshwater bodies, particularly algal dynamics, is challenging due to the biochemical process's complexity and uncertainties [32,36]. Computational technology advancements have improved numerical algal bloom models, but calibration and validation of model parameters are challenging [9,37].…”