Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures

Rapid and highly variable warming of lake surface waters around the globe

In this first worldwide synthesis of in situ and satellite-derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade-1) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors - from seasonally ice-covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade-1) to ice-free lakes experiencing increases in air temperature and solar radiation (0.53°C decade-1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes

Procesos físicos y ecológicos en el embalse de El Gergal (Sevilla): consecuencias sobre la calidad del agua

Tesis Univ. Granada. Departamento de Ecología. Leída el 14 de enero del 201

Ewens Ponds and South East wetlands data

This data set includes: <br>- Ewens Ponds monitoring data on nutrients, light, salinity, flow, temperature approximately every two months for one year period. <br>- South East Wetlands nutrient data base collated from previous research. <br>- Modelling input data and results for phytoplankton dynamic modelling under different flow and nutrient conditions.<br><br>The data set was generated within The Goyder Institute for Water Research Project No. E.2.7

The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa

Cyanobacteria are predicted to increase due to climate and land use change. However, the relative importance and interaction of warming temperatures and increased nutrient availability in determining cyanobacterial blooms are unknown. We investigated the contribution of these two factors in promoting phytoplankton and cyanobacterial biovolume in freshwater lakes. Specifically, we asked: (1) Which of these two drivers, temperature or nutrients, is a better predictor of cyanobacterial biovolume? (2) Do nutrients and temperature significantly interact to affect phytoplankton and cyanobacteria, and if so, is the interaction synergistic? and (3) Does the interaction between these factors explain more of the variance in cyanobacterial biovolume than each factor alone? We analyzed data from > 1000 U.S. lakes and demonstrate that in most cases, the interaction of temperature and nutrients was not synergistic; rather, nutrients predominantly controlled cyanobacterial biovolume. Interestingly, the relative importance of these two factors and their interaction was dependent on lake trophic state and cyanobacterial taxon. Nutrients played a larger role in oligotrophic lakes, while temperature was more important in mesotrophic lakes: Only eutrophic and hyper-eutrophic lakes exhibited a significant interaction between nutrients and temperature. Likewise, some taxa, such as Anabaena, were more sensitive to nutrients, while others, such as Microcystis, were more sensitive to temperature. We compared our results with an extensive literature review and found that they were generally supported by previous studies. As lakes become more eutrophic, cyanobacteria will be more sensitive to the interaction of nutrients and temperature, but ultimately nutrients are the more important predictor of cyanobacterial biovolume. © 2014, by the Association for the Sciences of Limnology and Oceanography, Inc.Anna Rigosi, Cayelan C. Carey, Bas W. Ibelings, and Justin D. Brooke

Top panels: the mean number of first choices made to each corner by the group of bumblebees (Exp. 1; groups means with SEM are shown) per session.

<p>Bottom panels: the mean number of times (choice frequency) the same bees (group means with SEM are shown) visited each corner per session.</p

The mean number of times (choice frequency) bumblebees visited each corner (group means with SEM are shown).

<p>Data for the two testing sessions were pooled because there was no significant interaction in the general ANOVA between distance from the feature and session (Exp. 2). Left panel: Near-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located at the target corner C. Right panel: Far-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located away from the target corner C.</p

(A) The mean number of first choices made to each corner by the group of bumblebees (Exp. 2; groups means with SEM are shown) per session.

<p>Top panels: Near-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located at the target corner C; Bottom panels: Far-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located away from the target corner C. (B) The mean number of times (choice frequency) bumblebees visited each corner (Exp. 2; groups means with SEM are shown) per session. Top panels: Near-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located at the target corner C; Bottom panels: Far-feature: the feature (white wall indicated by the horizontal grey stripe, all other walls are green) is located away from the target corner C.</p