The evolution of spatial variability during an algal bloom event in shallow lakes

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.


Download Preprint


David Adrian Ortiz , Grace M Wilkinson 


1. Lakes are both temporally and spatially heterogeneous, but little is known about how algae blooms develop across surfaces over time and what drivers are influencing those patterns over the span of the growing season. Does the common practice of single location monitoring capture the full range of a heterogeneous ecosystem? If not, is there a feasible optimal number of sampling locations to represent a lake?

2. We sampled a small eutrophic lake on a 65m grid, 98 locations, for several parameters (chlorophyll a, phycocyanin, dissolved oxygen, pH, and temperature) weekly over 122 days. We compared the spatial information collected against a high frequency sensor at the deep point of the lake that collected the same parameters. We compared mean lake estimates between high frequency and spatial collections, observed spatial autocorrelation and variability change over time, and identified optimal number of sample locations with rare faction analysis.

3. Spatial sampling was able to capture the complete range of values, providing insight to how the lake was functioning, during non-bloom and bloom conditions. Spatial autocorrelation rose for all variables during bloom conditions as variability dropped throughout the lake. Temperature spatial autocorrelation was found to correlate with dissolved oxygen and pH spatial autocorrelation, mainly during non-bloom periods. During non-bloom conditions high frequency monitoring can provide accurate estimates of lake mean for all variables, expect for temperature. In our 40 ha study lake we found an optimal number of sample locations range from 20-40, but can be as many as 60 during blooms.

4. Our unique combination of spatial and temporal monitoring highlights the range of values that lakes can undergo, how a single sampling location is often not a good representation of the entire waterbody, and how important it is to survey both if possible. By including temperature in our spatial analysis, we able to isolate biological (algal bloom and macrophytes) and physical drivers (wind and temperature) of spatial heterogeneity.

5. Several monitoring locations is especially important if making management decisions, as one sample point may lead to a biased and incorrect conclusion of what the lake is experiencing. Adding spatial monitoring to a monitoring program can help identify areas of concern and can a new focal point for management efforts or additional sampling (e.g. for microcystin or anoxia).



Terrestrial and Aquatic Ecology


spatial heterogeneity, spatial analysis, rarefaction analysis, macrophytes, eutrophic, rare faction analysis, euthropic


Published: 2021-02-23 04:16


CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.