Date of Completion


Document Type

Honors College Thesis


Rubenstein Wildlife and Fisheries Biology

Thesis Type

Honors College

First Advisor

Jason Stockwell

Second Advisor

Ariana Chiapella


zooplankton, space-for-time, lakes, methodology, resample, gradient


Climate change is rapidly altering the magnitude and phenology of ecological processes and communities. Single observation “snapshots” from multi-lake surveys over a geographic range are typically used to evaluate plankton dynamics and their responses to spatial gradients in environmental forcing (e.g. temperature). These surveys, called Space-for-Time-substitution (SFTS) surveys, make the critical assumption that environmental gradients are reliable proxies for time and are therefore useful for overcoming the tradeoff between geographic representation and detailed temporal datasets. SFTS surveys, however, have been critiqued for their assumption that spatial and temporal scales can be coupled, and the ability of large-scale snapshots to represent time-integrated patterns in zooplankton community structure and function remains untested at global scales. We compiled existing global lake time series to test whether SFTS surveys can capture zooplankton biodiversity and its relationship with lake temperature. We broadly hypothesized that single “snapshot” surveys would be unable to capture zooplankton diversity within lakes and that SFTS surveys wouldn’t be able to reproduce zooplankton diversity as a function of temperature between multiple permutations. Therefore, SFTS surveys cannot be used to infer zooplankton community dynamics over time. Results suggest that single “snapshot” surveys are unlikely to represent mean zooplankton diversity at a given moment in time, that SFTS surveys cannot reliably reproduce relationships between lake temperature and zooplankton diversity, and that the relationship between lake temperature and zooplankton diversity varies at local temporal scales.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.