Survey Analysis for Monitoring Population Levels (SAMPL): An agent-based model to simulate surveys of stationary organisms
Survey Analysis for Monitoring Population Levels (SAMPL) is a spatially-explicit Agent-Based Model (ABM) designed to evaluate the performance of various spatial sampling strategies for estimating population density for biological species. SAMPL is designed for field scientists and other interested parties who wish to understand the strengths and limitations of various sampling strategies. It allows the user to configure the true density, detectability, and distribution of the target organism(s), as well as the sampling strategy and the intensity of the sampling effort. It then quantifies the estimated density (if applicable), species detection rate, and other information about the sampling effort, which can be compared to the true density and number of organisms of interest. SAMPL was originally developed to simulate freshwater mussel surveys and the organisms of interest are referred to as 'mussels' in the model. However, the sampling strategies replicated in this model could be used to survey other organisms or even inanimate objects as well.
SAMPL allows the user to replicate four sampling strategies:
- simple random sampling: random quadrats are sampled.
- transect sampling: transects are laid vertically across the area of interest, and quadrats are sampled randomly along the transects.
- adaptive cluster sampling: quadrats are randomly sampled. If an organism is detected, the four direct neighboring quadrats are also sampled. This repeats until no more organisms are detected or the sampled 'cluster' reaches the edge of the model grid.
- timed searches: a simulated surveyor 'searches' the area of interest by traveling in a correlated random walk. When the surveyor encounters a target organism, it turns more tightly, thus simulating human search behavior.
For more information consult the model documentation, located in the Info tab of the NetLogo model.
SAMPL was designed to run on NetLogo 6.4.0 or later. To install NetLogo 6.4.0, go to https://ccl.northwestern.edu/netlogo/download.shtml and follow the download instructions.
To run SAMPL, open the NetLogo Interface tab and use the green dropdown lists and input boxes to configure the model inputs. Example inputs include sampling method, density of mussels, distribution of mussels, frequency and detectability of mussel species, the output file name, and parameters related to the specific sampling method. When the model is set up correctly, click the grey Initialize button, then click the Run Model button to run the simulation. To view metrics click the Calculate Metrics button. To save results as a CSV, click Initialize File once, followed by Save Results after each model run. The results CSV will be found in the Results folder.
For more detailed instructions consult the model documentation, located in the Info tab of the NetLogo model.
To run multiple model scenarios or repeating model scenarios with random variation, make use of the behavior space tool in the tools drop down list. For more information on the behavior space tool consult the NetLogo 6.4.0 User Manual https://ccl.northwestern.edu/netlogo/docs/behaviorspace.html.
SAMPL was designed to simulate freshwater mussel surveys in river and stream habitats and to estimate population densities based on those surveys. Specifically, the model was inspired by a study by Brittany Sanchez and Astrid Schwalb, published in 2021, which compares various freshwater mussel sampling strategies. The model was validated by expert reviewers who have had extensive experience surveying for freshwater mussels in the field.
SAMPL is a NetLogo model, and does not have built-in software tests. Instead, testing should include traditional line review, debugging, and a visual inspection of what is inputted into the model and what is shown in the model interface.
We welcome bug reports and questions in the form of GitHub issues.
We also welcome code contributions. The source code for this project is located in the NetLogo Code tab. To make a contribution, please fork this repository, commit your changes to the fork, and then create a pull request.
Sanchez, Brittney, and Astrid N. Schwalb. 2021. “Detectability Affects the Performance of Survey Methods: A Comparison of Sampling Methods of Freshwater Mussels in Central Texas.” Hydrobiologia 848 (12–13): 2919–29. https://doi.org/10.1007/s10750-019-04017-y.