Data From: Applying empirical dynamic modeling to distinguish abiotic and biotic drivers of population fluctuations in sympatric fishes
Cite this dataset
Wasserman, Ben; Rogers, Tanya; Munch, Stephan; Palkovacs, Eric (2022). Data From: Applying empirical dynamic modeling to distinguish abiotic and biotic drivers of population fluctuations in sympatric fishes [Dataset]. Dryad. https://doi.org/10.7291/D12T1W
Fluctuations in the population abundances of interacting species are widespread. Such fluctuations could be a response to abiotic factors, biotic interactions, or a combination of the two. Correctly identifying the drivers are critical for effective population management. However, such effects are not always static in nature. Nonlinear relationships between abiotic factors and biotic interactions make it difficult to parse true effects. We used a type of nonlinear forecasting, empirical dynamic modeling, to investigate the context-dependent species interaction between a common fish (threespine stickleback) and an endangered one (northern tidewater goby) in a fluctuating environment: a central California bar-built estuary. We found little evidence for competition, instead both species largely responded independently to abiotic conditions. Stickleback were negatively affected by sandbar breaching. The strongest predictor of tidewater goby abundance was stickleback abundance; however, this effect wasn’t a uniform negative effect of stickleback on goby as would be hypothesized under interspecific competition. The effect of stickleback on gobies was positive, though it was temporally restricted. Tidewater goby abundance in the summer was strongly positively correlated to stickleback abundance in the spring, which represents an offset in the reproductive and recruitment peaks in the two species that may help minimize competition and promote coexistence. Our study demonstrates how empirical dynamic modeling can be applied to understand drivers of population abundance in putative competitors and inform management for endangered species.
We surveyed fish in Younger Lagoon monthly from February 2014 through September 2020. Younger Lagoon is a 10 hectare bar built estuary, which is noteworthy in being unimpeded by habitat alteration such as channelization or anthropogenic breaching. Younger Lagoon experiences annual breaching cycles as described above. In addition, during the dry, warm summer conditions, the lagoon is often densely populated by a primary producer. In many years, that is the submerged vegetation Ruppia, but other years a phytoplankton bloom occurs. Anoxic conditions may occur in the late summer as the producer biomass begins to senesce and decay, especially overnight.
We placed 12 unbaited minnow traps (40.5 cm long, 22.9 cm diameter at the center, with 3 mm mesh, and openings with a diameter of 22 mm) along the eastern shore of the lagoon in the evening and retrieved them the next morning. Minnow traps were allowed to sink to the substrate. We did not place minnow traps in fixed locations. Instead, location was allowed to vary along the shoreline in order to prevent fish mortality since fluctuating water levels led to seasonal changes in habitat and anoxia risk. The front of the lagoon (the channel on the beach) was generally the deepest, the large central basin was less shallow, and the two upstream arms were the most shallow. When conditions warranted (warm temperatures and the potential for low oxygen) we varied the depth of water we set out traps in. As such we often moved them away from shore into deeper water. In the extreme, during hot summers and fall months, our traps in the central basin were placed along the thalweg (the deepest channel), and few if any traps were placed in the upstream arms because they were too shallow for the traps to even remain submerged. We counted the number of each species of fish encountered in each trap and report the average catch per unit effort for each survey.
Starting in September 2014, we measured the surface water temperature, salinity, and dissolved oxygen (percent saturation) using a YSI Pro2030 at a subset of the trap locations, usually every other trap. We used linear interpolation to fill in missing data due to equipment failure (1 salinity measurement and 2 dissolved oxygen measurements).
Rainfall data was provided by the University of California Natural Reserve System (https://ucnrs.dendra.science/). Data on estuary breaching was taken from an automated camera that photographed the lagoon mouth every 15 minutes during daylight hours. Photos were available for water years 2014-2020. We manually searched all photos available during the wet season to identify breaches. The lagoon does not breach during the dry season. Overnight breaches were detected by observing differences in mouth morphology from evening until morning photos. We augmented missing data with personal observations taken during the surveys and other visits to the lagoon.
Our focal variables were the mean number of stickleback and tidewater goby caught per trap. Potential environmental drivers included the total amount of precipitation that had fallen (rain), the total number of days the lagoon was documented as open (breach) since the last survey, and the mean of temperature, dissolved oxygen, and salinity weighted by the number of traps associated with each measurement.
University of California Natural Reserve System Mildred E. Mathias Grant
University of California Santa Cruz Hellman Fellowship
University of California Santa Cruz Cooperative Institute for Marine Ecosystems and Climate