Effects of local density dependence and temperature on the spatial synchrony of marine fish populations

Disentangling empirically the many processes affecting spatial population synchrony is a challenge in population ecology. Two processes that could have major effects on the spatial synchrony of wild population dynamics are density dependence and variation in environmental conditions like temperature. Understanding these effects is crucial for predicting the effects of climate change on local and regional population dynamics.
We quantified the direct contribution of local temperature and density dependence to spatial synchrony in the population dynamics of nine fish species inhabiting the Barents Sea. First, we estimated the degree to which the annual spatial autocorrelations in density are influenced by temperature. Second, we estimated and mapped the local effects of temperature and strength of density dependence on annual changes in density. Finally, we measured the relative effects of temperature and density dependence on the spatial synchrony in changes in density.
Temperature influenced the annual spatial autocorrelation in density more in species with greater affinities to the benthos and to warmer waters. Temperature correlated positively with changes in density in the eastern Barents Sea for most species. Temperature had a weak synchronising effect on density dynamics, while increasing strength of density dependence consistently desynchronised the dynamics.
Quantifying the relative effects of different processes affecting population synchrony is important to better predict how population dynamics might change when environmental conditions change. Here, high degrees of spatial synchrony in the population dynamics remained unexplained by local temperature and density dependence, confirming the presence of additional synchronizing drivers, such as trophic interactions or harvesting.

Methods
Data on fish densities from annual scientific bottom trawl surveys conducted from 1985 to 2016, between January and March, by the Norwegian Institute for Marine Research and the Polar Research Institute of Marine Fisheries and Oceanography (Fall et al., 2020). The exact sampling locations vary between years, but the survey follows a spatially stratified sampling design with fairly consistent spatial coverage of the Barents Sea, with some exceptions caused by years of bad weather, extensive sea ice or limited access to Russian waters. Each sampling station was sampled with a Campelen 1800 demersal trawl with a mesh size of 22 mm on the cod-end, which was towed for 30 minutes, or 15 minutes after 2010, at a speed of 3 knots. The area covered by the trawls and the geometry of the trawl’s mouth were monitored during towing with Doppler logs or GPS and SCANMAR systems. The abundance of each species within the catch was sampled onboard following the protocols outlined in Mjanger et al., 2020. When the catch was excessively large a representative subsample of the catch was sampled, and the counts were then scaled up to become representative of the entire catch. More details on the survey sampling design can be found in (Fall et al., 2020; Jakobsen et al., 1997). Finally, the density of each species was estimated by dividing their overall catch within the trawl by the area trawled and standardizing to individuals per nautical mile. Sea temperature measurements were collected at each fish sampling site using a CTD-probe (Appendix S1 Fig. S1). Since the fish data were collected through bottom trawl surveys, we here used the average Sea Bottom Temperature (SBT; averaging the records within the 30 deepest meters).