DOI: 10.5061/dryad.g79cnp5w2
title: Effects of local density dependence and temperature on the spatial synchrony of marine fish populations

description: 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.
description: 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).
description: #########################################################################
 #DATA: GENERAL INFORMATION 1. Title of Dataset: Effects of local density
 dependence and temperature on the spatial synchrony of marine fish
 populations 2. Author Information A. Principal Investigator Contact
 Information Name: Jonatan F. Marquez Institution: 1Centre for Biodiversity
 Dynamics, Department of Biology, Norwegian University of Science and
 Technology, 7491 Trondheim, Norway Email: jonatan.fredricson@gmail.com 3.
 Data collected through the 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). More details on the survey
 sampling design can be found in (Fall et al., 2020; Jakobsen et al.,
 1997). 4. Geographic location of data collection: Barents Sea, Norway ***
 ## Resolutions\_list.rda *** R data file containing lists within lists.
 Contains data frames with data for each grid cell (grids in
 Resolutions\_hexpoly.rda described below). The relevant local information
 is: \| \- Resolutions\_list \(&lt;\-\- R list object\) \| \|\- 6400
 \(&lt;\-\- list with Hexagon resolutions 6400\, 8100\, 10000 and 12100
 km2\) \| \| \|\- "\-0\.1" \(&lt;\-\- list with spatial
 repositions of hexagonal gridlevel\. Also includes
 "\-0\.1"\,"\-0\.35"\,"\-0\.6"\,"\-0\.85"\,"\-1\.1"\,"\-1\.35"\,"\-1\.6"\,"\-1\.85"\,"\-2\.1"\,"\-2\.35"\,"\-2\.6"\,"\-2\.85" and "\-3\.1"\) \| \| \| \|\- Pout \(&lt;\-\- Species data level\) \| \| \| \|\- APlaice \| \| \| \|\- Lumpus \| \| \| \|\- Haddock \| \| \| \|\- B\_redfish \| \| \| \|\- Cod \| \| \| \|\- Capelin \| \| \| \|\- B\_whiting \| \| \| \|\- Herring \| \| \|\- "\-0\.35" \| \| \| \|\- Pout \| \| \| \|\.\.\. \| \| \| \|\- Herring \| \| \|\.\.\. \| \| \|\- "\-2\.85" \| \|\.\.\. \| \|\- 12100\| \| \|\.\.\. -&gt;The first lists level contains a list with different resolutions for the averaging of densities (6400, 8100, 10000,12100, 14400). -&gt;The second lists levels includes versions of the data in which the grid used to average fish densities and temperature was spatial repositioned. -&gt;The third layer contain the study species: Beaked redfish (Sebastes mentella), blue whiting (Micromesistius poutassou), capelin (Mallotus villosus), cod (Gadus morhua), haddock (Melanogrammus aeglefinus), Norwegian spring-spawning herring (Clupea harengus), long rough dab (Hippoglossoides platessoides), lumpfish (Cyclopterus lumpus), and Norway pout (Trisopterus esmarkii). +Species data level--&gt; "data.frames" with the following columns: $gridid - identifies the grid cell $Year - Year for which the the values in column "N" (fish density, see below) correspond $Depth - Depth layer for which the temperature information corresponds. Only SBT( Sea Bottom Temperature) included in the submitted data. $Delta - Difference in fish density between that row's year and the following year. i.e. difference between "N" at year\_y and site\_s and N at year\_y+1 and site\_s $N - Average density of the fish in that row's grid cell and year $K - Estimated carrying capacity for row's grid cell. Sum all N (densities) across years at the corresponding gridcell and divided by the number of years with data. $Temperature - Temperature data for the grid cell and year prior (i.e., Year-1) to the one noted for that same row. Temperature corresponds to the months of January to March $Mean\_Temperature - Temperature in that row's grid cell averaged across all years ## Resolutions\_list.rda *** R data file with lists containing spatialPolygons objects representing the hexagonal grids used to average the fish densities and temperature. \|\-Resolutions\_hexpoly \| \|\-6400 \(&lt;\-\- list with Hexagon resolutions 6400\, 8100\, 10000 and 12100 km2\) \| \| \|\- "\-0\.1" \(&lt;\-\- list with spatial repositions of hexagonal gridlevel\. Also includes "\-0\.1"\,"\-0\.35"\,"\-0\.6"\,"\-0\.85"\,"\-1\.1"\,"\-1\.35"\,"\-1\.6"\,"\-1\.85"\,"\-2\.1"\,"\-2\.35"\,"\-2\.6" and "\-2\.85"\) \| \| \|\-\.\.\. \| \| \|\- "\-2\.85" \| \|\.\.\. \| \|\-12100 \| \| \|\.\.\. ######################################################################### #SCRIPTS: *** ## Dryad\_Functions.R *** Special functions to run the other scripts: Logit InvLogit cor.overlap ExtractGridEst2 par.boot GaussSyncF boot.optim lls boot.nlminb ## Dryad\_SpatialAutocorrelation.R *** Estimates and plots spatial autocorrelation in the data ## Dryad\_SynchronyAnalyses.R *** Estimates spatial synchrony patterns in the data ### Code/Software To be used on R. Last run on R version 4.1.2 (R Core Team 2021).