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

Marquez, Jonatan Fredricson, Norwegian Institute of Marine Research, https://orcid.org/0000-0003-2034-6634

Aanes, Sondre, Norwegian Computing Center

jonatan.fredricson@gmail.com

Published Sep 14, 2023 on Dryad. https://doi.org/10.5061/dryad.g79cnp5w2

Cite this dataset

Marquez, Jonatan Fredricson; Aanes, Sondre (2023). Effects of local density dependence and temperature on the spatial synchrony of marine fish populations [Dataset]. Dryad. https://doi.org/10.5061/dryad.g79cnp5w2

Abstract

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.

README

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#DATA:

GENERAL INFORMATION

Title of Dataset: Effects of local density dependence and temperature on the spatial synchrony of marine fish populations
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
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).
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:

->The first lists level contains a list with different resolutions for the averaging of densities (6400, 8100, 10000,12100, 14400).

->The second lists levels includes versions of the data in which the grid used to average fish densities and temperature was spatial repositioned.

->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--> "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 (<-- list with Hexagon resolutions 6400\, 8100\, 10000 and 12100 km2)
| | |- "-0.1" (<-- 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
| | |...

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#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).

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).

Funding

The Research Council of Norway, Award: 223257

The Research Council of Norway, Award: 244647