DOI: 10.5061/dryad.3r2280gs9
title: The impact of varying spatiotemporal scales on different joint species distribution models: A case study of pelagic fish species in the northwest Pacific Ocean

description: Joint Species Distribution Models (JSDMs) have become a critical tool in
 community ecology research, with a wide scope of application that is
 continuously expanding. However, inferring interspecies relationships from
 co-occurrence data remains a challenge. This study examined the impact of
 varying spatiotemporal scales on JSDMs, with a focus on model stability
 and the evaluation of interspecies relationships. We compared the
 performance of the models across 32 different spatiotemporal scales. Our
 results indicate that the spatiotemporal scale significantly affects the
 performance of JSDMs, with notable differences among the models. As both
 temporal and spatial scales increased, model simulation and prediction
 performance improved, and stability increased. Moreover, spatial scale has
 a substantial impact on the evaluation of interspecies relationships, with
 finer spatial scales identifying weaker positive relationships and
 stronger negative relationships. Among the models, HMSC demonstrated
 better balancing performance, while the Boral model showed the least
 stability. Overall, the optimal JSDM identified was the HMSC model with an
 annual temporal and 0.25° spatial scale.
description: To comprehensively evaluate the impact of varying spatiotemporal
 scales on JSDMs, this study was designed using two temporal scales
 (monthly and annual), four spatial scales (0.25°, 0.5°, 1°, and 2°), and
 four different JSDMs (Bayescomm, HMSC, Boral, and Gjam). Using three
 economically important pelagic fish species from the northwest Pacific
 Ocean as examples—Japanese sardine (<em>Sardinops
 melanostictus</em>), chub mackerel (<em>Scomber
 japonicus</em>), and neon flying squid (<em>Ommastrephes
 bartramii</em>)—we compared the performance of the models across 32
 different spatiotemporal scales.
description: # The impact of varying spatiotemporal scales on different joint species
 distribution models: A case study of pelagic fish species in the northwest
 Pacific Ocean
 \======================================================================================== #### "model_code" Data and commented code for the reproduction of moldeling. The distribution data of the three species is not publicly available due to privacy or ethical restrictions. All environmental data was provided in the ".xlsx" files.  ### variable definitions Sea Surface Temperature (SST): This variable indicates the temperature of the ocean’s surface water. It is an essential factor for studying species distribution and marine ecosystems. Chlorophyll-a Concentration (Chl-a): This variable represents the amount of chlorophyll-a pigment in the water, which is an indicator of phytoplankton biomass. Phytoplankton are microscopic plants that form the base of the marine food web, making this measurement crucial for understanding marine productivity and ecosystem health. Sea Surface Salinity (SSS): This variable indicates the salt concentration in seawater at the surface. Salinity affects water density and circulation patterns in the ocean, influencing marine species' life and distribution. Sea Level Anomaly (SLA): This variable represents variations from the average sea level in a given area. It can result from factors like ocean currents, temperature changes, and atmospheric pressure. Eddy Kinetic Energy (EKE): This variable quantifies the energy in ocean eddies, which are swirling water masses. EKE is important for studying ocean mixing processes, nutrient distributions, and the dynamics of marine ecosystems. Mixed Layer Depth (MLD): This variable refers to the depth of the upper layer of the ocean where water is well-mixed due to wind and wave action. The MLD influences the distribution of heat, nutrients, and marine organisms. Dissolved Molecular Oxygen (O2): This variable indicates the concentration of oxygen dissolved in seawater. Oxygen is vital for the survival of marine organisms, making its measurement important for marine species distribution and ecosystem health. Dissolved Molecular Nitrate (NO3): This variable represents the concentration of nitrate ions in seawater. Nitrate is a key nutrient for phytoplankton growth, and monitoring its levels helps understand primary production and nutrient cycling in marine ecosystems. Dissolved Molecular Phosphate (PO4): This variable indicates the concentration of phosphate ions in seawater. Similar to nitrate, phosphate is an essential nutrient for marine plants, and its levels influence primary productivity. Net Primary Production (NPP): This variable signifies the amount of organic matter produced by phytoplankton through photosynthesis minus the amount consumed through respiration. NPP is a critical measure of the productivity of marine ecosystems and overall ocean health. ### Units for all variables SST: Measured in degrees Celsius—°C Chl-a: Measured in milligrams per cubic meter —mg/m^3 SSS: Measured in practical salinity units—psu SLA: Measured in meters—m EKE: Measured in square meters per second squared—m^2/s^2 MLD: Measured in meters —m O2: Measured in millimoles per cubic meter—mmol/m^3 NO3: Measured in millimoles per cubic meter—mmol/m^3 PO4: Measured in millimoles per cubic meter—mmol/m^3 NPP: Measured in milligrams per cubic meter per day—mg/m^3day ### All files details Y"2×2data.xlsx The data for the time scale is in years, and the spatial scale is 2°. Y1×1data.xlsx The data for the time scale is in years, and the spatial scale is 1°. Y0.5×0.5data.xlsx The data for the time scale is in years, and the spatial scale is 0.5°. Y0.25×0.25data.xlsx The data for the time scale is in years, and the spatial scale is 0.25°. M2×2data.xlsx The data for the time scale is in months, and the spatial scale is 2°. M1×1data.xlsx The data for the time scale is in months, and the spatial scale is 1°. M0.5×0.5data.xlsx The data for the time scale is in months, and the spatial scale is 0.5°. M0.25×0.25data.xlsx The data for the time scale is in months, and the spatial scale is 0.25°. ### Software versions for models Bayesian Community Ecology Analysis: ‘BayesComm’ version 0.1-2 Generalized Joint Attribute Modeling: ‘gjam’ version 2.6.2 Bayesian Ordination and Regression Analysis: ‘boral’ version 2.0 Hierarchical Model of Species Communities: ‘Hmsc’ version 3.0-13 ### Software version for R the script was created using R version 4.2.0.