From a80d3519b1d997f017c887882308d6dc7277a155 Mon Sep 17 00:00:00 2001 From: Erik van Sebille Date: Mon, 22 Dec 2025 09:59:53 +0100 Subject: [PATCH] Adding matthews paper --- src/data/papers-citing-parcels.ts | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/data/papers-citing-parcels.ts b/src/data/papers-citing-parcels.ts index e713dc0..f234712 100644 --- a/src/data/papers-citing-parcels.ts +++ b/src/data/papers-citing-parcels.ts @@ -2731,4 +2731,14 @@ export const papersCitingParcels: Paper[] = [ abstract: 'This study identifies and quantifies the distinct contributions of wind and tides to the variability of Lagrangian residual transport in the Dutch Wadden Sea (DWS), a mesotidal system of interconnected tidal basins of high ecological relevance. A three-dimensional hydrodynamic model and offline particle tracking were used to simulate the transport of particle patches over individual tidal periods of the record 1980-2015 using depth-averaged currents. This transport was decomposed into the net displacement of their center of mass (advection) and the tidally averaged rate of change of dispersion from their center of mass (the dispersion coefficient). The results reveal that advection is predominantly wind-driven on the temporal scale of events. Strong winds from the North Sea aligned with the topographical orientation of the system trigger advection comparable to the width of the basins. Although the role of tides in advection is secondary, they induce residual circulation cells near the inlets, particularly evident during weak wind conditions. In contrast, dispersion is controlled by the tides and exhibits filamentous structures with large values around all the DWS inlets. The strength of these structures has a linear correlation with the tidal amplitude, which is mainly modulated by the spring-neap cycle. However, the location of these structures changes predominantly from shallow areas surrounding the channels when particles are released at high tide to within the channels when released at low tide. These findings underscore the distinct separable roles of wind and tides in Lagrangian residual transport within event-driven, multi-inlet coastal systems such as the DWS.', }, + { + title: + 'Influences of Dispersal and Environmental Selection on Zooplankton Distributions Across the Upper 1000 m of the North Pacific', + published_info: 'Journal of Biogeography, in press', + authors: + 'Matthews, SA, K Kaminsky, AE Cazares-Nuesser, JM Questel, L Blanco-Bercial, J Hirai, MD Ohman (2026)', + doi: 'https://doi.org/10.1111/jbi.70115', + abstract: + 'Aim: Test the response of mesopelagic zooplankton community composition and distributional ranges to dispersal potential and environment, in comparison with the epipelagic zooplankton community. Location: Epipelagic (0–200 m) and mesopelagic (200–1000 m) depth zones of the North Pacific Ocean. Taxon: Multicellular zooplankton. Methods:Metabarcoding of two molecular markers (18S and COI) in combination with a global ocean circulation model, analysed by General Dissimilarity Modelling. Results: We found no significant difference in beta-diversity across three depth strata (0–200, 200–500, and 500–1000 m), calculated from the nMDS dispersion of samples within each stratum. Similarity in beta-diversity within the three depth strata indicates that epipelagic and mesopelagic zooplankton communities have similar levels of spatial turnover in species composition despite differences in the magnitude of environmental gradients and dispersal potential. There were no differences in the biogeographic ranges of taxa associated with each depth zone, but we observed larger temperature, salinity, and dissolved oxygen habitat envelopes as well as narrower potential food ranges for deeper-dwelling taxa. Ocean basin-scale community dissimilarity was correlated with dispersal distance, as well as with changes in temperature, salinity, dissolved oxygen concentration, and food flux. Combined Generalised Dissimilarity Models incorporating both dispersal potential and environmental habitat variables revealed that the environmental variables temperature and food flux had the strongest predictive power to explain community dissimilarity.', + }, ]