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Figure 3 in Ancient DNA from the extinct New Zealand grayling (Prototroctes oxyrhynchus) reveals evidence for Miocene marine dispersal
Figure 3. Median-joining haplotype networks of the Retropinnidae constructed from A, cytochrome b (1363 comparable sites) and B, 16s rRNA (575 comparable sites) alignments in POPART (for GenBank Accession numbers, see Supporting Information, Table S2). Haplotypes (circles) are proportional to frequency (numbers), with number of substitutions indicated by hatches along branches. Black circles represent undetected intermediary haplotypes, with colours corresponding to species.
Figure 1 in Ancient DNA from the extinct New Zealand grayling (Prototroctes oxyrhynchus) reveals evidence for Miocene marine dispersal
Figure 1. The extinct New Zealand grayling (Prototroctes oxyrhynchus). Artwork by Frank Edward Clarke. Annotations in figure contain a nomen nudum. Museum of New Zealand Te Papa Tongarewa CC BY-NC-ND 4.0.
Dispersion in SiN microrings
<p>Modal dispersion calculated with Comsol for a silicon nitride microring. Ring dimensions: radius 50 um, height 800 nm, width 1 um. Contains the dispersion of TE and TM fundamental modes, with and without material dispersion.</p>
Dissipative Solitons and Switching Waves in Dispersion-Modulated Kerr Cavities
<p>Execution tested with Matlab 2020a or newer on Windows. Unzip folder to access files.</p> <p><br> Contact miles.anderson@epfl.ch for any serious questions on the contents.<br> All matlab code remains under copyright by the authors: Miles Anderson and Tobias J. Kippenberg, and is provided solely to be used to reproduce the figures of the aforementioned paper and example simulation results pertaining to the paper.</p> <p>Figure data and generation code is found in "Figure Data\Scripts and Data". Run matlab scripts in the given folder to generate the figures. Other relevant figures containing data is found in "\Other".</p> <p>Seven example matlab simulation scripts are found in "Simulation Example Code".</p> <ul> <li>Running 'lle_cavity_v4_CW_FI_Low2' models CW Faraday Instability appearance from Figure 3, in dimensionless units.</li> <li>Running 'lle_cavity_v4_Soliton_FI_1' models a dissipative soliton with Kelly sidebands or higher-order dispersive waves in dispersion modulated cavity, from Figure 4, in dimensionless units.</li> <li>Running 'lle_cavity_v4_SW_FI_Low2' models a switching wave with FI-motivated satellites in dispersion modulated cavity, from Figure 7, in dimensionless units.</li> <li>Running 'lle_SiNcavity_v4_SW_FaradaySatellite_F9C15R6_1_1b' (or just '1') uses experimental data to reproduce the experiment for the pulse-driven switching wave according to the LLE, the results of which are shown in Figure 7(f) of the main paper, and Figure S5 of the supplementary information.</li> <li>Running 'lle_SiNcavity_v4_SW_FaradaySatellite_F2C15R5_2_3' (and also '3_1') uses experimental data to reproduce the experiment for the pulse-driven switching wave according to the LLE, the results of which are shown in Figure 8 and 9 of the main paper, and Figure S6 of the supplementary information.</li> <li>Running 'lle_SiNcavity_v4_SolitonHDW_F1C16R6TM_5_s2' uses experimental data to reproduce the experiment as seen in Figure 6 for the pulse-driven soliton according to the LLE, results of which are shown in Figure S9 of the supplementary information.</li> </ul> <p>The script parameters may be modified to find results under different driving conditions and over different time periods and sampling rates as required.</p> <p>M. Anderson apologises in advance for the complexity, readability, and optimisation of the script.</p> <p>This work was supported by Contract No. D18AC00032 (DRINQS) from the Defense Advanced Research Projects Agency (DARPA). This material is based upon work supported by the Air Force Office of Scientific Research under Grant No. FA9550-19-1-0250. This work was further supported by the European Union’s Horizon 2020 Program for Research and Innovation under Grant No. 812818 (Marie Skłodowska-Curie ETN MICROCOMB) and by the Swiss National Science Foundation under Grant Agreement No. 192293.</p>
Fig. 3 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period
Fig. 3. Mean number of stink bugs observed in the trapping site (statistics: repeated measure ANOVA, Durbin-Conover pairwise comparison test, P ≤ 0.05)
Fig. 2 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period
Fig. 2. Arrangement of the traps. The edges of slots were marked with white lines on the photo, because of the better visualization
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
Code Appendix: Do Seed Dispersal Strategies Reflect Adaptation to Environmental Variability? Functional Ecology, 2023
<p>This code appendix contains all of the R code and data for the manuscript:<br> <br> Van Den Elzen, C. L., N. Sigman, and N. C. Emery. 2023. Do Seed Dispersal Strategies Reflect Adaptation to Environmental Variability?. Functional Ecology (Manuscript ID: FE-2022-00850).<br> <br> Abstract: </p> <p>1. Dispersal is one of the primary mechanisms by which organisms adapt to spatial and temporal variation in the environment. Theory predicts that increasing spatiotemporal variation drives selection for offspring dispersal away from their natal habitat and one another. However, due to inherent difficulties in measuring dispersal in plant systems, there are few empirical tests of the extent to which this hypothesis can explain variation in seed dispersal strategies.</p> <p>2. In this study, we characterized and compared the dispersal patterns of three closely related plant species that segregate across gradients in spatiotemporal variation in seasonal wetlands.</p> <p>3. We tracked individual seeds as they dispersed in their natural habitats to measure seed dispersal distance (the distance traveled from the maternal plant) and inter-seed spread (distances between dispersed seeds), and to identify the plant traits causing within-species variation in seed dispersal. We also evaluated the seed traits causing within-species variation in seed flight distance and terminal velocity in a wind tunnel and a drop tube, respectively.</p> <p>4. We found that average seed dispersal distance was lowest in the species that occupies the most spatiotemporally variable habitat, contradicting our predictions; however, inter-seed spread was lowest in the species from the least variable habitat, which aligned with our expectations.</p> <p>5. The maternal plant and seed traits explaining intraspecific variation in seed dispersal varied among species as well as the method used to measure dispersal potential. Two traits had non-intuitive effects on dispersal, including pappus size, which reduced seed flight distance in two of the focal taxa.</p> <p>6. Overall, our results indicate that the differences we detected in seed dispersal among three closely related plant taxa can be only partially explained by current patterns of environmental variability in their respective habitats, and that the traits driving within species variation in seed dispersal can evolve rapidly and change with the environmental context in which they are measured.</p>
To disperse or compete? Coevolution of traits leads to a limited number of reproductive strategies
<p><span><span>Reproductive strategies are defined by a combination of behavioural, morphological, and life-history traits. Reproductive investment and offspring propagule size are two key traits defining reproductive strategies. While a substantial amount of work has been devoted to understanding the independent fitness effects of each of these traits, it remains unclear how coevolution between them ultimately affects the evolution of reproductive strategies, and how this might influence the relationship between dispersal and environmental factors. In this study, we explore how the evolution of reproductive strategies defined by these two coevolving traits is influenced by resource availability and spatial structuring of the environment using a simulation model. We find three possible equilibrium strategies across all scenarios: a competitor strategy with high reproductive investment (producing large propagules which disperse short distances), and two coloniser strategies differing in reproductive investment (both producing small propagules which disperse long distances). The possible equilibrium strategies for each scenario depended on starting conditions, spatial structure and resource availability. Evolutionary transitions between these equilibrium strategies were more likely in heterogeneous than homogeneous landscapes and at higher resource levels. Transition from coloniser strategy to competitor strategy was usually a two-step process, with changes in propagule size following initial evolution in investment. This highlights how the interaction between the two trait axes affects the evolution of reproductive strategies, particularly where fitness valleys preclude the simultaneous evolution of traits. Our results highlight the need to incorporate trait coevolution into evolutionary models to help develop a more integrative understanding of the structure of natural populations and how the interaction between traits constrains or hinders evolutionary processes.</span></span></p>
Figure 5 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 5. Genetic clustering based on allele frequencies of 12 microsatellite loci genotyped for Rattus rattus Complex samples from MSEA and extralimital distribution in Indonesia (IDN). (A) PCoA, (B) STRUCTURE barplot.
Figure 4. Haplotype network for Rattus rattus Complex II in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 4. Haplotype network for Rattus rattus Complex II. The Nusa Tenggara samples are illustrated on the right of the network.
Figure 1 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 1. Cranial measurements taken for each specimen: supraoccipital height (BH), basal length (BLL), basilar length (BRL), length of the bullae (BULL), condylobasal length (CBL), condylobasilar length (CBRL), minimum corpus length (CL), length of the diastema (DA), foramen magnum height (FMH), foramen magnum width (FMW), length of the incisive foramina (FOR), length of the face (GES), length of the braincase (HKL), thickness of the incisor (ID), interorbital breadth (IOB), mandibular diastema length (LAL), thickness lower incisor (LID), mandibular alveoli length (MAL), mandibular toothrow length (crown) (MCL),mandibular depth (MD), maximum mandibular height (MDL), mandibular depth at M1 (MID), mandibular length (ML), nasal length (NAS), nasal breadth (NASB), occipital breadth (OCB), occipital length (OCN), supraoccipital width at the occipital condyles (OCW), length of the upper molar row (alveoli) (OZRA), length of the upper molar row (crown) (OZRK), palatal length (PL), palatine breadth (PRL), rostral breadth (RB), rostral height (RH), breadth of braincase (SKB), height of braincase with bullae (SKH), zygomatic plate (ZP), zygomatic breadth (ZYG).
Figure 3. Haplotype networks for Rattus exulans, R in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 3. Haplotype networks for Rattus exulans, R. argentiventer, and Rattus rattus Complex LIV. Sunda refers to the islands of Borneo, Java, and Sumatra; the Indonesian sample (brown) lacks further collection information.
Figure 6 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 6. Summary of likely movements of the three commensal rodents through the Nusa Tenggara island chain. Rat illustrations redrawn and adapted from R. Budden, location of RS3 Dong Song drums from Calo (2014).
Data from "Higher mortality is not a universal cost of dispersal: A case study in African wild dogs"
<p>Mortality is considered one of the main costs of dispersal. A reliable evaluation of mortality, however, is often hindered by a lack of information about the fate of individuals that disappear under unexplained circumstances (i.e. missing individuals). Here, we addressed this uncertainty by applying a Bayesian mortality analysis that inferred the fate of missing individuals based on information from individuals with known fate. Specifically, we tested the hypothesis that mortality during dispersal is higher than mortality among non-disperser using 32 years of mark-resighting data from a free-ranging population of the endangered African wild dog (<em>Lycaon pictus</em>) in northern Botswana. Contrary to expectations, we found that mortality during dispersal was lower than mortality among non-dispersers, indicating that higher mortality is not a universal cost of dispersal. Our findings suggest group living can incur costs for certain age classes, such as limited access to resources as group density increases, that exceed the mortality costs associated with dispersal. By challenging the accepted expectation of higher mortality during dispersal, we urge for further investigations of this key life-history trait, and propose a robust statistical approach to reduce bias in mortality estimates.</p>
Figure 3 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 3. Multivariate generalized linear mixed-effects model results showing correlations (see coloured scale for total range) of different dispersal modes among species.
Figure 2 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 2. Multivariate generalized linear mixed-effects model results showing joint effects of feeding strategies on dispersal modes in mites. Posterior means are given (dots), along with 50% (thick lines) and 95% (thin lines) credible intervals for Acariformes (orange) and Parasitiformes (violet). If the 95% credible intervals do not cover zero (intense colours), the particular dispersal mode is significantly more frequent or less frequent than expected by chance (at the α = 0.05 level).
Figure 4 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 4. Relationship between the number of studies carried out on the dispersal syndrome of a given mite species and the proportion of dispersal modes revealed. The shaded region depicts 95% confidential intervals around the fit. Points are observed data points. Note the logarithmic scale on both axes.
Figure 1 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 1. Database collection summary. PRISMA (preferred reporting items for systematic reviews and meta-analyses) diagram detailing the procedure for the identification and inclusion of relevant publications.
Code and data supplement for "Unveiling the transition from niche to dispersal assembly in ecology"
<p>This repository contains the data and code needed to reproduce the results and figures in the article “Unveiling the transition from niche to dispersal assembly in ecology” published in Nature.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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