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Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
Data from: Local environment and coral composition affect recovery and determine long-term coral responses to recurrent mass mortalities in the Lakshadweep Archipelago
<p>A quarter century after the first global coral bleaching event in 1998, reports differ on the relative importance of anthropogenic influences, local environment and bleaching recurrence in determining the resilience of coral reefs. While life history traits largely determine how corals respond to temperature anomalies, it is unclear if these traits also determine how corals fare over time. From 1998 to 2022, we tracked compositional changes in reefs across the Lakshadweep Archipelago to explore how global El Niño events, and local environment (wave climate and depth) influenced coral responses to repeated mass bleaching. From the 1998 to the 2016 bleaching event, the magnitude of coral mortality reduced overall, particularly at deeper reefs (shallow: -38% to -3%; deep: -18% to -0.45%). Post-bleaching recovery correlated positively with higher wave exposure, linked to the creation of stable structures for coral settlement and survival. Across bleaching phases, recovery was initially slow (6-7 years post-mortality), but, given time, showed a much steeper increase, led by space-occupying genera like <em>Acropora</em>. However, recurring mass bleaching maintained coral cover low (~15% across all sites). These broad trends mask dynamic compositional patterns. Genera such as <em>Porites</em>, <em>Pocillopora</em>, and <em>Favia</em> declined less through time compared to <em>Acanthastrea</em>, <em>Turbinaria</em>, <em>Psammocora</em> and <em>Plesiastrea </em>among others. We identified six community clusters that describe contrasting long-term responses to local and global factors, mediated by depth and wave exposure. Interestingly, genera with different functional traits cluster together indicating that bleaching susceptibility interacts with depth and exposure, creating a spatial mosaic of coral assemblages. These clusters serve as a predictive, site-specific framework to understand the dynamically shifting but declining assemblage of Lakshadweep reefs. While local management could help maintain this changing composition, urgent global action is needed to secure the long-term ecological integrity of tropical reefs.</p>
Data from: The biogeochemical boomerang: Site fidelity creates nutritional hotspots that may promote recurrent calving site reuse
<p>Animals interact with nutrient cycles by consuming and depositing nutrients, interactions that are studied in the separate fields of nutritional ecology and zoogeochemistry. Recent theoretical work has begun bridging these disciplines, highlighting that animal-driven nutrient recycling could be crucial in helping animals meet nutritional needs. When animals exhibit site fidelity, they consistently deposit nutrients, potentially improving vegetation quality. We investigated this potential feedback by analyzing changes in forage nitrogen stocks following simulated caribou calving. We found that forage nitrogen stocks increased after two weeks and remained elevated after one year, a change due to an increase in forage quality but not quantity. We thus highlight a positive zoogeochemical feedback whereby caribou deposit nutrients during calving that become bioavailable during lactation and provide evidence that site fidelity creates a biogeochemical boomerang in which animals deposit nutrients that can be reused at a later time.</p>
Self Consistent Recurrent Neural Network for Path Dependent Deformation
<p>Data and Machine Learning codes for the paper:</p> <ul> <li>Title<strong> : Self Consistent Recurrent Neural Network for Path Dependent Deformation</strong></li> </ul> <p><strong>Abstract</strong> : Current neural network (NN) structures can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is not known in advance which NN architectures will perform best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting material failure based on the earlier states in the form of deformation history. Following this investigation, the crucial issues arising from the mathematical computation process of the best-performing NN architectures and the physical properties of the deformation paths are examined in detail. Additionally, we propose a novel and adaptable RNN approach to address the fundamental challenges of truncation and consistency related to obtaining estimations that are compatible with the natural physical properties of deformation paths. This study will serve as a foundation for localization estimation and pave the way for future endeavors to propose further solutions to encountered challenges.</p>
Paleoseismology of the Northern Kongur Shan Extensional System, NE Pamir: Implications for Potential Irregular Earthquake Recurrence
<p>Data and code used in the manuscript submitted to JGR: Solid Earth, including:</p> <p>1. 0.1-m resolution DEM at the Alasai site</p> <p>2. Photomosaics of the lacustrine section and fault exposure</p> <p>3. Dataset on earthquake magnitude and liquefaction distance</p> <p>4. Matlab code for scarp degradation modeling</p> <p>5. Matlab code for Monte Carlo simulation of earthquake cycles</p>
Fig. 1 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 1. Map showing location od the study area (A) and the three fossiliferous localities (asterisked) of the Springhill Formation, Santa Cruz Province, Argentina (B). C. Stratigraphic section of the Springhill Formation in the Estancia El Álamo locality.
Fig. 5 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 5. Araucarian wood Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. A, B. Trunk showing diameter and incomplete length. Note branches (arrows). C. Deep intrusion of the trunk into the deposits. D. Detail of the decorticated trunk.
Fig. 9 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 9. Araucarian wood Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. Details of radial sections under SEM. A. Tracheid with slightly flattened pits (arrow). B. Tracheid showing contiguous pits with circular inner aperture. C–E. Araucarioid crossfield pits. C. General aspect. D. Contiguous alternate bordered pits placed in four vertical rows. Note circular pits in outline and circular inner aperture. E. Detail of inner apertures infilled with Si cement in elliptical form (arrow). Scale bars: A, B, E, 25 µm; C, 100 µm; D, 5 µm.
Fig. 4 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 4. XRD and SEM/EDS mineralogical and chemical analysis of Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. A. XRD pattern of the bulk sample showing quartz composition of the trunk. B. XRD pattern of the clay fraction showing no presence of clay minerals in the trunk. C, D. SEM of crossfield pits and tracheids in radial section. Note that quadrangular and rectangular areas correspond to the spot analysis shown in E–I. E–I. EDS patterns of xylem elements. E. Parenchyma ray cell wall. F. Inner aperture in crossfield pits. G, H. Tracheids cell walls. I. Tracheid pit cavity. All the EDS patterns are showing Si and O components.
Fig. 8 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 8. Araucarian wood Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. Radial sections observed under SEM. A. General aspect showing ray cells (arrow), crossfields pits (circle), axial tracheids (arrowhead). B. Detail of tracheids with uniseriate, and contiguous pits, arrow shows transition from biseriate to uniseriate pit rows. C. Detail of tracheids with biseriate, contiguous, and alternate to subopposite pits. Scale bars: A, B, 200 µm; C, 100 µm.
Fig. 3 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 3. Trunk location in the tectostratigraphic framework of the initial infilling of the AustralMagallanes Basin, from the rift stage to the beginning of the foreland stage (modified from Poiré et al. 2017). Not to scale.
Fig. 2 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 2. Outcrops of the Springhill Formation in the Estancia El Álamo locality. A. Panoramic view of the Springhill Formation outcrop overlying the El Quemado Complex. B. Polymictic conglomerate beds with a sandstone bed intercalation, Springhill Formation. C. Detail of the polymictic conglomerate with siliceous (S) and volcanic (V) clasts. D. Pyroclastic (P) and siliceous (S) clasts in the conglomerate.
Fig. 7 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 7. Araucarian wood Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. Transverse sections observed under LM. A. General aspect showing numerous, at least five frost rings (brackets). B, C. Detail of frost rings. B. Two frost rings. Note normal and rectilinear trajectory of rays (arrows) alternate with more sinuous and distended rays (arrowheads). C. Detail of frost ring cell layers, from inside to outside. Note normal tracheids that gradually grade into irregular shaped tracheids (bar) followed by a dark layer of collapsed dead cells (arrow) followed by a layer of distorted axial tracheids difficult to recognise individually. Also note dark contents in lumen cells. Scale bars: A, 3 mm; B, 1.5 mm; C, 150 µm.
Fig. 6 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 6. Araucarian wood Agathoxylon mendezii sp. nov. (MPMPB15596), Estancia El Álamo, Santa Cruz Province, Argentina, Berriasian–Valanginian. Wood sections observed under LM. A–C. Transverse view. A. Slightly marked growth ring (arrows). B. Detail of growth ring, arrow shows layers of rectangularflattened latewood tracheids. C. Detail of earlywood tracheids and rectilinear trajectory of rays (arrow). D–G. Longitudinal tangential view. D. General aspect, arrows indicate partially biseriate rays. E–G. Details of biseriate rays (arrows), arrowhead shows resin plugs. Scale bars: A, 500 µm; B, C, 150 µm; D–G, 100 µm.
Fig. 10 in Recurrent volcanic activity recorded in araucarian wood from the Lower Cretaceous Springhill Formation, Patagonia, Argentina: Palaeoenvironmental interpretations
Fig. 10. Hypothetical scenario in the Estancia El Álamo locality (Santa Cruz Province, Argentina, Berriasian–Valanginian) following volcanic disturbances. A. Preeruption stage. Seedlings, juvenile, and mature trees of Agathoxylon mendezii sp. nov. growing in a warm almost subtropical palaeoenvironment. Note volcanoes in the distance. Photo shows wood with slightly growth ring. B. Initial eruption stage. Volcanoes begin to eject silicate dust and sulfur compounds into the stratosphere. C. Climax eruption stage. Aerosol layer thickness is markedly increased producing the decrease of the surface air temperature below subzero values. Photo shows wood damaged by frost. D. Posteruption stage. Volcanoes activity begins to cease and temperature begins to rise to original values.
Figs. 1-2 in Eublemma baccatrix Hacker, 2019 (Lepidoptera: Erebidae: Boletobiinae: Eublemmini): recurrent immigrant or rapidly expanding species throughout the Iberian south area? Eublemma baccatrix Hacker, 2019 (Lepidoptera: Erebidae: Boletobiinae: Eublemmini): inmigrante recurrente o especie en rápida expansión por el sur ibérico?
Figs. 1-2.- Eublemma baccatrix Hacker, 2019. 1.- Casa Athene, Vejer de la Frontera, Cádiz, 10-11-2023, leg. & phot. Stephen Knapp, coll. José Luis Yela. 2.- Duquesa Fairways, San Luis de Sabinillas, Manilva, Málaga, phot. Richard Banham.
Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks
<p><strong>The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in <em>learning</em> features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal <em>in operando</em> spectroscopies and automated control for the manipulation of nanoscale structures in materials.</strong></p>
Fig. 8 in Recurrent constructional pattern of the crustacean burrow Sinusichnus sinuosus from the Paleogene and Neogene of Spain
Fig. 8. Trace fossils and modern burrows that render some similarities to Sinusichnus crustacean burrow networks. Circle on the right shows grapholglyptids and Cochlichnus at the same scale as the others, except for the burrows of the Chinese mitten crab, Eriocheir sinensis. Likewise, all drawings are plan views, except for Gyrolithes which is lateral view. Megagrapton irregulare after Häntzschel 1975; Stereobalanus burrow from Romero-Wetzel 1989; Ophiomorpha irregulaire after Bromley and Ekdale 1998; Eriocheir burrow from Rudnick et al. 2005; Gyrolithes marylandicus, Thalassinoides suevicus, Cosmorhaphe parva, and Protopaleodictyon helicoidea after Seilacher 2007; Cochlichnus anguineus from Gibert and Sáez 2009.
Fig. 9 in Recurrent constructional pattern of the crustacean burrow Sinusichnus sinuosus from the Paleogene and Neogene of Spain
Fig. 9. Depositional setting of the crustacean burrow Sinusichnus sinuosus Gibert, 1996 bearing outcrops in the Vilomara area, Spain. A. Stratigraphic log showing the location of Sinusichnus (indicated by stars) (vf, very fine-grained sand; vc, very coarse-grained sand). B. Correlation panel (from López-Blanco et al. 2000c) showing Sinusichnus occurrences in relation to facies belts on the Sant Llorenç del Munt fan-delta complex and within the regressive sequence set of the Vilomara Composite sequence. Location of A is indicated. C. Panoramic view of the transition from delta front (sandstone beds on the SE) to prodelta (siltstone and mudstone beds on the NW) including Sinusichnus horizons (stars). D. Detail of the tabular alternation of sandstones and siltstone beds on the transition from delta front to prodelta that bears S. sinuosus.
Fig. 7 in Recurrent constructional pattern of the crustacean burrow Sinusichnus sinuosus from the Paleogene and Neogene of Spain
Fig. 7. Graphics illustrating the relation between some measured parameters of the crustacean burrow Sinusichnus sinuosus Gibert, 1996. A. Above, correlation plot of amplitude (A) versus wavelength (λ) for all the specimens measured in the six localities. Below, same diagram for each one of the six localities individually. B. Above, correlation plot of diameter (Ø) versus A/λ ratio for all the specimens measured in the six localities. Below, same diagram for each one of the six localities individually.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.