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312 results for “ecological modelling”
Interagency Ecological Program: Water quality, fish, and zooplankton monitoring and modeling to support the 2018 Suisun Marsh Salinity Control Gates Summer Action
In summer 2018 we used a unique water control structure in the San Francisco Estuary (SFE) to direct a managed flow pulse into Suisun Marsh, one of the largest contiguous tidal marshes on the west coast of the United States. The action was designed to increase habitat suitability for the endangered Delta Smelt Hypomesus transpacificus, a small osmerid fish endemic to the upper SFE. The approach was to operate the Suisun Marsh Salinity Control Gates (SMSCG) in conjunction with increased Sacramento River tributary inflow to direct an estimated 160 x 10^6 m3 pulse of low salinity water into Suisun Marsh during August, a critical time period for juvenile Delta Smelt rearing. This dataset includes physical and biological monitoring data collected for the action. Datasets include Delta Smelt catch from the USFWS Enhanced Delta Smelt Monitoring program, zooplankton and Microcystis abundance from the Environmental Monitoring Program, historic Delta Smelt catch from the Summer Townet Survey, Delta Outflow from the Dayflow model, extent of Delta Smelt habitat from the UnTRIM Bay-Delta model, and water quality (Salinity, Temperature, Chlorophyll, and Turibidity) collected at continuous sondes at three locations. These data are associated with the manuscript "Evaluation of a large-scale flow manipulation to the upper San Francisco Estuary: Response of habitat conditions for an endangered native fish," by Dr. Ted Sommer, et al. 2020 PLOS One, in review.
MCR LTER: Coral Reef: Material legacy disturbance type model; data for Kopecky et al., 2023 Ecology
This data package contains the code necessary to create a mathematical model of coral reef recovery dynamics following different types and intensities of disturbances that either remove dead coral skeletons (e.g., tropical storms) or leave standing dead skeletons (e.g., coral bleaching) and run associated analyses. We explored the sensitivity of the model to variation in key parameters, such as the strength of herbivory, and the degree to which dead skeletons protect algae from herbivory. Further, we assessed disturbance intensities and values of these parameters that lead to shifts between coral and macroalgae-dominated reefs. This code was published in Ecology and were a part of the thesis of K. Kopecky (2023). Analyses and full methods descriptions of this model can be found in the manuscript “Material legacies can degrade resilience: Structure-retaining disturbances promote regime shifts on coral reefs” (DOI: https://doi.org/10.1002/ecy.4006). No novel data were used or generated in this study. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling
<p>This repository contains the dataset and the R script of the lake ecological model linked to the following publication:</p> <p>Article title: Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling</p> <p>Journal title: Water Research</p> <p>Article Number: 116681</p> <p>Abstract: In temperate lakes, it is generally assumed that light rather than temperature constrains phytoplankton growth in winter. Rapid winter warming and increasing observations of winter blooms warrant more investigation of these controls. We investigated the mechanisms regulating a massive winter diatom bloom in a temperate lake. High frequency data and process-based lake modeling demonstrated that phytoplankton growth in winter was dually controlled by light and temperature, rather than by light alone. Water temperature played a further indirect role in initiating the bloom through ice-thaw, which increased light exposure. The bloom was ultimately terminated by silicon limitation and sedimentation. These mechanisms differ from those typically responsible for spring diatom blooms and contributed to the high peak biomass. Our findings show that phytoplankton growth in winter is more sensitive to temperature, and consequently to climate change, than previously assumed. This has implications for nutrient cycling and seasonal succession of lake phytoplankton communities. The present study exemplifies the strength in integrating data analysis with different temporal resolutions and lake modeling. The new lake ecological model serves as an effective tool in analyzing and predicting winter phytoplankton dynamics for temperate lakes.</p>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Long time-series ecological niche modelling using archaeological settlement data.
<p><strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong></p> <p>Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD</p> <p> </p> <p><strong>CR_settlement_niche_combined.tif</strong></p> <p>All models combined by averaging.</p> <p> </p> <p><strong>CR_settlement_archeo.zip</strong></p> <p>Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:</p> <p><strong>Site_Type:</strong> Cemetery or Settlement</p> <p><strong>Archeo_Dat:</strong> Archaeological dating (culture or period)</p> <p><strong>Source:</strong> Source dataset (AMCR or LONGWOOD)</p> <p>AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.</p> <p>LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.</p> <p><strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating)</p> <p><strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration)</p> <p><strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)</p>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Design of a new model yeast consortium for ecological studies of enological fermentation
<p>Dataset describing the characterization of a new 6-species yeast consortium representative of wine yeast diversity, as well as its application as proof-of-concept to explore the diversity-functionnality relationship in microbial community.</p> <p>It includes the data, scripts, figures, tables, and additional informations pertaining to the article "Design of a new model yeast consortium for ecological studies of enological fermentation" to be submitted.</p>
Database on the Performance of Current Agro-Ecological Farming Systems (AEFS) as an Input to the Modelling in WP4
<p>This database contains farm data (e.g. yields) and results (indicators) from assessments with the three decision support tools in the UNISECO case studies: SMART (www.fibl.org/en/themes/smart-en.html), Cool Farm Tool (coolfarmtool.org) and COMPAS (www.thuenen.de). This version (2.0) was developed as a benchmark for the assessment of exemplary cases of how the core dilemmas of agro-ecological transitions may be overcome at farm level and as an input to the modelling at territorial level in WP4.</p> <p>This database was created in the course of the H2020 project UNISECO. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 773901.</p>
Ecological niche models for American black bear, Rafinesque's big-eared bat, and timber rattlesnake
<p>This data set contains rasters that are predictive environmental suitability maps for three wildlife species: the American black bear (<i>Ursus americanus</i>), Rafinesque's big-eared bat (<i>Corynorhinus rafinesquii</i>), and Timber rattlesnake (<i>Crotalus horridus</i>). Rasters for each species include: individual prediction maps for each of 5 ENMs (GBM: generalized boosting model, GLM: generalized linear model, MARS: multivariate adaptive regression spline, MX: maximum entropy, and RF: random forest), as well as the ensemble prediction map from all five ecological niche models (ENMs).</p>
Data from: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift
<p>The Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>) is native to Southeast Asia. Since its first detection in 2008 in Europe and North America, it has been a pest to the fruit production industry as it feeds and oviposits on ripening fruit. Here we aim to model the potential geographical distribution of <em>D. suzukii</em>. We performed an extensive literature review to map the current records. In total, 517 documented occurrences (96 native and 421 invasive) were identified spanning 52 countries. Next, we constructed three species distribution models (SDMs) based on occurrence records in: 1) the native range (SDMnative), 2) the invasive range in Europe (SDMEurope) and 3) a global model of all records (SDMglobal). The models aimed to investigate, whether this species will be able to occupy additional ecological niches beyond its native range and expand its current geographic distribution both globally and in Europe. The SDMs were generated using Maximum Entropy algorithms (Maxent) based on present occurrence records and bioclimatic variables (WorldClim). Predictions of habitat suitability vary greatly depending on the origins of occurrence records. According to all models, precipitation and low temperatures were key limiting factors for the distribution of <em>D. suzukii</em>, which suggests that this species requires a humid environment with mild winters in order to establish a permanent population in its invasive range. Several regions in the invasive range, not presently occupied by this species, were predicted highly suitable, especially in northern Europe, suggesting that <em>D. suzukii</em> is not occupying its full fundamental niche yet. Synthesis and applications. Based on these models of potential geographic distribution of the Spotted Wing Drosophila (<em>Drosophila</em> <em>suzukii</em>), we show a shift in the ecological niche in <em>D. suzukii</em> populations, emphasizing the importance of using presence and local environmental data. Further investigation regarding new occurrences is recommended to secure optimal pest management. Despite a continuing expansion, many countries still lack proper surveillance schemes, and we urge policymakers to initiate appropriate management programs.</p>
Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
<p><span>Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by <em>Laminaria</em> <em>hyperborea</em>). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (<em>Gadus</em> <em>morhua</em>) and kelp and European lobster (<em>Homarus</em> <em>gammarus</em>), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.</span></p>
Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies
<p>Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, behavior, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers interested in enumerating questing ticks attempt to integrate this heterogeneity by conducting replicate sampling bouts spread over the tick questing period as common field methods notoriously underestimate ticks. However, it is unclear how (or if) tick studies account for this heterogeneity in the modeling process. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1%) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.</p>
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 4. Areas (polygons) in Western Podillya (Ukraine), where there is a predicted probability for the accommodation 9, 8 or 7 reptile species (gradient from dark gray — 9 species to light — 7 species). Districts numbered as in fig. 3.
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 3. Areas (downward diagonal filled polygons) in Western Podillya (Ukraine), where the average predicted habitat suitability for reptile species exceeds 0.5 (Districts: 1 — Terebovlianskyi, 2 —Husiatynskyi, 3 — Buchatskyi, 4 — Chortkivskyi, 5 — Chemerovetskyi, 6 — Horodenkivskyi, 7 — Zalishchytskyi, 8 — Borshchivskyi, 9 — Kamianets-Podilskyi, 10 — Zastavnivskyi, 11 — Khotynskyi).
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 4. Two upper categories ("Moderate" and "High") collapsed to identify areas of predicted presence (dark gray shading) for B. variegata in the study area.
Fig. 1 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 1. Response of Bombina variegata to Bio 11: x-axis — mean temperature of coldest quarter (°C x 10); y- axis— logistic output (probability of presence).
ScienceDex guides
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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.