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Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway

<h3>Code</h3> MATLABWhaleDetectionCodeNWA.zip The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway (Goblot et al., in review) to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales).  <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip). Table S2 WhaleDetectionsFeb2020Jan2022LSZ.mat <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> Table S3 a) MonthlyBlueWhaleDetectionsOct2015Jan2022.csv and b) MonthlyFinWhaleDetectionsOct2015Jan2022.csv <ul> <li> <div> <div> <div> Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included. Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1. </div> </div> </div> </li> </ul> <h3>Additional</h3> Movie S1 LSLSWhales.mp4 <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>

Land management controls on soil carbon fluxes in Asia's largest tropical grassland

Data files for 'Land management controls on soil carbon fluxes in Asia’s largest tropical grassland', submitted to Ecological Indicators on 15 May 2024.

Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)

<h1>Overview</h1> The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023.  <h1>Maps Accuracy</h1> The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1. Table 1:  Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class. <table> <tbody> <tr> <td>   </td> <td> 2019 </td> <td>   </td> <td> 2020 </td> <td>   </td> <td> 2021 </td> <td>   </td> <td> 2022 </td> <td>   </td> <td> 2023 </td> <td>   </td> </tr> <tr> <td>   </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> </tr> <tr> <td> Forest </td> <td> 0.87 </td> <td> 0.99 </td> <td> 0.83 </td> <td> 0.99 </td> <td> 0.88 </td> <td> 0.99 </td> <td> 0.91 </td> <td> 0.99 </td> <td> 0.88 </td> <td> 1 </td> </tr> <tr> <td> Non Forest </td> <td> 0.99 </td> <td> 0.86 </td> <td> 0.99 </td> <td> 0.82 </td> <td> 0.99 </td> <td> 0.99 </td> <td> 0.99 </td> <td> 0.89 </td> <td> 1 </td> <td> 0.87 </td> </tr> <tr> <td> Overall Accuracy </td> <td> 0.92 </td> <td>   </td> <td> 0.90 </td> <td>   </td> <td> 0.93 </td> <td>   </td> <td> 0.94 </td> <td>   </td> <td> 0.93 </td> <td>   </td> </tr> </tbody> </table> <h1>Files descripion</h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul>

opencc-zeroMay 2024View details →

dryad40/100

Differential impacts of land use change on multiple components of common Milkweed (Asclepias syriaca) pollination success

Land-use change is one the greatest threats to biodiversity and is projected to increase in magnitude in the coming years, stressing the importance of better understanding how land-use change may affect vital ecosystem services, such as pollination. Past studies on the impact of land-use change have largely focused on only one aspect of the pollination process (e.g. pollinator composition, pollinator visitation, pollen transfer), potentially misrepresenting the full complexity of land-use effects on pollination services. Evaluating the impacts across multiple components of the pollination process can also help pinpoint the underlying mechanisms driving land-use change effects. This study evaluates how land-use change affects multiple aspects of the pollination process in common milkweed populations, including pollinator community composition, pollinator visitation rate, pollen removal, and pollen deposition. Overall, land-use change altered floral visitor composition, with small bees having a larger presence in developed areas. Insect visitation rate and pollen removal were also higher in more developed areas, perhaps suggesting a positive impact of land-use change. However, pollen deposition did not differ between developed and undeveloped sites. Our findings highlight the complexity evaluating land-use change effects on pollination, as these likely depend on the specific aspect of pollination evaluated and on the of the intensity of disturbance. Our study stresses the importance of evaluating multiple components of the pollination process in order to fully understand overall effects and mechanisms underlying land-use change effects on this vital ecosystem service.

А – типовые местонахоЖдениЯ: Зал. ЛаврентиЯ (красный маркер), б. ПровидениЯ (Зеленый маркер); B, B' – иЗобраЖениЯ раковины (B) и Зуба радулы (B') Bela violacea var. nodulosa. Вр=14.5 мм, ДЗ=0.25 мм, иЗ: Krause [1885, pl. 18, figs. 4, 12]; C, C' – синтип Bela violacea var. nodulosa (C) и увеличенный участок предпоследнего оборота (C'), ZMB 37860, Вр=12 мм (фотографиЯ – с раЗрешениЯ ZMB); D–I – иЗменчивость Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN беЗ номера, ЗФИ, о-в Аполлонова, Американский Залив, 3–4 м. Вр=8.2 мм; E – Defrancia becki. ZIN беЗ номера, ЗФИ, о-в Кука, 3–4 м. Вр=9.1 мм; F – Bela violacea var. laevior. Вр=12 мм, иЗ: Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Баренцево море, Югорский Шар, 13 м. Вр=8.7 мм; H – Pleurotoma bicarinata. ZIN 41203/156, ЗФИ, о-в ГрЭм-БЭм, 12–15 м. Вр=8.4 мм; I, I' – Зубы радулы типичной (I) и беЗкилевой (I') форм. ДЗ=0.12 мм и 0.21 мм, соответственно, иЗ: [Sars, 1878, pl. 9, figs. 7, 8]; J – иЗобраЖение раковины Lora inequita. Вр=11 мм, иЗ: Dall [1919, pl. 16, fig. 9]; K – голотип Lora inequita, USNM 222238. Вр=11 мм (фотографиЯ – с раЗрешениЯ USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: раковины (L) и Зуб радулы (L'). Вр=12 мм и 11.6 мм, соответственно, ДЗ=0.15 мм, иЗ: Богданов [1990, рис. 175, 176, 422 (7)]. A – type localities: Lawrence Bay (red circle), Providence Bay (green circle); B, B' – images of the shell (B) and tooth of the radula (B') of Bela violacea var. nodulosa. H=14.5 mm, L=0.25 mm, after Krause [1885, pl. 18, figs. 4, 12]; C, C' – a syntype of Bela violacea var. nodulosa (C) and the enlarged section of the penultimate whorl (C'), ZMB 37860, H=12 mm (photo – courtesy of ZMB); D–I – variability of Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN uncatalogued, Franz Josef Land, Apollonova Isl., American Gulf, 3–4 m. H=8.2 mm; E – Defrancia becki. ZIN uncatalogued, Franz Josef Land, Cook Isl., 3–4 m. H=9.1 mm; F – Bela violacea var. laevior. H=12 mm, after Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Barents Sea, Ugra Shar, 13 m. H=8.7 mm; H – Pleurotoma bicarinata. ZIN 41203/156, Franz Josef Land, Graham-Bam Isl., 12–15 m. H= 8.4 mm; I, I' – teeth of typical (I) and keelless (I') forms. L=0.12 mm and 0.21 mm, respectively; after Sars [1878, pl. 9, figs. 7,8]; J – image of Lora inequita. H=11 mm, after Dall [1919, pl.16, fig. 9]; K – the holotype of Lora inequita, USNM 222238. H=11 mm (photo – courtesy of USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: shells (L) and tooth (L'). H=12 mm and 11.6 mm, respectively, L=0.15 mm, after Bogdanov [1990, figs. 175, 176, 422 (7)]. in Curtitoma nodulosa (Krause, 1885) comb. nov. (Gastropoda: Mangeliidae), a rare species twice described from the northern part of Bering Sea

А – типовые местонахоЖдениЯ: Зал. ЛаврентиЯ (красный маркер), б. ПровидениЯ (Зеленый маркер); B, B' – иЗобраЖениЯ раковины (B) и Зуба радулы (B') Bela violacea var. nodulosa. Вр=14.5 мм, ДЗ=0.25 мм, иЗ: Krause [1885, pl. 18, figs. 4, 12]; C, C' – синтип Bela violacea var. nodulosa (C) и увеличенный участок предпоследнего оборота (C'), ZMB 37860, Вр=12 мм (фотографиЯ – с раЗрешениЯ ZMB); D–I – иЗменчивость Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN беЗ номера, ЗФИ, о-в Аполлонова, Американский Залив, 3–4 м. Вр=8.2 мм; E – Defrancia becki. ZIN беЗ номера, ЗФИ, о-в Кука, 3–4 м. Вр=9.1 мм; F – Bela violacea var. laevior. Вр=12 мм, иЗ: Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Баренцево море, Югорский Шар, 13 м. Вр=8.7 мм; H – Pleurotoma bicarinata. ZIN 41203/156, ЗФИ, о-в ГрЭм-БЭм, 12–15 м. Вр=8.4 мм; I, I' – Зубы радулы типичной (I) и беЗкилевой (I') форм. ДЗ=0.12 мм и 0.21 мм, соответственно, иЗ: [Sars, 1878, pl. 9, figs. 7, 8]; J – иЗобраЖение раковины Lora inequita. Вр=11 мм, иЗ: Dall [1919, pl. 16, fig. 9]; K – голотип Lora inequita, USNM 222238. Вр=11 мм (фотографиЯ – с раЗрешениЯ USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: раковины (L) и Зуб радулы (L'). Вр=12 мм и 11.6 мм, соответственно, ДЗ=0.15 мм, иЗ: Богданов [1990, рис. 175, 176, 422 (7)]. A – type localities: Lawrence Bay (red circle), Providence Bay (green circle); B, B' – images of the shell (B) and tooth of the radula (B') of Bela violacea var. nodulosa. H=14.5 mm, L=0.25 mm, after Krause [1885, pl. 18, figs. 4, 12]; C, C' – a syntype of Bela violacea var. nodulosa (C) and the enlarged section of the penultimate whorl (C'), ZMB 37860, H=12 mm (photo – courtesy of ZMB); D–I – variability of Curtitoma violacea: D – Pleurotoma violacea var. brevis. ZIN uncatalogued, Franz Josef Land, Apollonova Isl., American Gulf, 3–4 m. H=8.2 mm; E – Defrancia becki. ZIN uncatalogued, Franz Josef Land, Cook Isl., 3–4 m. H=9.1 mm; F – Bela violacea var. laevior. H=12 mm, after Sars [1878, pl. 17, fig. 3]; G – Bela bicarinata var. geminolineata. ZIN 21324/28, Barents Sea, Ugra Shar, 13 m. H=8.7 mm; H – Pleurotoma bicarinata. ZIN 41203/156, Franz Josef Land, Graham-Bam Isl., 12–15 m. H= 8.4 mm; I, I' – teeth of typical (I) and keelless (I') forms. L=0.12 mm and 0.21 mm, respectively; after Sars [1878, pl. 9, figs. 7,8]; J – image of Lora inequita. H=11 mm, after Dall [1919, pl.16, fig. 9]; K – the holotype of Lora inequita, USNM 222238. H=11 mm (photo – courtesy of USNM); L, L' – Oenopota inequita sensu Bogdanov non Dall: shells (L) and tooth (L'). H=12 mm and 11.6 mm, respectively, L=0.15 mm, after Bogdanov [1990, figs. 175, 176, 422 (7)].

Рис. 4. Смешанный лес в месте обитаниЯ Eostrobilops coreana в северной части п-ова Песчаный. Фото Л.А. ПроЗоровой, 1.11.2020 г. in On the distribution and ecology of a rare land snail, Eostrobilops coreana (Pilsbry, 1927) (Gastropoda: Pulmonata: Strobilopsidae)

Рис. 4. Смешанный лес в месте обитаниЯ Eostrobilops coreana в северной части п-ова Песчаный. Фото Л.А. ПроЗоровой, 1.11.2020 г.

Рис. 2. ИЗвестные местообитаниЯ улитки Eostrobilops coreana: 1 – СевернаЯ КореЯ, окрестности ПхеньЯна; 2 – СевернаЯ КореЯ, Канвондо, хребет Кымгансан; 3 – ЮЖнаЯ КореЯ, Чхунчхон-Пукто, окрестности г. ТанЯн; 4 – ЮЖнаЯ КореЯ, Канвондо, окрестности д. Синчервон; 5 – Приморский край, Заповедник КедроваЯ падь; 6 – Приморский край, хребет ЛоЗовый, пеЩера МедвеЖий Клык; 7 – Приморский край, п-ов Песчаный. Условные обоЗначениЯ: Зеленый цвет – леса раЗличной плотности, красный – урбаниЗированные территории, Желтый – сельскохоЗЯйственные Земли (по: Jakub [2018]). in On the distribution and ecology of a rare land snail, Eostrobilops coreana (Pilsbry, 1927) (Gastropoda: Pulmonata: Strobilopsidae)

Рис. 2. ИЗвестные местообитаниЯ улитки Eostrobilops coreana: 1 – СевернаЯ КореЯ, окрестности ПхеньЯна; 2 – СевернаЯ КореЯ, Канвондо, хребет Кымгансан; 3 – ЮЖнаЯ КореЯ, Чхунчхон-Пукто, окрестности г. ТанЯн; 4 – ЮЖнаЯ КореЯ, Канвондо, окрестности д. Синчервон; 5 – Приморский край, Заповедник КедроваЯ падь; 6 – Приморский край, хребет ЛоЗовый, пеЩера МедвеЖий Клык; 7 – Приморский край, п-ов Песчаный. Условные обоЗначениЯ: Зеленый цвет – леса раЗличной плотности, красный – урбаниЗированные территории, Желтый – сельскохоЗЯйственные Земли (по: Jakub [2018]).

ERA5-Land selected indicators daily aggregates for Africa, 1991

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1991. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

opencc-by-4.0Mar 2024View details →

Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"

<h1>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</h1> This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size.  <h2>How to use this repository to reproduce results </h2> This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis. What you will need:   - Installed R and RStudio for purely spatial cluster and global model analyses  - Basic understanding of how to open R and run code   You do NOT need:  - To download or install R packages on your own; that is taken care of within this environment  - To write any code   - To set up any working directories in R  <h3>Important: Using `renv`</h3> Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages.  The R package `renv` was used to create a project library, which contains all R packages that are used by the project. The packages in the project library are the versions used during the original analysis. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment.  <h2>## Basic step-by-step guide:</h2> - 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo - 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted) - 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file  - 4. Open any of the R scripts in the `analysis/` folder - 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages    - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line <h3># `analysis/` Folder</h3> The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there.  ## Files within the `analysis/` folder The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run.   - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values  - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript  - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis  - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species  <h3># `data/` Folder</h3> This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file.  ## Files within the `data/` folder   - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns.   - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'  - `sites22.csv` contains the names, site codes, and coordinates of the study sites  - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information   - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA  - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022  - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022  - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites   - `data dictionary.txt` file that defines column names and other data attributes for every dataset  <h3># `renv/` Folder</h3> The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder.    <h2>References for source data </h2>  - Some of the data in this repository were originally obtained from open access sources.   - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus  - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate  - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data  - All mosquito collection data was collected directly by the manuscript authors

Fig. 1 a in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany

Fig. 1 a Study sites in the south of North Rhine-Westphalia, Germany in 2018. Forest types (different shades of green) follow Authorised Topographic-Cartographic Information System data [39]. b Details of study site Bonn SÜd, with three transects and their respective trap locations (different colours represent different land use types). See Additional file 2: dataset S1 for coordinates of trap locations. Background map from http:// www.openstreetmap.org (OpenStreetMap contributors). The map was produced with QGIS version 3.2

Fig. 2 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany

Fig. 2 Setup of the transects. Trap locations range from oviposition habitat 1 (land use types—arable land, forest or settlement) through the transition zone into oviposition habitat 2 (land use types—forest, settlement or arable land). F100 Forest, 100 m from the transition zone; F10 forest, 10 m from the transition zone; F/S transition zone; S10 settlement, 10 m from the transition zone; S100 settlement, 100 m from the transition zone

opencc-by-4.0Dec 2014View details →

Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach

Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков).

Chang'e 5 Landing Camera Crater Detection Dataset

132 hand-labelled images from the Chang'e 5 Landing Camera. Visible impact craters in each image have their crater rim inscribed by a bounding ellipse. On average, there are approximately 50 labelled craters per image. The first 100 images of the landers descent were labelled - this is the intended training set. Every 10 images of the remaining 313 were then labelled - this is the intended testing set.   File Descriptions: CE5-ellipse-labels: joblib dump of ellipse parameters per image. change5-*.json: Raw labels as produced by the labelling software of choice, Label Studio.   Images: The images used in this work were produced and processed by the Ground Research and Application System (GRAS) of China's Lunar and Planetary Exploration Program (https://moon.bao.ac.cn). Specifically, the first 413 images from the Chang'e 5 landing camera level 2A were used. The images can be downloaded from here: <a href="https://moon.bao.ac.cn/ce5web/searchOrder_hyperSearchData.search?pid=CE5/LCAM/level/2A" target="_blank" rel="noopener">https://dx.doi.org/10.12350/CLPDS.GRAS.CE5.LCAM-2A.vA</a>.   Reference and Acknowledgement: Users of these annotations and associated data are requested to cite both the original dataset source (https://moon.bao.ac.cn) and the following paper: Matthew Rodda, Sofia McLeod, Ky Cuong Pham, and Tat-Jun Chin. (2024). Camera-Pose Robust Crater Detection from Chang'e 5. doi: https://doi.org/10.48550/arXiv.2406.04569   BibTeX: <pre><code>@misc{rodda2024camerapose, title={Camera-Pose Robust Crater Detection from Chang'e 5}, author={Matthew Rodda and Sofia McLeod and Ky Cuong Pham and Tat-Jun Chin}, year={2024}, eprint={2406.04569}, archivePrefix={arXiv}, primaryClass={cs.CV} }</code></pre>

Fig. 5 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 5: Common trends (uniteless) and factor loadings of anchovy and sardine landings and recruits at age 0. Trends were calculated with DFA using three common trends and two explanatory variables (T100, S100). Factor loadings are plotted for: 1 - sardine recruits age 0, 2 - sardine landings, 3 - anchovy recruits, 4 - anchovy landings.

Fig. 4 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 4: Annual time series of temperature, salinity, apparent oxygen utilisation (AOU), total inorganic nitrogen (TIN) and orthophosphates (HPO 2-), all collected at station S between 1975 and 2010.

Fig. 3 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 3: From top to bottom: annual time series of total (sardine+anchovy) recruits and landings; sardine recruits and landings; and anchovy recruits and landings, all between 1975 and 2010.

Fig. 2 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 2: Schematic diagram of the fisheries and environmental data, methods and models used in analyses.

Fig. 6 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 6: Observed values (diamonds) and fitted values (black lines) of: sardine recruits age 0, sardine landings, anchovy recruits age 0, anchovy landings. Fitted values were calculated with DFA using three common trends and two explanatory variables (T100,S100).

Fig. 1 in Hydrographic conditions driving sardine and anchovy populations in a land-locked sea Abstract

Fig. 1: The geography of the Adriatic-Ionian basin, with major dynamical features and oceanographic processes illustrated. WAC stands for the surface Western Adriatic Current driven by freshwater inputs, ADW denotes the bottom density current which originates from wintertime dense water generation at the shallow northern (D1) and deep southern (D2) Adriatic regions, while EAC stands for the Eastern Adriatic Current flowing at surface and intermediate layers. Ionian Sea circulation includes cyclonic (C) or anticyclonic (A) circular current regime in its northern parts, resulting in warm and saline Levantine Intermediate Water (LIW) and colder and less saline upper-layer Western Mediterranean Waters (WMW) flowing at different pathways depending on the BIOS (Bimodal Adriatic-Ionian Oscillation) phase (full line denotes pathways during cyclonic BIOS phase and dashed line during anticyclonic BIOS phase). Finally, EMT denotes the deep massive outflow of dense waters from the Aegean Sea to the Eastern Mediterranean during the Eastern Mediterranean Transient in early 1990s. Sardine and anchovy data were collected northwest from the GSA 17 line (pink, dashed). Station at which environmental data were collected is marked with S.