Quantification of Animal Responses to Tropical Cyclones

Tropical cyclones play a fundamental role in driving the dynamics of coastal ecosystems in the regions where they regularly occur. As climate patterns shift, the frequency, characteristics, and spatial distributions of tropical cyclones is shifting, making it important to understand the mechanisms underlying ecosystem responses to storms to allow forecasting. Evaluation of patterns relating storm characteristics, local environment, and ecosystem responses provides one pathway toward developing a comprehensive understanding of processes creating observed patterns. Data aggregated in this dataset quantifies the responses of animals to tropical cyclones. The 123 named storms included in the dataset span the period from 1969 to 2020 within the Atlantic Ocean and Caribbean Sea. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and freshwater systems from 278 unique locations. These data measure a variety of different levels of organismal organization ranging from species specific abundance or biomass to community level metrics such as biodiversity. Meteorological data characterizing tropical cyclones are also presented and are derived from: IBTrACS: International Best Track Archive for Climate Stewardship Global storm track data set for all recorded low pressure systems and tropical cyclones dating back to 1842. Data includes timestamped spatial information on storm center location as well as meteorological readings of wind speed, wind direction, and barometric pressure. Most importantly for the purposes of this data set, wind speeds at various distances from storm center are provided, which allows for the development of a model of wind speed with distance for different category storms. GRIDMET: University of Idaho Gridded Surface Meteorological Dataset The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the status property. At first, assets are ingested with status=early. After several days, they are replaced by assets with status=provisional. After about 2 months, they are replaced by the final assets with status=permanent. Daymet V3: Daily Surface Weather and Climatological Summaries Daymet V3 provides gridded estimates of daily weather parameters for United States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived from selected meteorological station data and various supporting data sources. Compared to the previous version, Daymet V3 uses an entirely new suite of inputs including: • NASA SRTM DEM version 2.1. • Land/Water Mask: MODIS 250 MOD44W_v2.NASA_ORNL_ • Horizon files derived from the SRTM DEM. • Ground station weather inputs from several sources with QA/QC. PERSIANN-CDR: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2. CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data (version 2.0 final) Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.