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Spatial data for creating a thermal inertia index and incorporating it for conservation applications
<p>This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth's Future. The repository contains the following items:</p> <p>1. README file of what is in the repository including methods associated with the geodatabase</p> <p>2. File Geodatabase</p> <p><strong>1. README file</strong></p> <p>The files collected here relate to a study being submitted to the American Geophysical Union's journal, Earth's Future. The title of the paper being submitted is, "The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies."</p> <p>The study was conducted across 40,250 km<sup>2</sup> of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models.</p> <p>The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height.</p> <p class="MsoNormal">The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. </p> <p><strong>2. File Geodatabase</strong></p> <p>The geodatabase is provided as a separate file.</p> <p>Name: ThermalInertiaIndex.gdb</p> <p>Contents:</p> <ul> <li>AllHexagons <ul> <li>A feature class containing all 408,948 hexagon grids used in this study</li> <li>Fields within the feature class:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>Id</p> </td> <td> <p>A unique ID for each hexagon</p> </td> </tr> <tr> <td> <p>Watershed</p> </td> <td> <p>Watershed the hexagon falls within</p> </td> </tr> <tr> <td> <p>DomWHR</p> </td> <td> <p>Habitat type (WHR) that had the majority coverage within the hexagon</p> </td> </tr> <tr> <td> <p>WHR_Name</p> </td> <td> <p>Descriptive name of the habitat type</p> </td> </tr> <tr> <td> <p>WHR_GroupName</p> </td> <td> <p>Major vegetation type</p> </td> </tr> <tr> <td> <p>CanopyHt_Score</p> </td> <td> <p>Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)</p> </td> </tr> <tr> <td> <p>CanopyHt_m</p> </td> <td> <p>Average canopy height within the hexagon (m)</p> </td> </tr> <tr> <td> <p>Conn_Score</p> </td> <td> <p>Connectivity Score ranging from 1 (low) to 5 (high)</p> </td> </tr> <tr> <td> <p>dem10m</p> </td> <td> <p>Average elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_min</p> </td> <td> <p>Minimum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_max</p> </td> <td> <p>Maximum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>SRtemp_min</p> </td> <td> <p>The lowest Solar Radiation load within the hexagon (degree C)</p> </td> </tr> <tr> <td> <p>ElevLR_NegEff2</p> </td> <td> <p>Effect of elevation on air temperature (degree C)</p> </td> </tr> <tr> <td> <p>Thermal_Inertia</p> </td> <td> <p>Hexagon buffering capacity (degree C)</p> </td> </tr> <tr> <td> <p>tave_5180</p> </td> <td> <p>Average temperature 1951-1980</p> </td> </tr> <tr> <td> <p>tave_8110</p> </td> <td> <p>Average temperature 1981-2010</p> </td> </tr> <tr> <td> <p>tave_1039mi8</p> </td> <td> <p>Average temperature 2010-2039 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069mi8</p> </td> <td> <p>Average temperature 2040-2069 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099mi8</p> </td> <td> <p>Average temperature 2070-2099 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_1039cn8</p> </td> <td> <p>Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069cn8</p> </td> <td> <p>Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099cn8</p> </td> <td> <p>Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)</p> </td> </tr> </tbody> </table> </div> <p> </p> <ul> <li>Connectivity_Scores <ul> <li>90m raster containing all 3 connectivity scores</li> <li>Fields within the raster:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>TNC_Conn_Score</p> </td> <td> <p>Connectivity Score from reclassed TNC/Omniscape</p> </td> </tr> <tr> <td> <p>CEHC_Score</p> </td> <td> <p>Connectivity Score from reclassed California Essential Habitat Connectivity</p> </td> </tr> <tr> <td> <p>Combined_Score</p> </td> <td> <p>Overall Connectivity Score</p> </td> </tr> </tbody> </table> </div> <p> </p>
Highly conserved thermal performance strategies may limit adaptive potential in corals
<p>Increasing seawater temperatures are expected to have profound consequences for reef-building corals' physiology. Understanding how demography changes in response to chronic exposure to warming will help forecast how coral communities will respond to climate change. Here, we measure growth rates of coral fragments of four common species, while exposing them to temperatures ranging from 19 to 31˚C for one month to calibrate their thermal-performance curves (TPCs). Our results show that, while there are contrasting differences between species, the shape of the TPCs was remarkably consistent among individuals of the same species. The low variation in thermal sensitivity within species may imply a reduced capacity for rapid adaptive responses to future changes in thermal regimes. Additionally, interspecific differences in thermal responses show a negative relationship between maximum growth and thermal optima, contradicting expectations derived from the classic "warmer-is-better" hypothesis. Among species, there was a trade-off between current and future growth, whereby most species perform well under current thermal regimes but are susceptible to future increases in temperature. Increases in water temperature with climate change are likely to reduce growth rates, further hampering future coral reef recovery rates and potentially altering community composition.</p>
Kenai Mountains to Sea: Using Thermal Infrared Imagery to Implement Long-Term Salmon Conservation - thermal imagery data set
<p>Cook Inletkeeper contracted NV5 Geospatial (formerly, Quantum Spatial Incorporated) to collect thermal infrared (TIR) during the summer of 2020 along four streams on the Kenai Peninsula in southern Alaska: Beaver Creek, Crooked Creek, Funny River, and Moose River under a project name “Kenai Rivers”. All streams were contracted to be flown in the summer of 2020 and during the afternoon hours in order to maximize the thermal contrast between the river’s water and the banks. The survey extends for a total length of 59.1 km miles of the Kenai Rivers. The Data were collected to aid the Cook Inletkeeper team to identify the spatial variability in surface temperatures as well as thermal influence of point sources, tributaries, and surface springs. The data will also be used to identify high-value habitats for the salmonids population within the four streams.</p> <p>Note: These data and related items of information have not been formally disseminated by NOAA and do not represent any agency determination, view, or policy.</p> <p>Funding for this project came, in part, from the Alaska Sustainable Salmon Fund (AKSSF Project #53003).</p>
Data from: Detection of evolutionary conserved and accelerated genomic regions related to adaptation to thermal niches in Anolis lizards
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Highly conserved thermal performance strategies may limit adaptive potential in corals
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Spatial data for creating a thermal inertia index and incorporating it for conservation applications
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Data from: Big biology meets microclimatology: Defining thermal niches of ectotherms at landscape scales for conservation planning
Temperature profoundly affects ecology, a fact ever more evident as the ability to measure thermal environments increases and global changes alter these environments. The spatial structure of thermalscapes is especially relevant to the distribution and abundance of ectothermic organisms but the ability to describe biothermal relationships at extents and grains relevant to conservation planning has been limited by small or sparse datasets. Here, we combine a large occurrence database of >23,000 aquatic species surveys with stream microclimate scenarios supported by an equally large temperature database for a 149,000-km mountain stream network to describe thermal relationships for 14 fish and amphibian species. Species occurrence probabilities peaked across a wide range of temperatures (7.0–18.8 °C) but distinct warm- or cold-edge distribution boundaries were apparent for all species and represented environments where populations may be most sensitive to thermal changes. Warm-edge boundary temperatures for a native species of conservation concern were used with geospatial datasets and a habitat occupancy model to highlight subsets of the network where conservation measures could benefit local populations by maintaining cool temperatures. Linking that strategic approach to local estimates of habitat impairment remains a key challenge but is also an opportunity to build relationships and develop synergies between the research, management, and regulatory communities. As with any data mining or species distribution modeling exercise, care is required in analysis and interpretation of results, but the use of large biological datasets with accurate microclimate scenarios can provide valuable information about the thermal ecology of many ectotherms and a spatially-explicit way of guiding conservation investments.
Supplementary material 1 from: Fernandez D, Millán A, Rizzo V, Comas J, Lleopard E, Pastor J, Pallarés S, Abellán P, Spada M, Bilton DT, Ribera I (2018) The CAVEheAT project: climate change, thermal niche and conservation of subterranean biodiversity. ARPHA Conference Abstracts 1: e30105. https://doi.org/10.3897/aca.1.e30105
The CAVEheATproject: climate change, thermal niche and conservation of subterranean biodiversity
Data from: Big biology meets microclimatology: Defining thermal niches of ectotherms at landscape scales for conservation planning
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