vignettes/app_details_vignette.Rmd
app_details_vignette.RmdThe NatureServe CCVI is a trait based climate change vulnerability assessment framework. It includes three commonly used components of vulnerability: exposure, sensitivity and adaptive capacity. In addition, it optionally incorporates the results of documented or modeled responses to climate change.
Exposure is assessed by determining the proportion of the species range that falls into 6 classes of temperature and moisture change which is used to assign an exposure multiplier. Sensitivity and adaptive capacity are assessed by scoring 23 vulnerability factors, on a scale from 0 (‘neutral’) to 3 (‘greatly increases vulnerability’). These scores are then multiplied by the exposure multiplier and are summed to give a total score for this section. Factors that cannot be answered can be left blank and contribute 0 to the total score. If fewer than 13 factors are scored the index value cannot be calculated.
In addition, there are 4 vulnerability factors for documented or modeled responses to climate change that can optionally be scored on a similar scale. These scores are simply summed to get the total score for this section. An index value is then determined for the two sections (exposure x sensitivity and adaptive capacity, and documented or modeled responses) by applying a set of thresholds to the scores. The two index values are then combined using a table that gives more weight to the sensitivity and adaptive capacity section. The possible index values are Less Vulnerable, Moderately Vulnerable, Highly Vulnerable, Extremely Vulnerable or Insufficient Evidence (if not enough factors of the CCVI are scored).
The NatureServe CCVI is a rapid assessment tool designed to allow a relative grouping of unrelated taxa by vulnerability to climate change and to highlight which factors contribute to the climate change vulnerability of individual species or groups of taxa. This information can be used to inform conservation decision making and to help identify actions to increase species resilience to climate change.
For more detailed information on how the index works and how each factor is scored see the NatureServe CCVI Guidelines and the references below.
The ccviR app and package allow for the use of different climate data sets as inputs to the index but it is important to note that, since the index results in a qualitative index of the relative vulnerability to climate change, only index values created using the same climate data sets should be compared. When multiple scenarios are included in one assessment the same thresholds for exposure categories should be used across scenarios so that they can be compared. In the pre-prepared data sets linked here this is achieved by determining thresholds for amount of change in temperature or moisture based on the least extreme climate scenario and applying the same thresholds to the more extreme climate scenario(s). For example, in the demonstration used in the app vignette the amount of change in the RCP 8.5 scenario is so much higher than in the RCP 4.5 scenario that the whole area has the highest exposure class under RCP 8.5 where as the exposure classes are evenly distributed under RCP 4.5
All polygon data should be provided as a shapefile while
raster data can be in any file format accepted by the
terra::rast function but “.tif” files are recommended.
The first time you use the app you will need to acquire a climate data set that includes the:
The data is classified based on the median and interquartile range for the future change in MAT and CMD and based on thresholds defined by the NatureServe Guidelines for MAP and MWMT-MCMT.
There are several versions of the climate data available for download here. To use the data you need to download one of the files, unzip it and store it in a convenient location. If you wish to create your own climate data set for use with the index based on different climate data you can follow the instructions in the “Preparing Custom Climate Data” vignette. When using the app to assess multiple different species the same climate data set should be used for all species because the index is relative so the results cannot be compared across different species if different data sets are used.
Ideally this is the complete area occupied by the species in North America. The full range is used to determine the range of climate conditions that the species can tolerate to answer the questions about historical thermal niche (C2ai) and historical hydrological niche (Cb2i). The portion of the range that is within the assessment area (see below) is used to determine the species exposure to climate change.
The area within which the species vulnerability is being assessed. For example, a country, province, state, or protected area. This can also be a subset of the species range if you are assessing sub-populations. This polygon is intersected with the range to create the polygon used to determine the exposure to climate change. The range within the assessment area is overlain on the exposure raster and the proportion of the range covered by each class is determined and used to calculate exposure multipliers for temperature, moisture and a combined multiplier. These exposure multipliers are used to modify the scores from the sensitivity questions in order to reach the total score.
The PTN polygon should include cool or cold environments occupied by the species that may be lost or reduced in the assessment area as a result of climate change. This could include species restricted to frost pockets, north-facing slopes, shady ravines, or alpine areas if these are among the coldest areas in the assessment so the species is unlikely to be able to shift within the assessment area if these cool areas are lost. If spatial data for the PTN is not available the question can be answered manually or left as unknown. It is rare to have spatial data for the physiological thermal niche so it should be filled in manually in most cases.
If applicable, the area outside North America occupied in the non-breeding season. Leave this blank for non-migratory species. This polygon will be overlapped with the Climate Change Exposure Index (CCEI) raster to calculate the migratory exposure index. A CCEI raster can be downloaded from the NatureServe website and included in the climate data folder. If either the non-breeding range polygon or the CCEI raster are not available or the migratory species check box is not checked the migratory exposure index will not be calculated.
The modeled change in the species range caused by climate change. The values of the raster cells should indicate whether the cell was lost, maintained, gained or was never included in the range. The raster is expected to be the result of some sort of modelling. For example, if an SDM for the species under the current climate was used to predict the current range and then it was projected on to a future climate scenario the difference between these two rasters could be used to create a range change raster. If both rasters are binary predictions of the species presence then if the current was subtracted from the future -1 would represent habitat lost, 0 maintained, and 1 gained. If raster data is not available in the appropriate format then the question can be answered manually or left blank if no model is available.
There are many potential sources of range polygons for species. When considering which data to use it is important to consider how different choices might affect the index. For example, if a wide ranging species occurs on both sides of a mountain range you might find a range polygon that includes the tops of the mountains in the species range even though the species likely cannot survive in those conditions. This can bias the historical thermal and hydrological niches calculated in the index. Therefore it is important to choose the range data carefully.
Some possible sources of range polygons are:
The app is launched by calling the run_ccvi_app()
function, which will launch the app in the user’s default internet
browser. All file selection windows will start in the current working
directory. To run the app with data stored in a different folder you can
call run_ccvi_app() with the folder path as the first
argument. For example, if all of my data is stored in a subdirectory of
the working directory named “data”, I would call
run_ccvi_app("data") to avoid having to open the data
folder every time I select a file. Alternatively, the complete path to
the data folder can be supplied
e.g. “C:/Users/username/Documents/path/to/folder” (Note that paths in R
must be supplied using forward slashes).
You can use a saved partially completed assessment as a template for running multiple related assessments. For example, to assess multiple populations of a species that are in different locations and may have different stressors, fill in the app with all the information that will not change between populations and save the state of the app in a csv by clicking “Save Progress”. Then for each population launch a new assessment, click “Load from a previous assessment” and select your template csv. Then modify it for the specific population and save the completed assessment in a new csv file. You may also wish to complete multiple assessments for a species if you want to enter different answers for vulnerability questions depending on the climate change scenario used. Multiple climate scenarios can be included in a single assessment and will affect the exposure multiplier and optionally the modelled response to climate change. However, all other vulnerability questions are assumed to be constant across scenarios. For example, if sea level rise was expected to have a different impact under RCP 8.5 than 4.5 you would need to perform two separate assessments to reflect this.