Merge pull request #176 from ilonaECCC/patch-5

Update paper.Rmd

inst/joss_paper/paper.Rmd

@@ -26,26 +26,26 @@ journal: JOSS
 
 # Summary
 
-Climate change vulnerability assessments (CCVAs) are tools increasingly adopted to rank species' vulnerability to the threat of climate change [@pacifici2015]. Common CCVA approaches include trait-based, correlative models (e.g. species distribution models), mechanistic models (e.g. mechanistic niche models), or a combination of these approaches [@foden2019; @pacifici2015]. CCVAs can be used to inform extinction risk assessments by identifying the mechanisms and magnitude of impacts [@foden2019]. One popular trait-based CCVA tool is the [NatureServe Climate Change Vulnerability Index](https://www.natureserve.org/conservation-tools/climate-change-vulnerability-index) (CCVI), which is a rapid assessment tool designed to rank species' vulnerability to climate change and to highlight which factors contribute to the vulnerability of individual species or groups[@young2016; @young2015]. This information can inform conservation decisions and identify actions to increase species' resilience to climate change.
+Climate change vulnerability assessments (CCVAs) are tools increasingly adopted to rank species' vulnerability to the threat of climate change [@pacifici2015]. Common CCVA approaches include trait-based, correlative models (e.g. species distribution models), mechanistic models (e.g. mechanistic niche models), or a combination of these approaches [@foden2019; @pacifici2015]. CCVAs can be used to inform extinction risk assessments by identifying the mechanisms and magnitude of impacts [@foden2019]. One popular trait-based CCVA tool is the [NatureServe Climate Change Vulnerability Index](https://www.natureserve.org/conservation-tools/climate-change-vulnerability-index) (CCVI), which is a rapid assessment tool that ranks species' vulnerability to climate change and highlights factors contributing to increased vulnerability[@young2016; @young2015]. Outputs of the NatureServe CCVI can inform conservation decisions and identify actions to increase species' resilience to climate change.
 
-The NatureServe CCVI combines several components that contribute to a species' vulnerability to climate change, including exposure to changes in temperature and moisture (Section A), indirect exposure to other changes brought on by climate change (e.g. sea level rise; Section B), and the species' traits that either make it sensitive to climate change or allow it to adapt to climate change (Section C; \autoref{fig:NS-alg}). It also optionally incorporates the results of documented or modeled responses to climate change [Section D\; @young2012; @young2016]. The resulting CCVI can have one of five index values, 'Less Vulnerable', 'Moderately Vulnerable', 'Highly Vulnerable', 'Extremely Vulnerable' or 'Insufficient Evidence'. 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. Sections B, C and optionally D are then scored on a scale from ‘neutral’ (score: 0) to ‘greatly increases vulnerability’ (score: 3). Where applicable, the score is multiplied by an exposure multiplier and then scores are summed separately for Sections B and C and Section D. Thresholds are applied to these scores to determine an index value and then the two index values are combined into a final index value using a table that gives more weight to the sensitivity and adaptive capacity sections (\autoref{fig:NS-alg}).
+The NatureServe CCVI combines several components that contribute to a species' vulnerability to climate change, including exposure to changes in temperature and moisture (Section A), indirect exposure to other changes brought on by climate change (e.g. sea level rise; Section B), and traits related to species' sensitivity or ability to adapt to climate change (Section C; \autoref{fig:NS-alg}). It also optionally incorporates the results of documented or modeled responses to climate change [Section D\; @young2012; @young2016]. The resulting CCVI ranks include: 'Less Vulnerable', 'Moderately Vulnerable', 'Highly Vulnerable', 'Extremely Vulnerable' or 'Insufficient Evidence'. Exposure is assessed by determining the proportion of the species' range that falls into six classes of temperature and moisture change, which is used to assign an exposure multiplier. Sections B, C and optionally D are each scored on a scale from ‘neutral’ (score: 0) to ‘greatly increases vulnerability’ (score: 3). The indirect exposure, sensitivity and adaptive capacity scores are multiplied by the exposure multiplier and summed. The final CCVI rank is a result of combining all scores through a variety of operations including weighting sensitivity and adpative capacity scores. [comment: i think in the process of reducing the following text, a lot is lost that affects comprehension, Consider deleting the following sentences and replacinng with my suggestions, above ] Where applicable the score is multiplied by an exposure multiplier and then scores are summed separately for Sections B and C and Section D. Thresholds are applied to these scores to determine an index value and then the two index values are combined into a final index value (i.e., rank) using a table that gives more weight to the sensitivity and adaptive capacity sections (\autoref{fig:NS-alg}).
 
 ![Algorithm for calculating the NatureServe Climate Change Vulnerability Index.\label{fig:NS-alg}](NS_CCVI_alg_diagram.png)
 
-The NatureServe CCVI tool is an Excel workbook that users can fill in based on spatial analyses, available research or expert opinion. While this Excel tool is easy to access and straightforward to use, it requires users to possess technical Geographical Information System (GIS) skills and software, that is often proprietary, to perform the spatial components of the analysis. This challenges the implementation of reproducible analyses, which are increasingly required for credible scientific processes [@munafò2017]. In addition, the Excel-based version requires repeated assessments to assess uncertainty associated with choice of Global Climate Models or emission scenarios.
+The NatureServe CCVI tool is an Excel workbook that users complete using available scientific information, expert opinion, and spatial analyses. While the tool is straightforward to use, it requires users to possess technical Geographical Information System (GIS) skills and access to (proprietary) software to perform the spatial analyses. This challenges the implementation of reproducible analyses, which are increasingly required for credible scientific processes [@munafò2017]. In addition, the Excel-based version requires repeated assessments to assess uncertainty associated with choice of Global Climate Models or emission scenarios.
 
-To improve accessibility and reproducibility of the NatureServe CCVI we have developed `ccviR`, an R package and Shiny app that implements the original CCVI in an easy to use Graphical User Interface (GUI). `ccviR` applies the original scoring algorithm to assess vulnerability and includes the Monte Carlo uncertainty analysis as in the Excel-based tool. A major advance of `ccviR` is the functionality to perform spatial analyses internally, using R, such that GIS skills and software are not required. To perform spatial analyses users are only required to specify the locations of spatial data files on their computer, using the Shiny app or R code, and `ccviR` automates subsequent analyses. Examples of these automated spatial processes include overlays of the species' range with: exposure categories, historical climate regime, and modeled changes in the species' range. The `ccviR` Shiny app presents the results of the spatial analyses using interactive maps, that allow for user-directed validation of the spatial analyses and opportunities to modify the resulting factor scores, if desired. Another significant addition is a second Shiny app that allows users to classify new independently-supplied climate data sets into the exposure categories used by the index based on the median and half the interquartile range. This makes the methods used to determine exposure classes explicit and repeatable for different climate data sets or different regions. Users must keep in mind that, since the CCVI is a relative index, index values should only be compared across species assessed using the same climate data.
+To improve accessibility and reproducibility of the NatureServe CCVI we developed `ccviR`, an R package and Shiny app that implements the original CCVI in an easy to use Graphical User Interface (GUI). `ccviR` applies the original scoring algorithm to assess vulnerability and includes the original Monte Carlo uncertainty analysis. A major advance of `ccviR` is the functionality to perform spatial analyses internally, using R, such that GIS skills and costly software are not required. Users are only required to specify the locations of spatial data files on their computer, using the Shiny app or R code, and `ccviR` automates all spatial analyses. Examples of these automated spatial processes include overlays of the species' range with exposure categories, historical climate regime, and modeled changes in the species' range. [Comment: this sentence could be deleted as it is saying the same thing as the sentence next paragraph mentioning veriffying spatial analyses. Tho, i do like how this sentence is written: The `ccviR` Shiny app presents the results of the spatial analyses using interactive maps, that allow for user-directed validation of the spatial analyses and opportunities to modify the resulting factor scores, if desired.] Another significant addition is a second Shiny app that allows users to classify independently-supplied climate data sets into the exposure categories used by the index based on the median and half the interquartile range. This makes the methods used to determine exposure classes explicit and repeatable for different climate data sets or different regions. [Comment: i have add these 2 sentences to the previous paragraph, as they flow with the subject of the paragraph whic is introducing novel ccviR things:] Additional novel features of `ccviR` include functionality to assess the consequences of uncertainty associated with choice of emission scenarios and/or global circulation models on CCVI outcomes. Additional enhancements include multiple interactive visualizations used to 1) verify spatial analyses and 2) explain what factors are driving the vulnerability of a species (\autoref{fig:ccviR-app}). 
 
-Additional novel features of `ccviR` include functionality to assess the consequences of uncertainty associated with choice of emission scenarios and/or global circulation models on CCVI outcomes. Additional enhancements include multiple interactive visualizations used to 1) verify spatial analyses and 2) explain what factors are driving the vulnerability of a species (\autoref{fig:ccviR-app}). The `ccviR` Shiny app can be launched from an R session with one line of code and runs locally, allowing easy access to files. While using the app, assessments can be saved to a csv file at any point, then the csv file can be used to restart the app and continue the assessment. The human readable csv format ensures that comments and assessment values can be used independent of the `ccviR` package which facilitates transparent and reproducible assessments. These csv files can also be used to compile data from multiple assessments for further analysis and synthesis. `ccviR` facilitates communication of scientific outcomes by including functionality to generate a pdf report summarizing the assessment.
+The `ccviR` Shiny app can be launched from an R session with one line of code and runs locally, allowing easy access to files. While using the app, assessments can be saved to a csv file at any point, then the csv file can be used to restart the app and continue the assessment. The human readable csv format ensures that comments and assessment values can be used independent of the `ccviR` package which facilitates transparent and reproducible assessments. These csv files can also be used to compile data from multiple assessments for further analysis and synthesis. `ccviR` facilitates communication of scientific outcomes by including functionality to generate a pdf report summarizing the assessment.
 
 ![Interactive visualizations in the ccviR app allow validation and interpretation of results for a) the calculation of the temperature exposure multiplier and b) the scores for each vulnerability factor used to calculate the index.\label{fig:ccviR-app}](app_visuals.png)
 
 # Statement of need
 
-The `ccviR` R package and Shiny app facilitates a seamless, accessible, and reproducible version of the NatureServe CCVI, that ensures an enhanced and intuitive user experience. The inclusion of the spatial analysis components within the package removes the need for technical GIS-based skills to perform CCVI assessments and allows conservation practitioners to focus on their areas of expertise. Simultaneously, 'ccviR' ensures that analyses are consistent and reproducible across species. While the Shiny app increases user friendliness for a wide audience, the `ccviR` package allows more proficient R users to customize their analysis workflow and more easily assess many species or perform sensitivity analyses. The R package framework also makes the CCVI more open with code and documentation available as well as unit tests to ensure the functionality of the tool. The inclusion of a Shiny app to allow users to create their own custom climate exposure data in `ccviR` extends the potential uses of the index to a greater geographic area and allows users to follow best practices of incorporating multiple future climate scenarios in their assessments.
+The `ccviR` R package and Shiny app facilitates a seamless, accessible, and reproducible version of the NatureServe CCVI, that ensures an enhanced and intuitive user experience. The inclusion of the spatial analysis components within the package removes the need for technical GIS-based skills to perform CCVI assessments and allows conservation practitioners to focus on their areas of expertise. Simultaneously, 'ccviR' ensures that analyses are consistent and reproducible across species. While the Shiny app increases user friendliness for a wide audience, the `ccviR` package allows more proficient R users to customize their analysis workflow and more easily assess many species or perform sensitivity analyses to assess the consequences of multiple forms of uncertainty. The R package framework ensures the CCVI more open with code and documentation available as well as unit tests to ensure the functionality of the tool. The inclusion of a Shiny app to allow users to create their own custom climate exposure data in `ccviR` extends the potential uses of the index to a greater geographic area and allows users to follow best practices of incorporating multiple future climate models and emission scenarios in their assessments.
 
 # Acknowledgements
 
-The authors would like to acknowledge contributions from Adriana Caswell to the testing and development of this package and conversations with Bruce Young that helped us to understand the NatureServe CCVI algorithm.
+The authors acknowledge Adriana Caswell for assistance with development and testing of this package and Bruce Young for help with understanding the NatureServe CCVI algorithm.
 
 # References