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wallaceEcoMod · Oct 9, 2018 · 46815a8 · 46815a8 · gepinillab · Oct 10, 2018
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diff --git a/.gitignore b/.gitignore
@@ -26,3 +26,4 @@ temp*
 KeepNA
 keepInteger
 inst/doc
+*Thumbs.db
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,23 +1,25 @@
 Package: wallace
-Version: 1.0.5
-Date: 2018-05-24
+Version: 1.0.6
+Date: 2018-10-05
 Title: A Modular Platform for Reproducible Modeling of Species Niches and Distributions
 Authors@R: c(person("Jamie M.", "Kass", email = "[email protected]", role = c("aut", "cre")),
+  person("Gonzalo E.", "Pinilla-Buitrago", email = "[email protected]", role = "aut"),
 	person("Bruno", "Vilela", email = "[email protected]", role = "aut"),
 	person("Matthew E.", "Aiello-Lammens", email = "[email protected]", role = "aut"),
 	person("Robert", "Muscarella", email = "[email protected]", role = "aut"),
 	person("Cory", "Merow", email = "[email protected]", role = "aut"),
 	person("Robert P.", "Anderson", email = "[email protected]", role = "aut"))
 Author: Jamie M. Kass [aut, cre],
+    Gonzalo E. Pinilla-Buitrago [aut],
     Bruno Vilela [aut],
     Matthew E. Aiello-Lammens [aut],
     Robert Muscarella [aut],
     Cory Merow [aut],
     Robert P. Anderson [aut]
 Maintainer: Jamie M. Kass <[email protected]>
-Depends: R (>= 3.2.1), shiny (>= 0.14.2), leaflet (>= 1.0.1)
-Imports: DT (>= 0.2), shinyjs, spocc (>= 0.5.4), RColorBrewer, dplyr,
-        spThin, ENMeval, rgeos, maptools, dismo, raster, shinythemes,
+Depends: R (>= 3.5.0), shiny (>= 1.1.0), leaflet (>= 2.0.2)
+Imports: DT (>= 0.4), shinyjs, spocc (>= 0.8.0), RColorBrewer, dplyr,
+        spThin, ENMeval (>= 0.3.0), rgeos, maptools, dismo, raster, shinythemes,
         magrittr, rgdal, leaflet.extras, XML, rmarkdown, testthat, zip
 Description: The 'shiny' application 'wallace' is a modular platform for reproducible modeling of species niches and distributions. 'wallace' guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface.
 License: GPL-3

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,13 @@
+# wallace 1.0.6
+- Wallace no longer needs rJava to run! Oh happy days! Wallace is now compatible with ENMeval 0.3.0, which now has no rJava dependency and runs Maxent using maxnet by default (CRAN package maxnet; https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.03049). This means Wallace no longer loads rJava automatically when using the ENMeval partition functions or running Maxent. You can still select the Java implementation of Maxent by choosing "maxent.jar" in the Maxent module, whereupon rJava will load. 
+- Wallace now works on computers that error when some non-ASCII characters are used. This problem was discovered during a workshop in Vietnam on some Chinese computers.
+- Users can now select bioclimatic variables when using 30 arc second data.
+- Added more instructions on how to troubleshoot installing rJava.
+- Occurrence points with NA environmental values now disappear from the map.
+- We also fixed some other small bugs dealing with the shiny code and Markdown file.
+- MESS color gradient
+- Small changes in text guidance
+
 # wallace 1.0.5
 - A brand new vignette was finally added to our website. Please find it here: https://wallaceecomod.github.io/vignettes/wallace_vignette.html
 - *Methods in Ecology and Evolution* paper published in April 2018 -- DOI remains the same.

diff --git a/R/wallace-package.R b/R/wallace-package.R
@@ -10,6 +10,8 @@
 #' @author \strong{Jamie M. Kass}\cr
 #' (email: \email{jkass@@gradcenter.cuny.edu};
 #' Website: \url{https://ndimhypervol.github.io/})
+#' @author \strong{Gonzalo E. Pinilla-Buitrago}\cr
+#' (email: \email{gpinillabuitrago@@gradcenter.cuny.edu})
 #' @author \strong{Bruno Vilela}\cr
 #' (email: \email{bvilela@@wustl.edu};
 #' Website: \url{https://bvilela.weebly.com/})
@@ -31,12 +33,12 @@
 #' @details \tabular{ll}{
 #' Package: \tab wallace\cr
 #' Type: \tab Package\cr
-#' Version: \tab 1.0.0\cr
-#' Date: \tab 2017-11-23\cr
+#' Version: \tab 1.0.6\cr
+#' Date: \tab 2018-10-05\cr
 #' License: \tab GNU 3.0\cr
 #' }
 #'
-#' @references Kass J.M., Vilela B., Aeillo-Lammens M.E., Muscarella R., Merow C., and Anderson R.P. (2017) \emph{Wallace}: A modular platform for reproducible ecological modeling. Version 1.0.0.
+#' @references Kass J.M., Pinilla-Buitrago G.E., Vilela B., Aeillo-Lammens M.E., Muscarella R., Merow C., and Anderson R.P. (2018) \emph{Wallace}: A modular platform for reproducible ecological modeling. Version 1.0.6
 #' @import shiny leaflet
 #' @importFrom magrittr "%>%"
 NULL
diff --git a/README.md b/README.md
@@ -1,23 +1,9 @@
 [![Build Status](https://travis-ci.org/wallaceEcoMod/wallace.svg?branch=master)](https://travis-ci.org/wallaceEcoMod/wallace) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![CRAN version](http://www.r-pkg.org/badges/version/wallace)](https://CRAN.R-project.org/package=wallace) [![downloads](http://cranlogs.r-pkg.org/badges/grand-total/wallace?color=orange)](http://cranlogs.r-pkg.org/badges/grand-total/wallace?color=orange)
 
-# Wallace (v1.0.5)
+# Wallace (v1.0.6)
 
 *Wallace* is a modular platform for reproducible modeling of species niches and distributions, written in R. The application guides users through a complete analysis, from the acquisition of data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface.
 
-# ATTENTION
-
-Due to the newest release of ENMeval v0.3.0, Wallace is having problems running the Maxent module. As a temporary solution, please install the older version of ENMeval v0.2.2.
-
-```R
-devtools::install_github("bobmuscarella/[email protected]")
-```
-
-If you want to test a development version of Wallace that uses ENMeval v0.3.0 and allows users the option of using the maxnet package to run Maxent, please install the *alpha* version of Wallace v1.0.5.9000. Please be aware that this is an untested version.
-
-```R
-devtools::install_github("wallaceEcoMod/wallace@maxnet", dependencies = TRUE)
-```
-#
 Install *Wallace* via CRAN and run the application with the following R code.
 
 ```R

diff --git a/inst/shiny/Rmd/gtext_comp1.Rmd b/inst/shiny/Rmd/gtext_comp1.Rmd
@@ -11,7 +11,7 @@ Niche/distributional modeling analyses require georeferenced occurrence records
 
 **REFERENCES**
 
-Anderson, R. P. (2012), Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions. *Annals of the New York Academy of Sciences*. 1260: 66-80.
+Anderson, R. P. (2012). Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions. *Annals of the New York Academy of Sciences*. 1260: 66-80.
 
 Franklin J. (2010). Mapping Species Distributions: Spatial Inference and Prediction. Data for species distribution models: the biological data. In: Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press.
 

diff --git a/inst/shiny/Rmd/gtext_comp1_dbOccs.Rmd b/inst/shiny/Rmd/gtext_comp1_dbOccs.Rmd
@@ -13,14 +13,14 @@ Over the past two decades, the worldwide biodiversity informatics community has
 
 This module relies on the R package `spocc`, which provides streamlined access to many species occurrence databases, some of which aggregate data from myriad providers. Users can choose between three of the largest databases: <a href="http://www.gbif.org" target="_blank">GBIF</a>, <a href="http://www.vertnet.org" target="_blank">VertNet</a>, and <a href="https://bison.usgs.gov" target="_blank">BISON</a>. Note that currently users must choose only one of these databases, and any later download overwrites previous ones. 
 
-Records used in downstream analyses in *Wallace* are filtered to remove those without georeferences (latitude/longitude coordinates) and those that have exact duplicate coordinates of other records. The "Occs Tbl" tab displays all the filtered records with several key fields: name, longitude, latitude, year, institutionCode, country, stateProvince, locality, elevation, and basisOfRecord (standard field names from GBIF). The records available for download as a .csv file have all original fields and include records without georeferences.
+Records used in downstream analyses in *Wallace* are filtered to remove those without georeferences (latitude/longitude coordinates) and those that have exact duplicate coordinates of other records (including number of decimal places). The "Occs Tbl" tab displays all the filtered records with several key fields: name, longitude, latitude, year, institutionCode, country, stateProvince, locality, elevation, and basisOfRecord (standard field names from GBIF). The records available for download as a .csv file have all original fields and include records without georeferences.
 
 **REFERENCES**
 
-Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., & Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. Biodiversity Informatics, 8: 94-172.
+Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., & Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. *Biodiversity Informatics*. 8: 94-172.
 
-Peterson, A. T., Soberón, J., & Krishtalka, L. (2015). A global perspective on decadal challenges and priorities in biodiversity informatics. *BMC Ecology*, 15: 15.
+Peterson, A. T., Soberón, J., & Krishtalka, L. (2015). A global perspective on decadal challenges and priorities in biodiversity informatics. *BMC Ecology*. 15: 15.
 
-Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009). eBird: A citizen-based bird observation network in the biological sciences. *Biological Conservation*, 142: 2282-2292.
+Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009). eBird: A citizen-based bird observation network in the biological sciences. *Biological Conservation*. 142: 2282-2292.
 
 Walters, M., and Scholes, R. J., (Eds.). (2017). The GEO Handbook on Biodiversity Observation Networks. Springer International Publishing. Link: http://link.springer.com/book/10.1007/978-3-319-27288-7
diff --git a/inst/shiny/Rmd/gtext_comp2.Rmd b/inst/shiny/Rmd/gtext_comp2.Rmd
@@ -11,4 +11,4 @@ Biodiversity data (especially from aggregated online databases) typically suffer
 
 **REFERENCES**
 
-Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z. Q., & Bourne, P. E. (2013). Biodiversity data should be published, cited, and peer reviewed. *Trends in Ecology & Evolution*, 28: 454-461.
+Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z. Q., & Bourne, P. E. (2013). Biodiversity data should be published, cited, and peer reviewed. *Trends in Ecology & Evolution*. 28: 454-461.
diff --git a/inst/shiny/Rmd/gtext_comp2_removeByID.Rmd b/inst/shiny/Rmd/gtext_comp2_removeByID.Rmd
@@ -15,5 +15,4 @@ Users can select individual localities for removal from the analysis by specifyi
 
 **REFERENCES**
 
-Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., & Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. Biodiversity Informatics, 8: 94-172.
-
+Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. *Biodiversity Informatics*. 8: 94-172.
diff --git a/inst/shiny/Rmd/gtext_comp2_selectOccsOnMap.Rmd b/inst/shiny/Rmd/gtext_comp2_selectOccsOnMap.Rmd
@@ -17,5 +17,4 @@ Occurrences that fall within (i.e. overlap with) the polygon are retained, and a
 
 **REFERENCES**
 
-Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., & Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. Biodiversity Informatics, 8: 94-172.
-
+Gaiji, S., Chavan, V., Ariño, A. H., Otegui, J., Hobern, D., Sood, R., Robles, E. (2013). Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials. *Biodiversity Informatics*. 8: 94-172.
diff --git a/inst/shiny/Rmd/gtext_comp2_spatialThin.Rmd b/inst/shiny/Rmd/gtext_comp2_spatialThin.Rmd
@@ -7,7 +7,7 @@ output: html_document
 
 **BACKGROUND**
 
-In addition to the possibility of errors regarding identification and georeferencing, datasets of occurrence records typically suffer from the effects of uneven (i.e., biased) sampling across geographic space. An example would be higher sampling effort near roads and established research centers. Geographically biased sampling also often results in biases in environmental space, which distorts estimates of the species' niche (Kadmon et al. 2004). Furthermore, this can falsely inflate estimates of model performance (Veloz 2009). Some ways of dealing with this problem include: 1) quantifying heterogeneity in sampling effort across geography by, for example, sampling for a "target group" of species detected with the same techniques (Anderson 2003) and correcting for such spatial sampling patterns during model building (Phillips et al. 2009); or 2) reducing the effects of biased sampling by thinning records based on geographic or environmental distances (Varela et al. 2014).  
+In addition to the possibility of errors regarding identification and georeferencing, datasets of occurrence records typically suffer from the effects of uneven (i.e., biased) sampling across geographic space. An example would be higher sampling effort near roads and established research centers. Geographically biased sampling also often results in biases in environmental space, which distorts estimates of the species' niche (Kadmon et al. 2004). Furthermore, this can falsely inflate estimates of model performance (Veloz 2009). Although these problems are well recognized, the field has not yet reached consensus regarding best practices (either conceptually or operationally). Nevertheless, some ways of dealing with this problem include: 1) quantifying heterogeneity in sampling effort across geography by, for example, sampling for a "target group" of species detected with the same techniques (Anderson 2003) and correcting for such spatial sampling patterns during model building (Phillips et al. 2009) not currently implemented in *Wallace*; or 2) reducing the effects of biased sampling by thinning records based on geographic distance (implemented here) or environmental distances (Varela et al. 2014).  
 
 **IMPLEMENTATION**
 
@@ -27,6 +27,6 @@ Kadmon R., Farber O., Danin A. (2004). Effect of roadside bias on the accuracy o
 
 Phillips S.J., Dudík M., Elith J., Graham C.H., Lehmann A., Leathwick J., Ferrier S. (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. *Ecological Applications*. 19:181-197.
 
-Varela S., Anderson R.P., García-Valdés R., Fernández-González F. (2014). Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. *Ecography*. 37:1084-1091.
+Varela S., Anderson R.P., García-Valdés R., Fernández-González F. (2014). Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. *Ecography*. 37: 1084-1091.
 
-Veloz S.D. (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. *Journal of Biogeography*. 36:2290-2299.
+Veloz S.D. (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. *Journal of Biogeography*. 36: 2290-2299.
diff --git a/inst/shiny/Rmd/gtext_comp3.Rmd b/inst/shiny/Rmd/gtext_comp3.Rmd
@@ -24,4 +24,3 @@ Kriticos, D. J., Webber, B. L., Leriche, A., Ota, N., Macadam, I., Bathols, J.,
 Peterson A. T., Soberón J., Pearson R. G., Anderson R. P., Martinez-Meyer E., Nakamura M., Araújo M. B. (2011). Environmental Data. In: *Ecological Niches and Geographic Distributions*. Princeton, New Jersey: Monographs in Population Biology, 49. Princeton University Press.
 
 Sbrocco, E. J., & Barber, P. H. (2013). MARSPEC: ocean climate layers for marine spatial ecology. *Ecology*. 94: 979-979.
-
diff --git a/inst/shiny/Rmd/gtext_comp3_userEnvs.Rmd b/inst/shiny/Rmd/gtext_comp3_userEnvs.Rmd
@@ -29,4 +29,4 @@ writeRaster(r, path_to_new_projected_raster_file)
 
 Users will need to know what the original CRS of their rasters is, then look up (i.e., Google search) its proj4 format. After saving, upload the new projected rasters and continue with your analysis. 
 
-**NOTE**: A reminder that some file types like .asc cannot embed CRS information in the file, so please avoid these types -- instead use types such as .tif that retain the CRS. 
+**NOTE**: A reminder that some file types like .asc cannot embed CRS information in the file, so please avoid these types -- instead use types such as .tif that retain the CRS.
diff --git a/inst/shiny/Rmd/gtext_comp3_worldclim.Rmd b/inst/shiny/Rmd/gtext_comp3_worldclim.Rmd
@@ -37,4 +37,4 @@ This module relies on the R package `raster` to download bioclimatic variables f
 
 **REFERENCES**
 
-Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. *International Journal of Climatology*. 25: 1965-1978.
+Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. *International Journal of Climatology*. 25: 1965-1978.
diff --git a/inst/shiny/Rmd/gtext_comp4.Rmd b/inst/shiny/Rmd/gtext_comp4.Rmd
@@ -11,8 +11,12 @@ Environmental data may be processed in many ways for niche/distributional modeli
 
 *Wallace* provides four alternative ways for delimiting a study region. The first three are  options under Module ***Select Study Region***: 1) a rectangular "bounding box" around occurrence localities, 2) a convex shape drawn around occurrence localities with minimized area (minimum convex polygon), or 3) buffers around occurrence localities. Alternatively, users can upload a polygon (Module ***User-specified Background Extent***). All study region polygons can then be buffered by a user-defined distance in degrees. 
 
+After choosing a way to delimit the study region (***Step 1***), *Wallace* samples background points (= pixels; ***Step 2***) from the environmental data according the the number provided by the user. If that number is smaller than the total in the environmental data (within the chosen background extent), some environmental conditions may be missed in the sample. Depending upon the variables used in the final model, such a situation may lead to the need for environmental extrapolation in order to make a prediction to the full background extent (see Components **Model**, **Visualize**, and **Project**; Guevara et al. 2018).
+
 **REFERENCES**
 
+Guevara, L., Gerstner, B. E., Kass, J. M., Anderson, R. P. (2018). Toward ecologically realistic predictions of species distributions: A cross-time example from tropical montane cloud forests. *Global Change Biology*. 24: 1511-1522.
+
 Peterson A. T., Soberón J., Pearson R. G., Anderson R. P., Martinez-Meyer E., Nakamura M., Araújo M. B. (2011). Modeling Ecological Niches. In: *Ecological Niches and Geographic Distributions*. Princeton, New Jersey: Monographs in Population Biology, 49. Princeton University Press.
 
 Williams, J. W., & Jackson, S. T. (2007). Novel climates, no-analog communities, and ecological surprises. *Frontiers in Ecology and the Environment*. 5: 475-482.