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title: "tab8_pjTime"
output: html_document
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### **Module:** ***Project to New Time***

**BACKGROUND**  

In simple terms, applying or “projecting” a niche/distributional model to a region or time period different from the ones used to make the model involves making a prediction based on the model and the new values of the predictor variables, as long as these values are available for the original region and time period. In reality, however, researchers should be cognizant of many possible pitfalls, including non-analog conditions (e.g., requiring extrapolation in environmental space) and heterogeneity in the effects of species interactions (Fitzpatrick and Hargrove 2009; Anderson 2013).

To predict to different times, datasets describing environmental variables in these times are needed. <a href="http://www.ipcc-data.org/guidelines/pages/gcm_guide.html" target="_blank">Global circulation models</a>, (GCMs) provide estimates for climate for both the past and future. Various GCMs may have disparate estimates because they are based on different assumptions. *Wallace* currently uses future climate data for 2050 and 2070 based on the <a href="http://cmip-pcmdi.llnl.gov/cmip5/" target="_blank">IPPC5</a> climate projections based on the WorldClim database. Future versions of *Wallace* will likely include functionalities to project to times in the past (e.g. Last Glacial Maximum, Mid-Holocene, etc.) as well.

**IMPLEMENTATION** 

This module relies on functionality for model prediction grids from the R package `dismo`.

Users must first select a model. Depending on the `ENMeval` settings selected in Component **Model**, there may be multiple choices for Maxent. Users then draw a polygon on the map to delineate the area desired for the new prediction. Once the polygon has been drawn, "Select" chooses this extent for all projection operations. Users must then select a time period to project to (year 2050 or 2070), and also a GCM that estimates the future climate. "Project" calculates the modeled response for the predictor variable values for each cell of the selected extent and plots the prediction on the map. Users can download the prediction as either raster grid types for analysis (.grd and .asc), or as an image file (.png).

Users may choose a thresholding rule to convert the continuous prediction to a binary one (0s and 1s), which can be interpreted as presence/absence or suitable/unsuitable. Please see guidance text for **Component: Model** for more details on thresholding rules. After the model and threshold selections are made, the prediction can be plotted on the map. Users can download the prediction as either raster grid types for analysis (.grd and .asc), or as an image file (.png).

*NOTE*: In *Wallace*, model predictions are currently always 'clamped' for grid cells with environmental values more extreme than those of the training dataset (background sample plus occurrence records). This is because, due to a bug in the `predict()` function in `dismo`, the "clamping" option is always on for model predictions (i.e., clamping cannot be turned off using dismo, to allow an unconstrained extrapolation beyond the enironmental condtions of the training dataset). This means that for all raster predictions generated by the model (both for predictions within the training extent and for any projections made to regions and/or time periods different from the training extent), predicted suitability values for environmental conditions more extreme than the training values are set to (i.e., 'clamped' to) the suitability values associated with the minimum (at the low end) or maximum (at the high end) value of the variable in the training dataset.

**REFERENCES**

Anderson R.P. (2013). A framework for using niche models to estimate impacts of climate change on species distributions. *Annals of the New York Academy of Sciences*. 1297: 8-28.

Fitzpatrick, M. C., & Hargrove, W. W. (2009). The projection of species distribution models and the problem of non-analog climate. *Biodiversity and Conservation*. 18: 2255-2261.
